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Data and code needed to reproduce the findings of the live birth prediction study.

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LiveBirthPrediction_Data_Code

Data and code needed to reproduce the findings of the live birth prediction study:

Liu, Hang, Zhuoran Zhang, Yifan Gu, Changsheng Dai, Guanqiao Shan, Haocong Song, Daniel Li, Wenyuan Chen, Ge Lin, and Yu Sun. "Development and evaluation of a live birth prediction model for evaluating human blastocysts: a retrospective study." eLife 12 (2023): e83662.

Figure 1-Source Data 1: This directory contains the code to reproduce the model.

Figure-2-Code-Data: This directory contains the data and code used for generating ROC curves shown in Figure 2 of the manuscript.

Figure 3-Source Data 1: This directory contains the data and code used for generating the AUC ranking chart shown in Fgiure 3 of the manuscript.

Figure-4-Data: This directory contains the data and code used for generating the heatmaps shown in Figure 4 of the manuscript.

Codes and software used to analyze the data:

  1. P-value analysis of clinical features: Python 3.6, scikit-learn 1.1;
  2. Sequential forward feature selection: MLXTEND 0.21.0 (http://rasbt.github.io/mlxtend/);
  3. Model development: PyTorch 1.10.1, PyTorch Image Models(timm)(https://github.com/rwightman/pytorch-image-models);
  4. ROC curve comparison: MedCalc 20.

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Data and code needed to reproduce the findings of the live birth prediction study.

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