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A Machine Learning Model for Predicting Deterioration of COVID-19 Inpatients

Public repository containing research code for the COVID-19 prediction model, described in the manuscript "A Machine Learning Model for Predicting Deterioration of COVID-19 Inpatients"


Omer Noy*, Dan Coster*, Maya Metzger, Itai Attar, Shani Shenhar-Tsarfaty, Shlomo Berliner, Galia Rahav, Ori Rogowski, Ron Shamir


Install requirements

python -m pip install -r requirements.txt

This code was tested with python 3.7.


The repository is organized as follows:

  • anomaly_scores Anomaly detection approaches, used as anomaly features
  • data_preprocessing Code for preprocessing the parsed data, including time-series formatting, imputation, etc.
  • feature_generation Code for features engineering, including historical summary statistics and trend features.
  • feature_selection Code for feature selection strategies.
  • ml_models ML model classes, including pre-training processing, fit and evaluation methods.
  • outlier_removal Values Removal according to predefined clinical ranges.


The data used in our study cannot be shared. This section describes the data format used for the code.

Our data-specific parser generates 3 main pandas dataframes:

  • Baseline_df Contains demographics and background disease (static features).
  • vital_df Vital signs (longitudinal features).
  • labs_df Lab tests (longitudinal features).

Baseline dataframe format (Baseline_df):

Columns Data type
Patient ID object
Admission Date datetime64[ns]
Gender bool
Age float64
... String
Background diseases bool

Longitudinal dataframes format (vital_df, labs_df):

Columns Data type
Patient ID object
Date Time datetime64[ns]
Feature Name String
Value float64

Using data_preprocessing/create_time_series_data, the dataframes can be merged and pivoted into a time-series format, with columns representing features and rows representing the longitudinal patients' observations.


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