OBJECTIVES: Prediction models are increasingly used in healthcare for risk stratification and personalized care. Many models are developed using machine learning, which requires tuning hyperparameters to maximize performance based on a chosen loss function metric. In healthcare, the area under the receiver operating characteristic curve (AUROC) is commonly used for this purpose, but it may not always be the most appropriate choice for every clinical application. We empirically characterize whether the choice of loss function metric in hyperparameter optimization leads to systematic differences in model behavior across several clinical prediction tasks using real-world healthcare data. METHODS: We utilized fifteen different loss function metrics to guide hyperparameter selection across three clinical prediction tasks and four machine learning algorithms. We then compared how loss function metric choice affected selected hyperparameters, overall performance, and individual predicted probabilities. RESULTS: We observed that certain hyperparameters tended to have similar optimal values across different loss function metrics, although this pattern differed by algorithm. The best-performing models, evaluated using AUROC, were often not the models with hyperparameters optimized using AUROC. While models performed similarly at a population level, based on discrimination and calibration. The choice of the loss function metric had significant impact on the individual predicted risk for a patient. DISCUSSION: The predictive multiplicity observed can have significant impact on the patient level, while not observed in the population level model evaluation. CONCLUSION: Predictive multiplicity can have a serious impact on patient treatment decisions but is not yet well understood.

OBJECTIVE: We propose Heterogeneity-aware Collaborative One-shot Lossless Algorithm for Generalized Linear Model (COLA-GLM-H), a novel one-shot lossless distributed algorithm that enables the integration of heterogeneous multi-institutional data while relying solely on instituion-level summary information rather than patient-level data. MATERIALS AND METHODS: Generalized Linear Models (GLMs) are widely used in medical research for analyzing diverse outcome types. In multi-institution settings, we demonstrated that the global likelihood can be reconstructed using only institution-level summary statistics, enabling lossless estimation without accessing individual records. We validated COLA-GLM-H in two real-world studies: (1) an emulated U.S. pediatric centralized network (719,383 patients) evaluating long-term cardiovascular risks following COVID-19, and (2) an internationally decentralized network of 120,429 hospitalized patients from seven databases across three countries assessing risk factors for COVID-19 mortality. RESULTS: In the centralized network, COLA-GLM-H produced estimates identical to those from pooled analyses. In the decentralized setting, the algorithm effectively integrated heterogeneous data across multiple clinical institutions using a single communication round. CONCLUSIONS: COLA-GLM-H provides a lossless, communication-efficient, and computation-efficient solution for multi-institutional research using only institution-level summary data. It accounts for between-institution heterogeneity and supports all outcome types within the exponential family, enabling secure, scalable, and accurate analysis in collaborative clinical research.

OBJECTIVE: This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation. MATERIALS AND METHODS: We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases. We use a train-test split of 75%/25% and evaluate performance with discrimination and calibration. Statistical analysis for difference in performance uses Friedman's test and critical difference diagrams. RESULTS: Of the 840 models we develop, L1 and ElasticNet emerge as superior in both internal and external discrimination, with a notable AUC difference. BAR and IHT show the best internal calibration, without a clear external calibration leader. ElasticNet typically has larger model sizes than L1. Methods like IHT and BAR, while slightly less discriminative, significantly reduce model complexity. CONCLUSION: L1 and ElasticNet offer the best discriminative performance in logistic regression for healthcare predictions, maintaining robustness across validations. For simpler, more interpretable models, L0-based methods (IHT and BAR) are advantageous, providing greater parsimony and calibration with fewer features. This study aids in selecting suitable regularization techniques for healthcare prediction models, balancing performance, complexity, and interpretability.

OBJECTIVE: This systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether using text data in addition to more commonly used structured data improves the prediction performance. MATERIALS AND METHODS: We searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic prediction models using information extracted from unstructured text in a data-driven manner, published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models. RESULTS: We identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared with using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and attention for the explainability of the developed models were limited. CONCLUSION: The use of unstructured text in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The text data are source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.

OBJECTIVES: This systematic review aims to provide further insights into the conduct and reporting of clinical prediction model development and validation over time. We focus on assessing the reporting of information necessary to enable external validation by other investigators. MATERIALS AND METHODS: We searched Embase, Medline, Web-of-Science, Cochrane Library, and Google Scholar to identify studies that developed 1 or more multivariable prognostic prediction models using electronic health record (EHR) data published in the period 2009-2019. RESULTS: We identified 422 studies that developed a total of 579 clinical prediction models using EHR data. We observed a steep increase over the years in the number of developed models. The percentage of models externally validated in the same paper remained at around 10%. Throughout 2009-2019, for both the target population and the outcome definitions, code lists were provided for less than 20% of the models. For about half of the models that were developed using regression analysis, the final model was not completely presented. DISCUSSION: Overall, we observed limited improvement over time in the conduct and reporting of clinical prediction model development and validation. In particular, the prediction problem definition was often not clearly reported, and the final model was often not completely presented. CONCLUSION: Improvement in the reporting of information necessary to enable external validation by other investigators is still urgently needed to increase clinical adoption of developed models.