SLE_try0622.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SLE try0622",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "#最後改良>>>1.是否將初始if判斷式改為while loop?? 2.填答時如何顯示各domain(v)/如果填指示外的字元如何處理(v) 3.結果陽性時給予主要原因(占分最高)之domain(V)\n",
        "# entry criterion & ADDITVE CRITERION & 答案與分數轉換 & 分數結果判斷 基本完成\n",
        "# SLE final diagnosis\n",
        "def SLE(total_score):\n",
        "  if total_score >= 10:\n",
        "    return f'''Yes. This patient is classified as SLE.\n",
        "    Main causes: (1)clinical: {main_clinical_cause} ； (2)immunology: {main_immunological_cause}\n",
        "    Each gets {highest} pts of total {total_score} pts and accounts for {highest/total_score * 100:.2f} % of total weights.'''\n",
        "  else:\n",
        "    return \"No. This patient is not classified as SLE.\"\n",
        "#clinical domains & criteria\n",
        "Constitutional = {'Fever':2}\n",
        "Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "Neuropsychiatric = {'Delirium':2, 'Psychosis':3, 'Siezure':5}\n",
        "Mucocutaneous = {'Non-scarring alopecia':2, 'Oral ulcers':2, 'Subacute cutaneous OR discoid lupus':4, 'Acute cutaneous lupus':6}\n",
        "Serosal = {'Pleural or pericardial effusion':5, 'Acute pericarditis':6}\n",
        "Musculoskeletal = {'Joint involvment':6}\n",
        "Renal = {'Proteinuria > 0.5g/24h':4, 'Renal biopsy Class II or V lupus nephritis':8, 'Renal biopsy Class III or IV lupus nephritis':10}\n",
        "#Immunology domains & criteria\n",
        "Antiphospholipid_antibodies = {'Anti-cardiolipin antibodies OR Anti-β2GP1 antibodes OR Lupus anticoagulant':2}\n",
        "Complement_proteins = {'Low C3 OR low C4':3, 'Low C3 AND low C4':4}\n",
        "SLE_specific_antibodies = {'Anti-dsDNA antibody OR Anti-Smith antibody':6}\n",
        "#clinical & immunological\n",
        "clinical = ['Constitutional', 'Hematologic', 'Neuropsychiatric', 'Mucocutaneous', 'Serosal', 'Musculoskeletal', 'Renal']\n",
        "immunological = ['Antiphospholipid_antibodies', 'Complement_proteins', 'SLE_specific_antibodies']\n",
        "#clinical_Q\n",
        "clinical_Q = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_Q)):\n",
        "  clinical_Q[i] = clinical_Q[i].keys()\n",
        "#Immunological_Q\n",
        "immunological_Q = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(0, len(immunological_Q)):\n",
        "  immunological_Q[i] = immunological_Q[i].keys()\n",
        "#clinical_weight\n",
        "clinical_trueweight = []\n",
        "clinical_weight = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(len(clinical_weight)):\n",
        "  clinical_weight[i] = list(clinical_weight[i].values())\n",
        "  clinical_trueweight.append(clinical_weight[i].copy()) \n",
        "  clinical_trueweight[i][0:len(clinical_trueweight[i])] = [None] * len(clinical_trueweight[i])\n",
        "#immunological_weight\n",
        "immunological_trueweight = [] \n",
        "immunological_weight = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(len(immunological_weight)):\n",
        "  immunological_weight[i] = list(immunological_weight[i].values())\n",
        "  immunological_trueweight.append(immunological_weight[i].copy())\n",
        "  immunological_trueweight[i][0:len(immunological_trueweight[i])] = [None] * len(immunological_trueweight[i])\n",
        "#Ans\n",
        "Ans_clinical = [[], [], [], [], [], [], []]  #想讓答案初始值設為None(上面已完成)\n",
        "Ans_immunological = [[], [], []]\n",
        "\n",
        "#entry criterion\n",
        "print('Welcome to SLE diagnosis assistant!')\n",
        "print('Here comes entry criteron.')\n",
        "titer = float(input('ANA at a titer of 1:\"?\" on HEp-2 cells.'))\n",
        "if titer <= 80:   #記得後面要縮排>>考慮改良為while loop\n",
        "  print('Additive criteria are needed before final diagnosis.')\n",
        "  print('''The following are some important conditions in clinical or immunological domains.\n",
        "   Please input \"Y\" if a patient meets the condition while input \"N\" if one doesn't.''')\n",
        "  #嘗試利用for loop處理與醫師問答和答案紀錄\n",
        "  print(\"First, we need to know the patient's clinical conditions through the following questionaire.\")\n",
        "  for i in range(0, len(clinical)):\n",
        "    print('{0}. Questions about {1} domain:'.format(i+1, clinical[i])) #字串格式化\n",
        "    for condition in clinical_Q[i]:\n",
        "      print(condition, '?')\n",
        "      Ans_clinical[i] += input( 'Please input \"Y\" or \"N\".' )\n",
        "  print(\"Ans_clinical: \", Ans_clinical)\n",
        "\n",
        "  print(\"Then, we would like to further understand the patient's immunological conditions through the following questionaire.\")\n",
        "  for i in range(0, len(immunological_Q)):\n",
        "    print(f'{i+1}. Questions about {immunological[i]} domain:')\n",
        "    for condition in immunological_Q[i]:\n",
        "      print(condition, '?')\n",
        "      Ans_immunological[i] += input( 'Please input \"Y\" or \"N\".' )\n",
        "  print(\"Ans_immunological: \", Ans_immunological)\n",
        "\n",
        "  #處理clinical domains的weights\n",
        "  for i in range(len(Ans_clinical)):\n",
        "    for j in range(len(Ans_clinical[i])):\n",
        "      if Ans_clinical[i][j] == \"Y\":\n",
        "        clinical_trueweight[i][j] = clinical_weight[i][j]\n",
        "      elif Ans_clinical[i][j] == \"N\":\n",
        "        clinical_trueweight[i][j] = 0          #要是非N、Y怎麼辦，如何讓他重填\n",
        "      else:\n",
        "        print('''You filled in wrong key words(not \"Y\" or \"N\") in the questionaire, so the result may be wrong.\n",
        "        We suggest that you restart the system again and be more careful!''')\n",
        "  print(\"clinical_trueweight: \", clinical_trueweight)   \n",
        "  #處理immunological domains的weights\n",
