Ten key considerations for developing interpretable risk prediction models

Artificial intelligence (AI) and machine learning (ML) have rapidly transformed clinical risk prediction by enabling high-dimensional data analysis, automated pattern recognition, and individualized risk estimation [1]. Compared with conventional regression models, ML algorithms can automatically learn nonlinear relationships among diverse clinical variables and optimize predictive performance through hyperparameter tuning [2]. These advances have driven successful clinical applications in diagnostic classification and disease forecasting. Notable examples include a DenseNet-based model for multiclass thyroid disorder prediction using Tc-99m scintigraphy [3], antimicrobial resistance modeling [4,5], and the early prediction of infectious threats such as hospital-acquired infections (HAI) [6],multidrug-resistant organisms (MDRO) [7], and invasive Escherichia coli [8].Despite these successes, current risk prediction models face persistent limitations. Many models emphasize predictive accuracy while neglecting interpretability, transparency, and reproducibilityfactors essential for clinical adoption and ethical deployment [9]. To address these methodological gaps, this study proposes a structured workflow for developing Interpretable Risk Prediction Models (IRPMs) that integrate interpretability checkpoints within the established Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) + artificial intelligence (AI) [10] and Prediction model Risk Of Bias ASsessment Tool (PROBAST) + AI [11] guidelines. The IRPM framework extends the traditional thirteen-step process for developing, validating, and reporting prediction models by embedding interpretability-specific milestones and facilitating compliance with FAIR principles (Findable, Accessible, Interoperable, and Reusable) [12].The methodological foundation of this workflow is illustrated through a recent case study on early prediction of E. coli infection among Intensive Care Unit (ICU) patients, conducted using the large-scale Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset [13].In this study, Yang et al. (2025) [13] applied Least Absolute Shrinkage and Selection Operator (LASSO) + Boruta for feature selection and built eight ML models, identifying the support vector machine (SVM) as the most optimal model (Area Under Receiver Operating Characteristic Curve (AUC) = 0.745; 95 % Confidence Interval (CI): 0.726-0.764). It should be noted that although the SVM achieved the best discrimination among evaluated models (AUC = 0.745), this level of performance remains modest for clinical deployment and highlights the importance of complementary It should be noted that the study is presented as an illustrative example rather than as the basis for the framework itself. The IRPM recommendations are intended to generalize across clinical prediction settings. For example, prior work [14] has shown that different class-imbalance correction strategies can substantially alter calibration and discrimination performance, highlighting the importance of reporting resampling decisions transparently.Recent international guidelines such as TRIPOD + AI [10] and PROBAST + AI [11] set comprehensive standards for reporting and evaluating AI-driven prediction models. They focus primarily on methodological completeness and risk-of-bias assessment rather than on interpretability workflows. The IRPMs workflow complements these by introducing three operational interpretability safeguards: (i) an interpretability checkpoint, (ii) explanation stability testing, and (iii) causal plausibility assessment (Table 1 and Supplementary Table S1). Applied to the study of Yang [13], IRPMs clarify the novelty of how their ML model + SHAP pipeline aligns with TRIPOD + AI Items 12e (performance + interpretability) and 18e-18f (open science) [10],while highlighting areas for improvement, such as subgroup fairness analysis and external validation.Specifically, Yang and colleagues first assessed the current risk of E. coli infection in ICU patients, and then reviewed prior studies on early prediction using cephalosporins and invasive procedures [13].They identified an urgent need for robust, clinically interpretable ML models specifically tailored to ICU populations. When designing a study, it is crucial to clearly detail the novelty: whether your model outperforms existing approaches or demonstrates innovation in interpreting clinical variables for easier infection surveillance. Employing mature, validated ML algorithms (e.g., logistic regression, random forest, and other classification models) is encouraged to enhance transparency and clinical credibility.Overall, when designing a study, researchers should articulate novelty explicitly-whether the model introduces an innovative interpretability approach or enhances clinical usability. The IRPMs workflow operationalizes these principles by translating TRIPOD + AI's high-level reporting recommendations into actionable modeling steps.Data leakage is when information from validation or test sets inadvertently influences model training, which remains one of the most common pitfalls in clinical ML. Preventing data leakage is essential for maintaining model validity. A common practice is to use temporal validation, establishing a strict chronological split between your derivation and validation datasets [15]. The guidelines of TRIPOD + AI at Items 5-12 mandate transparent reporting of data sources, preprocessing, and partitioning [10].In their E. coli study [13], Yang et al. extracted demographic, laboratory, comorbidity, and treatment data for 52,554 ICU patients from MIMIC-IV. Missing values were imputed using the missForest algorithm [16], and undersampling for class balancing [14] was performed strictly within training folds Class imbalance is where positive outcomes are much fewer than negative ones, which can distort performance and fairness. TRIPOD + AI (Item 13) requires