Explainable Predictive Analytics for Supporting Clinical Risk Assessment in Healthcare Information Systems

Martin Novak

Explainable Predictive Analytics for Supporting Clinical Risk Assessment in Healthcare Information Systems

Author(s)	Martin Novak
Country	Slovakia
Abstract	Healthcare information systems contain increasingly large collections of longitudinal clinical and administrative data that can support predictive risk assessment. Conventional statistical models remain important because their structure can often be interpreted directly, while more flexible machine-learning models may capture complex nonlinear relationships but provide less transparent predictions. In clinical environments, predictive accuracy alone is insufficient because practitioners must understand the factors contributing to a risk estimate before deciding whether the prediction is clinically plausible and relevant. This hypothetical methodological study evaluates explainable predictive analytics for supporting clinical risk assessment using structured healthcare information-system data. A fictional dataset containing 2,400 patient encounters and twenty-six predictor variables is divided into training, validation, and independent testing subsets. Three conceptual models are compared: regularized logistic regression, an explainable generalized additive model, and a random forest accompanied by local model-agnostic explanations. The target outcome is a fictional composite thirty-day adverse clinical outcome used only for methodological illustration. The hypothetical analysis evaluates discrimination and classification through area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, precision, F1 score, and Brier score. The random forest produces the strongest assigned predictive performance, while the generalized additive model provides a favorable balance between accuracy and intrinsic interpretability. A second analysis examines explanation fidelity, stability, sparsity, and availability, illustrating that post-hoc explanations can increase transparency but do not make a complex model equivalent to an inherently interpretable model. The study concludes that explainable predictive analytics can support clinical risk assessment when predictive performance, calibration, explanation quality, data provenance, and clinician oversight are considered jointly. Explanations should assist verification and contextual interpretation rather than create unjustified confidence in model outputs. External validation, prospective assessment, subgroup evaluation, and rigorous governance would be required before any real healthcare deployment.
Keywords	Explainable Artificial Intelligence, Clinical Risk Assessment, Predictive Analytics, Healthcare Information Systems, Clinical Decision Support, Machine Learning, Electronic Health Records, Interpretability, Risk Prediction, Health Informatics.
Field	Engineering
Published In	Volume 2, Issue 5, September-October 2026
Published On	2026-10-09

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Explainable Predictive Analytics for Supporting Clinical Risk Assessment in Healthcare Information Systems

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