Details

Autor(en) / Beteiligte

• Herrero-Zazo, Maria
• Fitzgerald, Tomas
• Taylor, Vince
• Street, Helen
• Chaudhry, Afzal N.
• Bradley, John R.
• Birney, Ewan
• Keevil, Victoria L.

Titel

Using machine learning to model older adult inpatient trajectories from electronic health records data

Ist Teil von

• iScience, 2023-01, Vol.26 (1), p.105876-105876, Article 105876

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Electronic Health Records (EHR) data can provide novel insights into inpatient trajectories. Blood tests and vital signs from de-identified patients’ hospital admission episodes (AE) were represented as multivariate time-series (MVTS) to train unsupervised Hidden Markov Models (HMM) and represent each AE day as one of 17 states. All HMM states were clinically interpreted based on their patterns of MVTS variables and relationships with clinical information. Visualization differentiated patients progressing toward stable ‘discharge-like’ states versus those remaining at risk of inpatient mortality (IM). Chi-square tests confirmed these relationships (two states associated with IM; 12 states with ≥1 diagnosis). Logistic Regression and Random Forest (RF) models trained with MVTS data rather than states had higher prediction performances of IM, but results were comparable (best RF model AUC-ROC: MVTS data = 0.85; HMM states = 0.79). ML models extracted clinically interpretable signals from hospital data. The potential of ML to develop decision-support tools for EHR systems warrants investigation. [Display omitted] •Time-series blood test & vital sign data from older inpatients were presented to HMM•Hidden clinically interpretable states were extracted, linked with diagnoses and death•States modeled inpatient trajectories, differentiating risk from admission-discharge•The clinical interpretation of HMM states helped explain how ML models organize data Health technology; Diagnostic technique in health technology; Applied computing in medical science; Machine learning