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cs-598-dlh-paper-32

Dependencies

  • Python >= 3.8.11
  • Please use conda to install all the dependencies using the command
    • conda env create -f environment.yml

How to run the code

Once you install the dependency, please run the following code to open Jupyter Notebook on your terminal (Mac OS/Linux system)

  • jupyter notebook Then you can run the cell block one by one.

Data download instruction

The filtered data of MIMIC-III is dataset/t1 folder. We only use a subset of MIMIC-III and it is filtered based on the requirement in the paper. This subset of MIMIC-III is already decoded that does not contains any original information from the original MIMIC-III.

The file 'Extract MIMIC III Dataset.ipynb` contains code to extract patient information from the raw MIMIC III Dataset.

Table of results

Macro-Averging Measures of Disease Prediction

Method Accuracy Recall Precision F1 Score
Euclidean 0.3703 0.38209 0.4264 0.2907
Cosine 0.3925 0.4061 0.3968 0.3842
CNN-triple 0.4888 0.4832 0.4832 0.4824

Patient Clustering Performance Based on Learned Distance

Method Rand index Purity NMI
Euclidean 0.4284 0.4 0.0190
Cosine 0.5473 0.4074 0.0162
CNN-triplet 0.5653 0.4888 0.0679

Citation:

  1. Suo, Q., Ma, F., Yuan, Y., Huai, M., Zhong, W., Gao, J., Zhang, A. (2018). Deep Patient Similarity Learning for Personalized Healthcare. IEEE transac- tions on nanobioscience, 17(3), 219–227. https://doi.org/10.1109/TNB.2018.2837622