        "  for i in range(len(Ans_immunological)):\n",
        "    for j in range(len(Ans_immunological[i])):\n",
        "      if Ans_immunological[i][j] == \"Y\":\n",
        "        immunological_trueweight[i][j] = immunological_weight[i][j]\n",
        "      elif Ans_immunological[i][j] == \"N\":\n",
        "        immunological_trueweight[i][j] = 0\n",
        "      else:\n",
        "        print('''You filled in wrong key words(not \"Y\" or \"N\") in the questionaire, so the result may be wrong.\n",
        "        We suggest that you restart the system again and be more careful!''')\n",
        "  print(\"immunological_trueweight: \", immunological_trueweight) \n",
        "  clinical_trueweight2 = [] \n",
        "  immunological_trueweight2 = []\n",
        "  for weight_list in clinical_trueweight:\n",
        "    clinical_trueweight2.append(max(weight_list)) \n",
        "  for weight_list in immunological_trueweight:\n",
        "    immunological_trueweight2.append(max(weight_list))\n",
        "  print(\"clinical_trueweight2: \", clinical_trueweight2)  # >>>自己檢查用\n",
        "  print(\"immunological_trueweight2: \", immunological_trueweight2)\n",
        "  clinical_total_score = sum(clinical_trueweight2)\n",
        "  immunological_total_score = sum(immunological_trueweight2)\n",
        "  total_score = clinical_total_score + immunological_total_score\n",
        "  # 找到weght最高的domain(想想如何呈現前三高)\n",
        "  highest_clinical = max(clinical_trueweight2)\n",
        "  highest_immunological = max(immunological_trueweight2)\n",
        "  highest = highest_clinical if highest_clinical > highest_immunological else highest_immunological\n",
        "  main_clinical_cause = []\n",
        "  main_immunological_cause = []\n",
        "  for i in range(len(clinical_trueweight2)):\n",
        "    if clinical_trueweight2[i] == highest:\n",
        "      main_clinical_cause.append(f'{clinical[i]} domain')\n",
        "  for j in range(len(immunological_trueweight2)):\n",
        "    if immunological_trueweight2[j] == highest:\n",
        "      main_immunological_cause.append(f'{immunological[j]} domain') \n",
        "  main_clinical_cause = tuple(main_clinical_cause)     \n",
        "  main_immunological_cause = tuple(main_immunological_cause)\n",
        "  print('main_clinical_cause:', main_clinical_cause) ##N\n",
        "  print('main_immunological_cause:', main_immunological_cause)\n",
        "  print(\"clinical_total_score:\", clinical_total_score)\n",
        "  print(\"immunological_total_score: \", immunological_total_score)\n",
        "  print(\"total_score: \", total_score)\n",
        "  print(SLE(total_score))\n",
        "else:\n",
        "  print('This patient is not classified as SLE!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwY-Mf4Xhlei",
        "outputId": "1c1bf267-faa1-46e8-b39b-e55135468dd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Welcome to SLE diagnosis assistant!\n",
            "Here comes entry criteron.\n",
            "ANA at a titer of 1:\"?\" on HEp-2 cells.70\n",
            "Additive criteria are needed before final diagnosis.\n",
            "The following are some important conditions in clinical or immunological domains.\n",
            "   Please input \"Y\" if a patient meets the condition while input \"N\" if one doesn't.\n",
            "First, we need to know the patient's clinical conditions through the following questionaire.\n",
            "1. Questions about Constitutional domain:\n",
            "Fever ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "2. Questions about Hematologic domain:\n",
            "Leukopenia ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Thrombocytopenia ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Autoimmune hemolysis ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "3. Questions about Neuropsychiatric domain:\n",
            "Delirium ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Psychosis ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Siezure ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "4. Questions about Mucocutaneous domain:\n",
            "Non-scarring alopecia ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Oral ulcers ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Subacute cutaneous OR discoid lupus ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Acute cutaneous lupus ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "5. Questions about Serosal domain:\n",
            "Pleural or pericardial effusion ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Acute pericarditis ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "6. Questions about Musculoskeletal domain:\n",
            "Joint involvment ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "7. Questions about Renal domain:\n",
            "Proteinuria > 0.5g/24h ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Renal biopsy Class II or V lupus nephritis ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Renal biopsy Class III or IV lupus nephritis ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Ans_clinical:  [['Y'], ['N', 'N', 'N'], ['N', 'Y', 'Y'], ['N', 'Y', 'Y', 'Y'], ['N', 'Y'], ['Y'], ['N', 'N', 'N']]\n",
            "Then, we would like to further understand the patient's immunological conditions through the following questionaire.\n",
            "1. Questions about Antiphospholipid_antibodies domain:\n",
            "Anti-cardiolipin antibodies OR Anti-β2GP1 antibodes OR Lupus anticoagulant ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "2. Questions about Complement_proteins domain:\n",
            "Low C3 OR low C4 ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Low C3 AND low C4 ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "3. Questions about SLE_specific_antibodies domain:\n",
