{"doi":"10.1093/jamia/ocad081","title":"Automated identification of eviction status from electronic health record notes","abstract":"OBJECTIVE: Evictions are important social and behavioral determinants of health. Evictions are associated with a cascade of negative events that can lead to unemployment, housing insecurity/homelessness, long-term poverty, and mental health problems. In this study, we developed a natural language processing system to automatically detect eviction status from electronic health record (EHR) notes. MATERIALS AND METHODS: We first defined eviction status (eviction presence and eviction period) and then annotated eviction status in 5000 EHR notes from the Veterans Health Administration (VHA). We developed a novel model, KIRESH, that has shown to substantially outperform other state-of-the-art models such as fine-tuning pretrained language models like BioBERT and Bio_ClinicalBERT. Moreover, we designed a novel prompt to further improve the model performance by using the intrinsic connection between the 2 subtasks of eviction presence and period prediction. Finally, we used the Temperature Scaling-based Calibration on our KIRESH-Prompt method to avoid overconfidence issues arising from the imbalance dataset. RESULTS: KIRESH-Prompt substantially outperformed strong baseline models including fine-tuning the Bio_ClinicalBERT model to achieve 0.74672 MCC, 0.71153 Macro-F1, and 0.83396 Micro-F1 in predicting eviction period and 0.66827 MCC, 0.62734 Macro-F1, and 0.7863 Micro-F1 in predicting eviction presence. We also conducted additional experiments on a benchmark social determinants of health (SBDH) dataset to demonstrate the generalizability of our methods. CONCLUSION AND FUTURE WORK: KIRESH-Prompt has substantially improved eviction status classification. We plan to deploy KIRESH-Prompt to the VHA EHRs as an eviction surveillance system to help address the US Veterans' housing insecurity.","journal":"Journal of the American Medical Informatics Association","year":2023,"id":338402,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9312,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":335,"name":"Jack Tsai","orcid":"0000-0002-0329-648X","position":1,"is_corresponding":false},{"id":674146,"name":"Weisong Liu","orcid":"0000-0003-3825-5597","position":2,"is_corresponding":false},{"id":984575,"name":"David A. Levy","orcid":"0009-0000-1530-2309","position":3,"is_corresponding":false},{"id":1070837,"name":"Emily Druhl","orcid":"0000-0003-3547-5359","position":4,"is_corresponding":false},{"id":397820,"name":"Joel I. Reisman","orcid":"0000-0002-0552-161X","position":5,"is_corresponding":false},{"id":1070838,"name":"Hong Yu","orcid":"0000-0003-0667-8413","position":6,"is_corresponding":false},{"id":984573,"name":"Zonghai Yao","orcid":"0000-0002-5707-8410","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-19T01:10:35.973121Z","pmid":"37203429","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":[]}