Medical Data Science

Machine learning for critical care — predicting ICU feeding complications, phenotyping rare respiratory diseases, and enabling privacy-preserving medical data collaboration.

Medical Data Science

Medical Data Science

Machine learning for ICU nutrition, rare respiratory disease phenotyping, and privacy-preserving clinical data collaboration.

Critical care patients are among the most vulnerable — yet managing their nutrition, predicting complications, and coordinating care remains largely manual. We build machine learning systems that turn ICU data into actionable clinical decisions, improving patient outcomes through real-time prediction.

Research Areas

  • ICU Nutrition & Feeding Complications — Models trained on a decade of records from Rabin Medical Center that predict feeding intolerance and caloric deficit before symptoms appear, optimizing enteral nutrition per patient.
  • Rare Respiratory Disease Phenotyping — Unsupervised clustering and AI phenotypical analysis for NTM lung infections, accelerating diagnosis and predicting treatment response in pulmonology.
  • Privacy-Preserving Medical Data Collaboration — Federated learning and crowdsourcing frameworks enabling multi-hospital research without exposing patient records.

Technical Methods

Our pipeline combines gradient-boosted trees and deep learning for tabular clinical data, time-series models for continuous monitoring streams, and survival analysis for time-to-event outcomes. All models are validated prospectively against held-out hospital cohorts before clinical reporting.

Left: Real-time patient monitoring generating continuous data streams. Middle: Critical care enteral nutrition delivery. Right: AI-driven analysis of complex clinical data patterns.

Related Publications

2025

  1. Survival Forest Models for ICU Mortality Prediction based on Nutrition and Clinical Factors
    2025
    Anat Goldstein, Chen Hajaj, Pierre Singer, and Orit Raphaeli
    IEEE Access
  2. Predicting Onset and Progression of Neurodegenerative Diseases Using Blood Test Data and Machine Learning Models
    2025
    Amir Glik, Chen Hajaj, Orit Rephaeli, and Anat Goldstein

2024

  1. Phenotypical Characteristics of Nontuberculous Mycobacterial Infection in Patients with Bronchiectasis
    2024
    Assaf Frajman, Shimon Izhakian, Ori Mekiten, Ori Hadar, Ariel Lichtenstadt, Chen Hajaj, Shon Shchori, Moshe Heching, Dror Rosengarten, and Mordechai R Kramer
    Respiratory Research

2023

  1. Protein Intake and Clinical Outcomes of Enterally Fed Critically Ill Patients
    2023
    O Raphaeli, L Statlander, C Hajaj, I Bendavid, and Pierre Singer
    Clinical Nutrition ESPEN
  2. Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study
    2023
    Orit Raphaeli, Liran Statlender, Chen Hajaj, Itai Bendavid, Anat Goldstein, Eyal Robinson, and Pierre Singer
    Nutrients

2022

  1. Using the Cardio-Vascular Index (CVRI) to Predict Mortality in Septic Shock
    2022
    O Raphaeli, I Bendavid, C Hajaj, L Statlander, A Goldstein, E Chen, P Singer, and U Gabbay
    ISICEM

2021

  1. Prediction Model for the Spread of the COVID-19 Outbreak in the Global Environment
    2021
    Ron S Hirschprung, and Chen Hajaj
    Heliyon
  2. Feeding Intolerance as a Predictor of Clinical Outcomes in Critically Ill Patients: A Machine Learning Approach
    2021
    O Raphaeli, C Hajaj, I Bendavid, A Goldstein, E Chen, and P Singer
    Clinical Nutrition ESPEN
  3. Using machine learning to support early prediction of feeding intolerance in critically ill patients
    2021
    O Raphaeli, C Hajaj, I Bendavid, A Goldstein, E Chen, and P Singer
    ESICM LIVES

2018

  1. A crowdsourcing framework for medical data sets
    2018
    Cheng Ye, Joseph Coco, Anna Epishova, Chen Hajaj, Henry Bogardus, Laurie Novak, Joshua Denny, Yevgeniy Vorobeychik, Thomas Lasko, Bradley Malin, and  others
    AMIA Summits on Translational Science Proceedings

2015

  1. Automatic Anatomical Shape Correspondence and Alignment Using Mesh Features
    2015
    Tal Darom, Yaniv Gur, Chen Hajaj, and Yosi Keller
    Biomedical Imaging (ISBI), 2015
  2. Strategy-Proof and Efficient Kidney Exchange Using a Credit Mechanism
    2015
    Chen Hajaj, John P Dickerson, Avinatan Hassidim, Tuomas Sandholm, and David Sarne
    In proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence