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 justification for resampling and recalibration [10]. Ideally, the clinical data can be collected from different hospitals or countries, but due to the privacy of the patients and policy of different countries, it could be difficult to realize multicenter validation, which might weaken the model's applicability on a broad scale. In Yang et al.'s dataset [13], E. coli infection represented 7.9 % of cases (4, 157 patients). The authors applied undersampling to balance classes while preserving data authenticity. Alternative approaches include Synthetic Minority Over-sampling Technique (SMOTE), Oversample using Adaptive Synthetic (ADASYN), cost-sensitive learning, and prevalence-aware threshold optimization. While oversampling can improve sensitivity, it risks introducing synthetic artifacts. Undersampling maintained real-world structure and yielded strong calibration and Decision Curve Analysis (DCA)results. Future IRPMs studies should pursue multi-center data collection or federated learning to mitigate imbalance and enhance generalizability, aligning with TRIPOD + AI (Item 16) ("differences between development and evaluation data") [10].A common question for researchers performing machine learning is whether regression or classification models should be chosen. Model selection must balance interpretability and predictive performance. TRIPOD + AI (Item 12c) [10] To ensure reproducibility through transparent hyperparameter tuning, some researchers prefer to use nested cross-validation (nCV) [19]. In this approach, the classification model and features for each outer fold are selected based on which combination achieves the highest AUPRC or balanced accuracy during inner-fold validation. TRIPOD + AI (Item 12c) mandates reporting of all model-building steps and tuning procedures [10]. Yang et al. used grid search and k-fold cross-validation, emphasizing reproducibility over exhaustive optimization [13]. Alternative strategies include randomized search, Bayesian optimization, and ensemble stacking, which can improve performance but introduce stochasticity. The TRIPOD + AI Adherence Tool [10] provides scoring rules for reproducibility, encouraging researchers to publish tuning scripts, fixed random seeds, and parameter grids. Future IRPMs should include hyperparameter tables, validation variance, and rationale for algorithmic choices to enable replication.Area Under the Curve (AUC) and the Receiver Operating Characteristic curve (ROC) are commonly used to evaluate the model's performance with a 95% Confidence Interval (CI).TRIPOD + AI (Item 12e) recommends reporting discrimination, calibration, and clinical utility [10].Other performance metrics, including sensitivity, specificity, F1-score, accuracy, and balanced accuracy, should also be reported. Their uncertainty can be estimated using bootstrap resampling (e.g., 1,000 resamples). It should be noted that due to class imbalance in clinical data, metrics such as AreaUnder the Precision-Recall Curve (AUPRC) or Average Precision are often more informative than AUC.Besides, the model's performance can be impacted by some uncertainties, for research using earlystage clinical data to predict E. coli infection during admission (e.g., duration of antibiotics and catheter use) [13], the limited availability of variables and the required timeliness of the prediction significantly increase the difficulty of improving the model's performance. Therefore, when the AUC of the model Interpretability can transform statistical predictions into clinical insights. TRIPOD + AI (Items 12e and 14) require transparent explanation and fairness evaluation [10]. When the best model has been chosen with robust calibration and clinical utility supported by decision analyses, the clinical features can be interpreted via SHAP (SHapley Additive exPlanations) [20] or Local Interpretable Model-Agnostic Explanations (LIME) [21] Interpretability can be categorized as intrinsic (inherent transparency, as in logistic regression or decision trees) and post-hoc (external explanation of complex models such as ensembles or neural networks) (Supplementary Table S2).To strengthen interpretability and confirm robustness, SHAP can be complemented with other XAI methods (Supplementary Table S3 S4.Every IRPMs must transparently acknowledge constraints. It is important to mention the limitations and compare the relevant work to understand the stage at which your study currently stands and how it may be improved in the future. TRIPOD + AI (Item 26) stresses that transparent discussion of limitations contextualizes results and strengthens credibility. For example, in the study of Yang et al., the E. coli infection patients were derived from a single-center database (MIMIC-IV), which lacks external, multi-center validation, thereby limiting the generalizability to other healthcare systems with different patient characteristics and microbiological epidemiology. No method is perfect. Common limitations include bias arising from missing data and imputation assumptions, the risk of overfitting despite cross-validation, trade-offs between interpretability and predictive performance, lack of external validation, and the inability to establish causal relationships.Due to the workload or data limitations, it is not always possible to apply fully robust, validated methods to develop a robust, interpretable, and clinically applicable tool. As datasets continue to grow, deep learning approaches, such as artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), could be leveraged to automatically learn high-order nonlinear relationships among complex clinical and microbiological variables. Importantly, recent advances in interpretable deep learning, including attention mechanisms, concept activation vectors, and counterfactual explanation frameworks, have improved the interpretability of deep learning models without necessarily compromising predictive performance. Future IRPMs should aim to combine predictive power with explanation stability and causal plausibility, rather than viewing complexity and interpretability as opposing forces. Future IRPMs will likely evolve toward multimodal foundation models integrating structured electronic health record (EHR) data, microbiology reports, imaging, and genomics through self-supervised representation learning. Federated training frameworks may further enhance generalizability across institutions while preserving patient privacy. Future IRPMs should include ethical frameworks (e.g., JustEFAB [22], US Food and Drug Administration (FDA)and Health Canada guidance) for deployment. Importantly, future systems should aim to prioritize causal robustness, fairness auditing, and prospective validation to ensure real-world clinical reliability.Most importantly, future IRPMs should combine predictive accuracy with explanation stability, fairness, and clinical usability, fully aligned with TRIPOD + AI's open-science ethos.The IRPMs study bridges methodological rigor and interpretability within the evolving standards of Clarifies scope and intended outcome.2. Register protocol and metadata dataset DOI (e.g., OSF, Zenodo).Ensures findability and traceability of study materials.3. Specify data source (e.g., MIMIC-IV or institutional dataset) and access link. Allows data verification and reuse.4. Describe data partitioning (train/validation/test) and sample size. Prevents data leakage and bias.5. List all variables used, definitions, units, and missing-data handling meth od.Supports data reconstruction and co nsistency.Feature Selection and Pre processing 6. Provide code for feature selection methods (e.g., LASSO, Boruta, SVM-RFE).Reproduces variable selection steps .7. Report software versions and libraries used (e.g., R 4.4.2, Python 3.9, sci kit-learn 1.6.1).Ensures computational compatibilit y.Model Development 8. Specify model types and hyperparameters for each algorithm.Enables exact replication of model t raining.9. Provide random seeds and cross-validation details (k-fold number, stratif ication rules).Guarantees consistent results across runs.10. Publish scripts for calculating AUC, AUPRC, F1, calibration, DCA, an d CIC.Validates reported performance met rics.11. Include bootstrapping or cross-validation statistics (≥ 1 000 resamples). Quantifies statistical uncertainty.12. Share code for SHAP, LIME, or PDP visualizations and subgroup fairn ess analysis. Reproduces interpretability outputs.