            "Anti-dsDNA antibody OR Anti-Smith antibody ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Ans_immunological:  [['N'], ['Y', 'N'], ['Y']]\n",
            "clinical_trueweight:  [[2], [0, 0, 0], [0, 3, 5], [0, 2, 4, 6], [0, 6], [6], [0, 0, 0]]\n",
            "immunological_trueweight:  [[0], [3, 0], [6]]\n",
            "clinical_trueweight2:  [2, 0, 5, 6, 6, 6, 0]\n",
            "immunological_trueweight2:  [0, 3, 6]\n",
            "main_clinical_cause: ('Mucocutaneous domain', 'Serosal domain', 'Musculoskeletal domain')\n",
            "main_immunological_cause: ('SLE_specific_antibodies domain',)\n",
            "clinical_total_score: 25\n",
            "immunological_total_score:  9\n",
            "total_score:  34\n",
            "Yes. This patient is classified as SLE.\n",
            "    Main causes: (1)clinical: ('Mucocutaneous domain', 'Serosal domain', 'Musculoskeletal domain') ； (2)immunology: ('SLE_specific_antibodies domain',)\n",
            "    Each gets 6 pts of total 34 pts, and accounts for 17.65 % of total weights.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#字串格式化\n",
        "#clinical domains & criteria\n",
        "Constitutional = {'Fever':2}\n",
        "Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "Neuropsychiatric = {'Delirium':2, 'Psychosis':3, 'Siezure':5}\n",
        "Mucocutaneous = {'Non-scarring alopecia':2, 'Oral ulcers':2, 'Subacute cutaneous OR discoid lupus':4, 'Acute cutaneous lupus':6}\n",
        "Serosal = {'Pleural or pericardial effusion':5, 'Acute pericarditis':6}\n",
        "Musculoskeletal = {'Joint involvment':6}\n",
        "Renal = {'Proteinuria > 0.5g/24h':4, 'Renal biopsy Class II or V lupus nephritis':8, 'Renal biopsy Class III or IV lupus nephritis':10}\n",
        "#Immunology domains & criteria\n",
        "Antiphospholipid_antibodies = {'Anti-cardiolipin antibodies OR Anti-β2GP1 antibodes OR Lupus anticoagulant':2}\n",
        "Complement_proteins = {'Low C3 OR low C4':3, 'Low C3 AND low C4':4}\n",
        "SLE_specific_antibodies = {'Anti-dsDNA antibody OR Anti-Smith antibody':6}\n",
        "#clinical & immunological\n",
        "clinical = ['Constitutional', 'Hematologic', 'Neuropsychiatric', 'Mucocutaneous', 'Serosal', 'Musculoskeletal', 'Renal']\n",
        "immunological = ['Antiphospholipid_antibodies', 'Complement_proteins', 'SLE_specific_antibodies']\n",
        "#clinical_Q\n",
        "clinical_Q = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_Q)):\n",
        "  clinical_Q[i] = clinical_Q[i].keys()\n",
        "#Immunological_Q\n",
        "immunological_Q = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(0, len(immunological_Q)):\n",
        "  immunological_Q[i] = immunological_Q[i].keys()\n",
        "#clinical_weight\n",
        "clinical_trueweight = []\n",
        "clinical_weight = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(len(clinical_weight)):\n",
        "  clinical_weight[i] = list(clinical_weight[i].values())\n",
        "  clinical_trueweight.append(clinical_weight[i].copy()) \n",
        "  clinical_trueweight[i][0:len(clinical_trueweight[i])] = [None] * len(clinical_trueweight[i])\n",
        "#immunological_weight\n",
        "immunological_trueweight = [] \n",
        "immunological_weight = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(len(immunological_weight)):\n",
        "  immunological_weight[i] = list(immunological_weight[i].values())\n",
        "  immunological_trueweight.append(immunological_weight[i].copy())\n",
        "  immunological_trueweight[i][0:len(immunological_trueweight[i])] = [None] * len(immunological_trueweight[i])\n",
        "#Ans\n",
        "Ans_clinical = [[], [], [], [], [], [], []]  #想讓答案初始值設為None(上面已完成)\n",
        "Ans_immunological = [[], [], []]\n",
        "\n",
        "#entry criterion\n",
        "print('Welcome to SLE diagnosis assistant!')\n",
        "print('Here comes entry criteron.')\n",
        "titer = float(input('ANA at a titer of 1:\"?\" on HEp-2 cells.'))\n",
        "if titer <= 80:   #記得後面要縮排>>考慮改良為while loop\n",
        "  print('Additive criteria are needed before final diagnosis.')\n",
        "  print('''The following are some important conditions in clinical or immunological domains.\n",
        "   Please input \"Y\" if a patient meets the condition while input \"N\" if one doesn't.''')\n",
        "  #嘗試利用for loop處理與醫師問答和答案紀錄\n",
        "  print(\"First, we need to know the patient's clinical conditions through the following questionaire.\")\n",
        "  for i in range(0, len(clinical)):\n",
        "    print('{0}. Questions about {1} domain:'.format(i+1, clinical[i]))\n",
        "    for condition in clinical_Q[i]:\n",
        "      print(condition, '?')\n",
        "      Ans_clinical[i] += input( 'Please input \"Y\" or \"N\".' )\n",
        "  print(\"Ans_clinical: \", Ans_clinical)\n",
        "\n",
        "  print(\"Then, we would like to further understand the patient's immunological conditions through the following questionaire.\")\n",
        "  for i in range(0, len(immunological_Q)):\n",
        "    print(f'{i+1}. Questions about {immunological[i]} domain:')\n",
        "    for condition in immunological_Q[i]:\n",
        "      print(condition, '?')\n",
        "      Ans_immunological[i] += input( 'Please input \"Y\" or \"N\".' )\n",
        "  print(\"Ans_immunological: \", Ans_immunological)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7XA_gXnR3Lto",
        "outputId": "832b5e67-04cb-48e3-a239-a322d937793d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Welcome to SLE diagnosis assistant!\n",
            "Here comes entry criteron.\n",
            "ANA at a titer of 1:\"?\" on HEp-2 cells.70\n",
            "Additive criteria are needed before final diagnosis.\n",
            "The following are some important conditions in clinical or immunological domains.\n",
