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Exploiting interpretable simple structures for predictive healthcare models
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AI
DOI：10.1080/24725579.2026.2663285.png)
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Abstract
En 中文
Precision medicine traditionally identifies patient subgroups using genomic and molecular profiling, but such approaches are costly and complex, limiting widespread implementation. Electronic health records (EHRs) provide a scalable and accessible alternative for identifying clinically meaningful subgroups. We introduce a Simple Structure (SS) method, which partitions patients based on the ease of predicting outcomes using simple models, rather than relying on unsupervised clustering or feature-based stratification. Two datasets are analyzed: one predicting COVID-19 morbidity and another predicting heart failure diagnosis. For the COVID-19 data, an ensemble of logistics regression models trained on simple structures outperforms the benchmarks, and the features used by the ensemble model are more diverse than those used by the global model, thus revealing subgroups of patients where different comorbidities are important. Similarly, in the heart failure dataset the SS-identified subgroups discover patient subgroups with distinct risk factor profiles. This suggests that rather than a single global model with a fixed set of features, different patient subgroups exhibit unique predictors of heart failure. The ensemble of simple models, leveraging these subgroup-specific risk factors, outperforms simple global models while maintaining interpretability, offering a step toward precision healthcare.
Keywords:
Precision medicine
interpretable machine learning
covid-19 morbidity prediction
heart failure prediction
Journal
I
IF:
1.3
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24
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0