{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-readmission-risk-from-doctors","title":"Predicting readmission risk from doctors' notes","arxiv_id":"1711.10663","date":"2017-11-29","proceeding":null,"authors":["Erin Craig","Carlos Arias","David Gillman"],"abstract":"We develop a model using deep learning techniques and natural language\nprocessing on unstructured text from medical records to predict hospital-wide\n$30$-day unplanned readmission, with c-statistic $.70$. Our model is\nconstructed to allow physicians to interpret the significant features for\nprediction.","url_abs":"http://arxiv.org/abs/1711.10663v2","url_pdf":"http://arxiv.org/pdf/1711.10663v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-readmission-risk-from-doctors","repo_url":"https://github.com/farinstitute/ReadmissionRiskDoctorNotes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}