Medical Data Science

Machine learning applications for critical care and medical decision-making

Medical Data Science

Machine learning applications for critical care and medical decision-making

Critical care medicine generates massive amounts of data, but making real-time predictions from it is challenging. We develop machine learning systems for ICU nutrition, respiratory diseases, and collaborative medical research—improving patient outcomes through data-driven insights.

Research Areas

Critical Care Nutrition

Predict ICU feeding complications before symptoms appear. Optimize protein intake per patient. Identify high-risk cases early using decade-long datasets from Rabin Medical Center.

Respiratory Medicine

AI phenotypical analysis for rare diseases like NTM infections. Accelerate diagnosis and predict treatment responses.

Medical Data Collaboration

Privacy-preserving crowdsourcing for medical datasets. Enable scalable research while protecting patient data.

Technical Methods

Predictive Modeling

Machine learning models trained on extensive clinical datasets. Validation using real patient data from hospital systems.

Phenotypical Analysis

Pattern recognition in complex patient profiles to identify disease characteristics and treatment responses.

Privacy-Preserving Learning

Methods that enable data sharing and collaboration while maintaining patient privacy and data security.

Impact

Our critical care nutrition models have shown reductions in feeding complications through early prediction. Respiratory medicine applications enable faster diagnosis of rare conditions. The collaborative data collection frameworks support medical research at scale while preserving privacy.

Left: Real-time patient monitoring generating continuous data streams. Middle: Critical care nutrition delivery systems. Right: AI-powered analysis of complex medical 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