            "   Please input \"Y\" if a patient meets the condition while input \"N\" if one doesn't.\n",
            "First, we need to know the patient's clinical conditions through the following questionaire.\n",
            "1. Questions about Constitutional domain:\n",
            "Fever ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "2. Questions about Hematologic domain:\n",
            "Leukopenia ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Thrombocytopenia ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Autoimmune hemolysis ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "3. Questions about Neuropsychiatric domain:\n",
            "Delirium ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Psychosis ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Siezure ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "4. Questions about Mucocutaneous domain:\n",
            "Non-scarring alopecia ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Oral ulcers ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Subacute cutaneous OR discoid lupus ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Acute cutaneous lupus ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "5. Questions about Serosal domain:\n",
            "Pleural or pericardial effusion ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Acute pericarditis ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "6. Questions about Musculoskeletal domain:\n",
            "Joint involvment ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "7. Questions about Renal domain:\n",
            "Proteinuria > 0.5g/24h ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Renal biopsy Class II or V lupus nephritis ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Renal biopsy Class III or IV lupus nephritis ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Ans_clinical:  [['Y'], ['Y', 'N', 'N'], ['Y', 'Y', 'Y'], ['N', 'N', 'Y', 'Y'], ['N', 'N'], ['Y'], ['Y', 'N', 'Y']]\n",
            "Then, we would like to further understand the patient's immunological conditions through the following questionaire.\n",
            "1. Questions about Antiphospholipid_antibodies domain:\n",
            "Anti-cardiolipin antibodies OR Anti-β2GP1 antibodes OR Lupus anticoagulant ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "2. Questions about Complement_proteins domain:\n",
            "Low C3 OR low C4 ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Low C3 AND low C4 ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "3. Questions about SLE_specific_antibodies domain:\n",
            "Anti-dsDNA antibody OR Anti-Smith antibody ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Ans_immunological:  [['N'], ['Y', 'N'], ['N']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#clinical domains & criteria >>改良2-1: 將問題domain顯示出來\n",
        "Constitutional = {'Fever':2}\n",
        "Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "Neuropsychiatric = {'Delirium':2, 'Psychosis':3, 'Siezure':5}\n",
        "Mucocutaneous = {'Non-scarring alopecia':2, 'Oral ulcers':2, 'Subacute cutaneous OR discoid lupus':4, 'Acute cutaneous lupus':6}\n",
        "Serosal = {'Pleural or pericardial effusion':5, 'Acute pericarditis':6}\n",
        "Musculoskeletal = {'Joint involvment':6}\n",
        "Renal = {'Proteinuria > 0.5g/24h':4, 'Renal biopsy Class II or V lupus nephritis':8, 'Renal biopsy Class III or IV lupus nephritis':10}\n",
        "#Immunology domains & criteria\n",
        "Antiphospholipid_antibodies = {'Anti-cardiolipin antibodies OR Anti-β2GP1 antibodes OR Lupus anticoagulant':2}\n",
        "Complement_proteins = {'Low C3 OR low C4':3, 'Low C3 AND low C4':4}\n",
        "SLE_specific_antibodies = {'Anti-dsDNA antibody OR Anti-Smith antibody':6}\n",
        "#clinical & immunological\n",
        "clinical = ['Constitutional', 'Hematologic', 'Neuropsychiatric', 'Mucocutaneous', 'Serosal', 'Musculoskeletal', 'Renal']\n",
        "immunological = ['Antiphospholipid_antibodies', 'Complement_proteins', 'SLE_specific_antibodies']\n",
        "#clinical_Q\n",
        "clinical_Q = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_Q)):\n",
        "  clinical_Q[i] = clinical_Q[i].keys()\n",
        "#Immunological_Q\n",
        "immunological_Q = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(0, len(immunological_Q)):\n",
        "  immunological_Q[i] = immunological_Q[i].keys()\n",
        "#clinical_weight\n",
        "clinical_trueweight = []\n",
        "clinical_weight = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(len(clinical_weight)):\n",
        "  clinical_weight[i] = list(clinical_weight[i].values())\n",
        "  clinical_trueweight.append(clinical_weight[i].copy()) \n",
        "  clinical_trueweight[i][0:len(clinical_trueweight[i])] = [None] * len(clinical_trueweight[i])\n",
        "#immunological_weight\n",
        "immunological_trueweight = [] \n",
        "immunological_weight = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(len(immunological_weight)):\n",
        "  immunological_weight[i] = list(immunological_weight[i].values())\n",
        "  immunological_trueweight.append(immunological_weight[i].copy())\n",
        "  immunological_trueweight[i][0:len(immunological_trueweight[i])] = [None] * len(immunological_trueweight[i])\n",
        "#Ans\n",
        "Ans_clinical = [[], [], [], [], [], [], []]  #想讓答案初始值設為None(上面已完成)\n",
        "Ans_immunological = [[], [], []]\n",
        "\n",
        "#entry criterion\n",
        "print('Welcome to SLE diagnosis assistant!')\n",
        "print('Here comes entry criteron.')\n",
        "titer = float(input('ANA at a titer of 1:\"?\" on HEp-2 cells.'))\n",
        "if titer <= 80:   #記得後面要縮排>>考慮改良為while loop\n",
        "  print('Additive criteria are needed before final diagnosis.')\n",
        "  print('''The following are some important conditions in clinical or immunological domains.\n",
        "   Please input \"Y\" if a patient meets the condition while input \"N\" if one doesn't.''')\n",
        "  #嘗試利用for loop處理與醫師問答和答案紀錄\n",
        "  print(\"First, we need to know the patient's clinical conditions through the following questionaire.\")\n",
        "  for i in range(0, len(clinical)):\n",
        "    print(i+1,'.', 'Questions about', clinical[i], 'domain')\n",
        "    for condition in clinical_Q[i]:\n",
        "      print(condition, '?')\n",
        "      Ans_clinical[i] += input( 'Please input \"Y\" or \"N\".' )\n",
        "  print(\"Ans_clinical: \", Ans_clinical)\n",
        "\n",
        "  print(\"Then, we would like to further understand the patient's immunological conditions through the following questionaire.\")\n",
        "  for i in range(0, len(immunological_Q)):\n",
        "    print(i+1,'.', 'Questions about', immunological[i], 'domain')\n",
        "    for condition in immunological_Q[i]:\n",
        "      print(condition, '?')\n",
        "      Ans_immunological[i] += input( 'Please input \"Y\" or \"N\".' )\n",
        "  print(\"Ans_immunological: \", Ans_immunological)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j-0-iKVNBTAr",
        "outputId": "b5d38454-43cb-40d6-96e2-770183e4af52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Welcome to SLE diagnosis assistant!\n",
            "Here comes entry criteron.\n",
            "ANA at a titer of 1:\"?\" on HEp-2 cells.50\n",
            "Additive criteria are needed before final diagnosis.\n",
            "The following are some important conditions in clinical or immunological domains.\n",
            "   Please input \"Y\" if a patient meets the condition while input \"N\" if one doesn't.\n",
            "First, we need to know the patient's clinical conditions through the following questionaire.\n",
            "1 . Questions about Constitutional domain\n",
            "Fever ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "2 . Questions about Hematologic domain\n",
            "Leukopenia ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Thrombocytopenia ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Autoimmune hemolysis ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "3 . Questions about Neuropsychiatric domain\n",
            "Delirium ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Psychosis ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Siezure ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "4 . Questions about Mucocutaneous domain\n",
            "Non-scarring alopecia ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Oral ulcers ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Subacute cutaneous OR discoid lupus ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Acute cutaneous lupus ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "5 . Questions about Serosal domain\n",
            "Pleural or pericardial effusion ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Acute pericarditis ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "6 . Questions about Musculoskeletal domain\n",
            "Joint involvment ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "7 . Questions about Renal domain\n",
            "Proteinuria > 0.5g/24h ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Renal biopsy Class II or V lupus nephritis ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Renal biopsy Class III or IV lupus nephritis ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Ans_clinical:  [['Y'], ['Y', 'N', 'Y'], ['N', 'N', 'Y'], ['N', 'N', 'N', 'Y'], ['Y', 'Y'], ['N'], ['N', 'Y', 'N']]\n",
            "Then, we would like to further understand the patient's immunological conditions through the following questionaire.\n",
            "1 . Questions about Antiphospholipid_antibodies domain\n",
            "Anti-cardiolipin antibodies OR Anti-β2GP1 antibodes OR Lupus anticoagulant ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "2 . Questions about Complement_proteins domain\n",
            "Low C3 OR low C4 ?\n",
            "Please input \"Y\" or \"N\".N\n",
            "Low C3 AND low C4 ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "3 . Questions about SLE_specific_antibodies domain\n",
            "Anti-dsDNA antibody OR Anti-Smith antibody ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Ans_immunological:  [['N'], ['N', 'Y'], ['Y']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#clinical domains & criteria\n",
        "Constitutional = {'Fever':2}\n",
        "Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "Neuropsychiatric = {'Delirium':2, 'Psychosis':3, 'Siezure':5}\n",
        "Mucocutaneous = {'Non-scarring alopecia':2, 'Oral ulcers':2, 'Subacute cutaneous OR discoid lupus':4, 'Acute cutaneous lupus':6}\n",
        "Serosal = {'Pleural or pericardial effusion':5, 'Acute pericarditis':6}\n",
        "Musculoskeletal = {'Joint involvment':6}\n",
        "Renal = {'Proteinuria > 0.5g/24h':4, 'Renal biopsy Class II or V lupus nephritis':8, 'Renal biopsy Class III or IV lupus nephritis':10}\n",
        "#Immunology domains & criteria\n",
        "Antiphospholipid_antibodies = {'Anti-cardiolipin antibodies OR Anti-β2GP1 antibodes OR Lupus anticoagulant':2}\n",
        "Complement_proteins = {'Low C3 OR low C4':3, 'Low C3 AND low C4':4}\n",
        "SLE_specific_antibodies = {'Anti-dsDNA antibody OR Anti-Smith antibody':6}\n",
        "#clinical_Q\n",
        "clinical_Q = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_Q)):\n",
        "  clinical_Q[i] = clinical_Q[i].keys()\n",
        "#Immunological_Q\n",
        "immunological_Q = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(0, len(immunological_Q)):\n",
        "  immunological_Q[i] = immunological_Q[i].keys()\n",
