{"doi":"10.1101/2020.07.15.20154864","title":"Dense phenotyping from electronic health records enables machine-learning-based prediction of preterm birth","abstract":"Abstract Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy. Here, we apply machine learning to diverse data from EHRs to predict singleton preterm birth. Leveraging a large cohort of 35,282 deliveries, we find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC=0.75, PR-AUC=0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC=0.65, PR-AUC=0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth sub-types enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth sub-types (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5,978 deliveries) from a different healthcare system. By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy.","journal":"medRxiv","year":2020,"id":120325,"datarank":0.37273599746820013,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.0,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9344,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":482706,"name":"Brian L. Le","orcid":"0009-0000-4062-094X","position":1,"is_corresponding":false},{"id":52431,"name":"Idit Kosti","orcid":"0000-0003-2212-5472","position":2,"is_corresponding":false},{"id":11369,"name":"Péter Straub","orcid":"0009-0009-9693-8207","position":3,"is_corresponding":false},{"id":558236,"name":"Digna R. Velez-Edwards","orcid":null,"position":4,"is_corresponding":false},{"id":11243,"name":"Lea K. Davis","orcid":"0000-0001-5143-2282","position":5,"is_corresponding":false},{"id":557468,"name":"J.M. Newton","orcid":"0000-0001-6013-851X","position":6,"is_corresponding":false},{"id":40923,"name":"Louis J. Muglia","orcid":"0000-0002-0301-8770","position":7,"is_corresponding":false},{"id":40922,"name":"Antonis Rokas","orcid":"0000-0002-7248-6551","position":8,"is_corresponding":false},{"id":373246,"name":"Cosmin A. Bejan","orcid":"0000-0001-5107-0584","position":9,"is_corresponding":false},{"id":2824,"name":"Marina Sirota","orcid":"0000-0002-7246-6083","position":10,"is_corresponding":false},{"id":258343,"name":"John A. Capra","orcid":"0000-0001-9743-1795","position":11,"is_corresponding":false},{"id":413519,"name":"Abin Abraham","orcid":"0000-0002-9951-2879","position":0,"is_corresponding":true}],"reference_count":88,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:14:33.640077Z","pmid":null,"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":[]}