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This scoping review assesses study characteristics and objectives, identifies model types, and appraises model performance and reporting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We conducted a scoping review, focused on a PubMed and Web of Science search for studies using temporal EHR sequences to identify disease signatures or predict disease presence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We identified 62 studies. Statistical methods, such as testing temporal associations were primarily used for clustering, while deep learning models focused on outcome prediction. Sixty-five percent of studies used secondary care data, with the most common outcomes being disease agnostic (39%) and cardiovascular disease (20%). Forty-eight studies aimed at risk prediction, with 50% comparing trajectory-based models to static baselines. Among 31 studies reporting area under the curve (AUC), temporal models showed moderate performance gains (relative\/absolute AUC: median 5.7%\/4.2%, range \u22122.6% to 58.9%\/\u22122.3% to 33.0%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Trajectory studies are increasing in volume, but lacking in application to primary care datasets, a diverse set of diseases, external validation, and consideration of clinical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>While the field\u2019s nascency hinders firm conclusions, there are promising results across a range of model types and objectives. Continued research from diverse perspectives will help determine whether this growing field can deliver meaningful clinical benefits.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf208","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T13:01:54Z","timestamp":1763038914000},"page":"521-535","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation of trajectory analysis for disease risk assessment: a scoping review"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5015-6732","authenticated-orcid":false,"given":"Freya","family":"Pollington","sequence":"first","affiliation":[{"name":"Epidemiology of Cancer Healthcare and Outcomes (ECHO) Research Group, Department of Behavioural Science and Health, Institute of Epidemiology & Health Care, University College London , London WC1E 7HB,","place":["United 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