        "#clinical_weight\n",
        "clinical_trueweight = []\n",
        "clinical_weight = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(len(clinical_weight)):\n",
        "  clinical_weight[i] = list(clinical_weight[i].values())\n",
        "  clinical_trueweight.append(clinical_weight[i].copy()) \n",
        "  clinical_trueweight[i][0:len(clinical_trueweight[i])] = [None] * len(clinical_trueweight[i])\n",
        "#immunological_weight\n",
        "immunological_trueweight = [] \n",
        "immunological_weight = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(len(immunological_weight)):\n",
        "  immunological_weight[i] = list(immunological_weight[i].values())\n",
        "  immunological_trueweight.append(immunological_weight[i].copy())\n",
        "  immunological_trueweight[i][0:len(immunological_trueweight[i])] = [None] * len(immunological_trueweight[i])\n",
        "print(clinical_trueweight)\n",
        "print(immunological_trueweight)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZhLpxmqzaov",
        "outputId": "865f4cf8-0a4e-41ff-f479-bc2736a431dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[None], [None, None, None], [None, None, None], [None, None, None, None], [None, None], [None], [None, None, None]]\n",
            "[[None], [None, None], [None]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = [1, 2, 4, 3]\n",
        "a[0:len(a)] = [None] * len(a)\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myxoZtmGxceK",
        "outputId": "56897f0f-b2aa-4dce-e8c1-3f0c3ee7b342"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, None, None, None]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# b = [2, 2, 4, 3]\n",
        "# a = []\n",
        "# a[0] = b[0] #list assignment index out of range\n",
        "# a \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "2KKya8iOz1nq",
        "outputId": "e2edb257-3e08-4a29-ad4a-ff51501442ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-2d2c4f453707>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list assignment index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# entry criterion & ADDITVE CRITERION & 答案與分數轉換 & 分數結果判斷 基本完成\n",
        "# SLE final diagnosis\n",
        "def SLE(total_score):\n",
        "  if total_score >= 10:\n",
        "    return \"Yes. This patient is classified as SLE.\"\n",
        "  else:\n",
        "    return \"No. This patient is not classified as SLE.\"\n",
        "#clinical domains & criteria\n",
        "Constitutional = {'Fever':2}\n",
        "Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "Neuropsychiatric = {'Delirium':2, 'Psychosis':3, 'Siezure':5}\n",
        "Mucocutaneous = {'Non-scarring alopecia':2, 'Oral ulcers':2, 'Subacute cutaneous OR discoid lupus':4, 'Acute cutaneous lupus':6}\n",
        "Serosal = {'Pleural or pericardial effusion':5, 'Acute pericarditis':6}\n",
        "Musculoskeletal = {'Joint involvment':6}\n",
        "Renal = {'Proteinuria > 0.5g/24h':4, 'Renal biopsy Class II or V lupus nephritis':8, 'Renal biopsy Class III or IV lupus nephritis':10}\n",
        "#Immunology domains & criteria\n",
        "Antiphospholipid_antibodies = {'Anti-cardiolipin antibodies OR Anti-β2GP1 antibodes OR Lupus anticoagulant':2}\n",
        "Complement_proteins = {'Low C3 OR low C4':3, 'Low C3 AND low C4':4}\n",
        "SLE_specific_antibodies = {'Anti-dsDNA antibody OR Anti-Smith antibody':6}\n",
        "#clinical_Q\n",
        "clinical_Q = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_Q)):\n",
        "  clinical_Q[i] = clinical_Q[i].keys()\n",
        "#Immunological_Q\n",
        "immunological_Q = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(0, len(immunological_Q)):\n",
        "  immunological_Q[i] = immunological_Q[i].keys()\n",
        "#clinical_weight\n",
        "clinical_weight = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_weight)):\n",
        "  clinical_weight[i] = list(clinical_weight[i].values())\n",
        "clinical_trueweight = clinical_weight.copy()\n",
        "#immunological_weight\n",
        "immunological_weight = [Antiphospholipid_antibodies, Complement_proteins, SLE_specific_antibodies]\n",
        "for i in range(0, len(immunological_weight)):\n",
        "  immunological_weight[i] = list(immunological_weight[i].values())\n",
        "immunological_trueweight = immunological_weight.copy()\n",
        "#Ans\n",
        "Ans_clinical = [[], [], [], [], [], [], []]\n",
        "Ans_immunological = [[], [], []]\n",
        "\n",
        "#entry criterion\n",
        "print('Welcome to SLE diagnosis assistant!')\n",
        "print('Here comes entry criteron.')\n",
        "titer = float(input('ANA at a titer of 1:\"?\" on HEp-2 cells.'))\n",
        "if titer <= 80:   #記得後面要縮排>>考慮改良為while loop\n",
        "  print('Additive criteria are needed before final diagnosis.')\n",
        "  print('''The following are some important conditions in clinical or immunological domains.\n",
        "   Please input \"Y\" if a patient meets the condition while input \"N\" if one doesn't.''')\n",
        "else:\n",
        "  print('This patient is not classified as SLE!')\n",
        "#嘗試利用for loop處理與醫師問答和答案紀錄\n",
        "print(\"First, we need to know the patient's clinical conditions through the following questionaire.\")\n",
        "for i in range(0, len(clinical_Q)):\n",
        "  for condition in clinical_Q[i]:\n",
        "    print(condition, '?')\n",
        "    Ans_clinical[i] += input( 'Please input \"Y\" or \"N\".' )\n",
        "print(Ans_clinical)\n",
        "\n",
        "print(\"Then, we would like to further understand the patient's immunological conditions through the following questionaire.\")\n",
        "for i in range(0, len(immunological_Q)):\n",
        "  for condition in immunological_Q[i]:\n",
        "    print(condition, '?')\n",
