{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:40:09Z","timestamp":1746232809114,"version":"3.40.4"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T00:00:00Z","timestamp":1714694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"MIT Abdul Latif Jameel Clinic for Machine Learning in Health"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2205320"],"award-info":[{"award-number":["IIS-2205320"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Machine Learning Core"},{"DOI":"10.13039\/100005615","name":"Beth Israel Deaconess Medical Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005615","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MIT Deshpande Center"},{"name":"MachineLearningApplications@CSAIL initiative"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae092","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T19:33:47Z","timestamp":1714073627000},"page":"1578-1582","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing"],"prefix":"10.1093","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5039-0655","authenticated-orcid":false,"given":"Sharon","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology , Cambridge, MA 02139,","place":["United States"]},{"name":"Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology , Cambridge, MA 02139,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5585-3823","authenticated-orcid":false,"given":"Barbara D","family":"Lam","sequence":"additional","affiliation":[{"name":"Division of Hematology and Oncology, Department of Medicine, Beth Israel Deaconess Medical Center , Boston, MA 02215,","place":["United States"]},{"name":"Division of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center , Boston, MA 02215,","place":["United 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