Assessing Disease Comorbidity in Hospital Patients through Machine Learning and Network Analysis Techniques

LAZARO GIBERT GARCIA; GUSTAVO PINERO; AXEL SOTO; ANA MAGUITMAN; GERARDO SIMARI; CARLOS CHESÑEVAR; GABRIELA DIAZ; CAVERZAN, ESTEBAN; CARLOS LORENZETTI

Handbook of Machine Learning in Healthcare

Springer

Año: 2025;

Abstract Accurate assessment of disease comorbidity in hospital patients has alwaysbeen crucial for improving treatment strategies and healthcare outcomes. In the lastfew years, different machine learning (ML) and network analysis (NA) techniqueshave been developed to enhance the detection, prediction, and understanding ofcomorbid conditions in hospital patients. In many cases, these techniques allowefficient identification of comorbidity from medical records, without requiring timeconsumingand expensive clinical annotation, which is also prone to inconsistencies.In this chapter, we survey the state-of-the-art ML and NA techniques to improvethe detection, analysis and prediction of comorbid conditions in hospital patients.We describe and compare different approaches based on ML and NA algorithmsto model the complex relationships between co-occurring diseases, analyzing howdifferent features (such as patient demographics, medical records, and laboratoryresults) can be used to train and optimize the associated models.We also discuss theuse of different advanced techniques applied to electronic health records—such asnamed entity recognition (NER)—that aim to provide better interpretability of theresulting models, with direct implications for improving patient management andhealthcare delivery.