        "    Ans_immunological[i] += input( 'Please input \"Y\" or \"N\".' )\n",
        "print(Ans_immunological)\n",
        "\n",
        "#處理clinical domains的weights\n",
        "for i in range(len(Ans_clinical)):\n",
        "  for j in range(len(Ans_clinical[i])):\n",
        "    if Ans_clinical[i][j] == \"Y\":\n",
        "      clinical_trueweight[i][j] = clinical_weight[i][j]\n",
        "    else:\n",
        "      clinical_trueweight[i][j] = 0\n",
        "print(clinical_trueweight)   \n",
        "#處理immunological domains的weights\n",
        "for i in range(len(Ans_immunological)):\n",
        "  for j in range(len(Ans_immunological[i])):\n",
        "    if Ans_immunological[i][j] == \"Y\":\n",
        "      immunological_trueweight[i][j] = immunological_weight[i][j]\n",
        "    else:\n",
        "      immunological_trueweight[i][j] = 0\n",
        "print(immunological_trueweight) \n",
        "clinical_trueweight2 = [] \n",
        "immunological_trueweight2 = []\n",
        "for weight_list in clinical_trueweight:\n",
        "  clinical_trueweight2.append(max(weight_list)) \n",
        "for weight_list in immunological_trueweight:\n",
        "  immunological_trueweight2.append(max(weight_list))\n",
        "print(clinical_trueweight2)  # >>>自己檢查用\n",
        "print(immunological_trueweight2)\n",
        "clinical_total_score = sum(clinical_trueweight2)\n",
        "immunological_total_score = sum(immunological_trueweight2)\n",
        "total_score = clinical_total_score + immunological_total_score\n",
        "print(clinical_total_score)\n",
        "print(immunological_total_score)\n",
        "print(total_score)\n",
        "print(SLE(total_score))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "vqRwAwfzNSSC",
        "outputId": "052ac6aa-0be3-4d3e-c376-0616dde3a0d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Welcome to SLE diagnosis assistant!\n",
            "Here comes entry criteron.\n",
            "ANA at a titer of 1:\"?\" on HEp-2 cells.81\n",
            "This patient is not classified as SLE!\n",
            "First, we need to know the patient's clinical conditions through the following questionaire.\n",
            "Fever ?\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \"\"\"\n\u001b[0;32m--> 727\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-4a2c099d5c1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     55\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mcondition\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclinical_Q\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcondition\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'?'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0mAns_clinical\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'Please input \"Y\" or \"N\".'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAns_clinical\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Constitutional = {'Fever':2}\n",
        "Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "Neuropsychiatric = {'Delirium':2, 'Psychosis':3, 'Siezure':5}\n",
        "Mucocutaneous = {'Non-scarring alopecia':2, 'Oral ulcers':2, 'Subacute cutaneous OR discoid lupus':4, 'Acute cutaneous lupus':6}\n",
        "Serosal = {'Pleural or pericardial effusion':5, 'Acute pericarditis':6}\n",
        "Musculoskeletal = {'Joint involvment':6}\n",
        "Renal = {'Proteinuria > 0.5g/24h':4, 'Renal biopsy Class II or V lupus nephritis':8, 'Renal biopsy Class III or IV lupus nephritis':10}\n",
        "#clinical_Q\n",
        "clinical_Q = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_Q)):\n",
        "  clinical_Q[i] = clinical_Q[i].keys()\n",
        "#clinical_weight\n",
        "clinical_weight = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_weight)):\n",
        "  clinical_weight[i] = list(clinical_weight[i].values())\n",
        "clinical_trueweight = clinical_weight.copy()\n",
        "print(clinical_weight)\n",
        "print(clinical_trueweight)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9MQKYwJhZ-W",
        "outputId": "f1428ac3-4969-4f3f-bb28-5189196c239e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2], [3, 4, 4], [2, 3, 5], [2, 2, 4, 6], [5, 6], [6], [4, 8, 10]]\n",
            "[[2], [3, 4, 4], [2, 3, 5], [2, 2, 4, 6], [5, 6], [6], [4, 8, 10]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "rGI4XWrHhku-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Constitutional = {'Fever':2}\n",
        "Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "Neuropsychiatric = {'Delirium':2, 'Psychosis':3, 'Siezure':5}\n",
        "Mucocutaneous = {'Non-scarring alopecia':2, 'Oral ulcers':2, 'Subacute cutaneous OR discoid lupus':4, 'Acute cutaneous lupus':6}\n",
        "Serosal = {'Pleural or pericardial effusion':5, 'Acute pericarditis':6}\n",
        "Musculoskeletal = {'Joint involvment':6}\n",
        "Renal = {'Proteinuria > 0.5g/24h':4, 'Renal biopsy Class II or V lupus nephritis':8, 'Renal biopsy Class III or IV lupus nephritis':10}\n",
        "#clinical_weight\n",
        "clinical_weight = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_weight)):\n",
        "  clinical_weight[i] = clinical_weight[i].values()\n",
        "print(clinical_weight)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAPQXHVJbVoc",
        "outputId": "a771893e-5ff4-42d6-a1a8-ea902fa155bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[dict_values([2]), dict_values([3, 4, 4]), dict_values([2, 3, 5]), dict_values([2, 2, 4, 6]), dict_values([5, 6]), dict_values([6]), dict_values([4, 8, 10])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#debug try\n",
        "Ans_clinical = [[], [], [], [], [], [], []]\n",
        "Constitutional = {'Fever':2}\n",
        "Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "Neuropsychiatric = {'Delirium':2, 'Psychosis':3, 'Siezure':5}\n",
