{"doi":"10.1093/jamia/ocaf058","title":"Improving clinical decision support through interpretable machine learning and error handling in electronic health records","abstract":"OBJECTIVE: To develop an electronic medical record (EMR) data processing tool that confers clinical context to machine learning (ML) algorithms for error handling, bias mitigation, and interpretability. MATERIALS AND METHODS: We present Trust-MAPS, an algorithm that translates clinical domain knowledge into high-dimensional, mixed-integer programming models that capture physiological and biological constraints on clinical measurements. EMR data are projected onto this constrained space, effectively bringing outliers to fall within a physiologically feasible range. We then compute the distance of each data point from the constrained space modeling healthy physiology to quantify deviation from the norm. These distances, termed \"trust-scores,\" are integrated into the feature space for downstream ML applications. We demonstrate the utility of Trust-MAPS by training a binary classifier for early sepsis prediction on data from the 2019 PhysioNet Computing in Cardiology Challenge, using the XGBoost algorithm and applying SMOTE for overcoming class-imbalance. RESULTS: The Trust-MAPS framework shows desirable behavior in handling potential errors and boosting predictive performance. We achieve an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.89-0.92) for predicting sepsis 6 hours before onset-a marked 15% improvement over a baseline model trained without Trust-MAPS. DISCUSSIONS: Downstream classification performance improves after Trust-MAPS preprocessing, highlighting the bias reducing capabilities of the error-handling projections. Trust-scores emerge as clinically meaningful features that not only boost predictive performance for clinical decision support tasks but also lend interpretability to ML models. CONCLUSION: This work is the first to translate clinical domain knowledge into mathematical constraints, model cross-vital dependencies, and identify aberrations in high-dimensional medical data. Our method allows for error handling in EMR and confers interpretability and superior predictive power to models trained for clinical decision support.","journal":"Journal of the American Medical Informatics Association","year":2025,"id":531354,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9564,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1412073,"name":"Hassan Mortagy","orcid":null,"position":1,"is_corresponding":false},{"id":1411592,"name":"Nathan Dwarshuis","orcid":"0009-0001-9615-0243","position":2,"is_corresponding":false},{"id":988326,"name":"Jeffrey Wang","orcid":"0000-0002-6900-8268","position":3,"is_corresponding":false},{"id":663694,"name":"Philip Yang","orcid":"0000-0001-5142-1137","position":4,"is_corresponding":false},{"id":557991,"name":"Andre L. Holder","orcid":"0000-0003-2635-3923","position":5,"is_corresponding":false},{"id":1411593,"name":"Swati Gupta","orcid":"0000-0002-9566-3856","position":6,"is_corresponding":false},{"id":621071,"name":"Rishikesan Kamaleswaran","orcid":"0000-0001-8366-4811","position":7,"is_corresponding":false},{"id":1411591,"name":"Mehak Arora","orcid":"0000-0003-1225-9549","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:51:14.579161Z","pmid":"40261883","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}