        "Mucocutaneous = {'Non-scarring alopecia':2, 'Oral ulcers':2, 'Subacute cutaneous OR discoid lupus':4, 'Acute cutaneous lupus':6}\n",
        "Serosal = {'Pleural or pericardial effusion':5, 'Acute pericarditis':6}\n",
        "Musculoskeletal = {'Joint involvment':6}\n",
        "Renal = {'Proteinuria > 0.5g/24h':4, 'Renal biopsy Class II or V lupus nephritis':8, 'Renal biopsy Class III or IV lupus nephritis':10}\n",
        "#clinical_Q\n",
        "clinical_Q = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "for i in range(0, len(clinical_Q)):\n",
        "  clinical_Q[i] = clinical_Q[i].keys()\n",
        "for i in range(0, len(clinical_Q)):\n",
        "  for condition in clinical_Q[i]:\n",
        "    print(condition, '?') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QS4z0vdyT4jJ",
        "outputId": "68aa2f5c-6db5-4df9-907c-d194249afa5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fever ?\n",
            "Leukopenia ?\n",
            "Thrombocytopenia ?\n",
            "Autoimmune hemolysis ?\n",
            "Delirium ?\n",
            "Psychosis ?\n",
            "Siezure ?\n",
            "Non-scarring alopecia ?\n",
            "Oral ulcers ?\n",
            "Subacute cutaneous OR discoid lupus ?\n",
            "Acute cutaneous lupus ?\n",
            "Pleural or pericardial effusion ?\n",
            "Acute pericarditis ?\n",
            "Joint involvment ?\n",
            "Proteinuria > 0.5g/24h ?\n",
            "Renal biopsy Class II or V lupus nephritis ?\n",
            "Renal biopsy Class III or IV lupus nephritis ?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Ans_Hematologic = []\n",
        "Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "Hematologic_Q = Hematologic.keys()\n",
        "for condition in Hematologic_Q:\n",
        "  print(condition, '?')\n",
        "  Ans_Hematologic += input( 'Please input \"Y\" or \"N\".' )\n",
        "print(Ans_Hematologic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gD3kAMlp_4ij",
        "outputId": "de07e1da-73ea-42e9-d8a0-5c390c857a2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Leukopenia ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Thrombocytopenia ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "Autoimmune hemolysis ?\n",
            "Please input \"Y\" or \"N\".Y\n",
            "['Y', 'Y', 'Y']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "zPp1KbfHR6hg",
        "outputId": "bef77de9-2cf2-401f-ed66-211e49407d92"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-fd4f4d0d5bdd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdomain\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclinical_Q\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m   \u001b[0;32mfor\u001b[0m \u001b[0mcondition\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclinical_Q\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdomain\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcondition\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'?'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0mAns_clinical\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'Please input \"Y\" or \"N\".'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not dict_keys"
          ]
        }
      ],
      "source": [
        "# def SLE(total_score):\n",
        "#   if total_score >= 10:\n",
        "#     return \"Yes. This patient is classified as SLE.\"\n",
        "#   else:\n",
        "#     return \"No. This patient is not classified as SLE.\"\n",
        "# print(SLE(4))\n",
        "\n",
        "#clinical domains & criteria\n",
        "# Constitutional = {'Fever':2}\n",
        "# Hematologic = {'Leukopenia':3, 'Thrombocytopenia':4, 'Autoimmune hemolysis':4}\n",
        "# Neuropsychiatric = {'Delirium':2, 'Psychosis':3, 'Siezure':5}\n",
        "# Mucocutaneous = {'Non-scarring alopecia':2, 'Oral ulcers':2, 'Subacute cutaneous OR discoid lupus':4, 'Acute cutaneous lupus':6}\n",
        "# Serosal = {'Pleural or pericardial effusion':5, 'Acute pericarditis':6}\n",
        "# Musculoskeletal = {'Joint involvment':6}\n",
        "# Renal = {'Proteinuria > 0.5g/24h':4, 'Renal biopsy Class II or V lupus nephritis':8, 'Renal biopsy Class III or IV lupus nephritis':10}\n",
        "# #clinical_Q\n",
        "# clinical_Q = [Constitutional, Hematologic, Neuropsychiatric, Mucocutaneous, Serosal, Musculoskeletal, Renal]\n",
        "# Ans_clinical = []\n",
        "# for domain in clinical_Q:\n",
        "#   clinical_Q[domain] = clinical_Q[domain].keys()\n",
        "\n",
        "for domain in clinical_Q:\n",
        "  for condition in clinical_Q[domain]:\n",
        "    print(condition, '?')\n",
        "    Ans_clinical += input( 'Please input \"Y\" or \"N\".' )\n",
        "print(Ans_clinical)\n",
        "  \n",
        "#Immunology domains & criteria\n",
        "# Antiphospholipid_antibodies = {'Anti-cardiolipin antibodies OR Anti-β2GP1 antibodes OR Lupus anticoagulant':2}\n",
        "# Complement_proteins = {'Low C3 OR low C4':3, 'Low C3 AND low C4':4}\n",
        "# SLE-specific_antibodies = {'Anti-dsDNA antibody OR Anti-Smith antibody':6}\n",
        "\n",
        "\n",
        "# print('Welcome to SLE diagnosis assistant!')\n",
        "# print('Here comes entry criteron.')\n",
        "# titer = float(input('ANA at a titer of 1:\"?\" on HEp-2 cells.'))\n",
        "# if titer <= 80:\n",
        "#   print('Additive criteria are needed before final diagnosis.')\n",
        "#   print('''The following are some important conditions in clinical or immunological domains.\n",
        "#    Please input \"Y\" if a patient meets the condition while input \"N\" if one doesn't.''')\n",
        "#   while 1 <= i <= len():\n",
        "#   a = [Constitutional_Q, Hematologic_Q, Neuropsychiatric_Q]???\n",
        "# else:\n",
        "#   print('This patient is not classified as SLE!')"
      ]
    }
  ]
}