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Hybrid collaborative filtering methods for recommending search terms to clinicians.
Ren, Zhiyun; Peng, Bo; Schleyer, Titus K; Ning, Xia.
Afiliación
  • Ren Z; Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH 43210, USA. Electronic address: ren.685@osu.edu.
  • Peng B; Department of Computer Science and Engineering, The Ohio State University, 281 W Lane Ave, Columbus, OH 43210, USA. Electronic address: peng.707@buckeyemail.osu.edu.
  • Schleyer TK; Regenstrief Institute, 1101 W 10th St, Indianapolis, IN 46202, USA; Indiana University School of Medicine, 340 W 10th St #6200, Indianapolis, IN 46202 USA. Electronic address: schleyer@regenstrief.org.
  • Ning X; Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH 43210, USA; Department of Computer Science and Engineering, The Ohio State University, 281 W Lane Ave, Columbus, OH 43210, USA; Translational Data Analytics Institute, The Ohio State University, 1760 Nei
J Biomed Inform ; 113: 103635, 2021 01.
Article en En | MEDLINE | ID: mdl-33307213
With increasing and extensive use of electronic health records (EHR), clinicians are often challenged in retrieving relevant patient information efficiently and effectively to arrive at a diagnosis. While using the search function built into an EHR can be more useful than browsing in a voluminous patient record, it is cumbersome and repetitive to search for the same or similar information on similar patients. To address this challenge, there is a critical need to build effective recommender systems that can recommend search terms to clinicians accurately. In this study, we developed a hybrid collaborative filtering model to recommend search terms for a specific patient to a clinician. The model draws on information from patients' clinical encounters and the searches that were performed during them. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. In one variation of the model (Hybrid Collaborative Filtering Method for Healthcare, or HCFMH), we use only the most recent ICD codes assigned to the patient, and in the other (Co-occurrence Pattern based HCFMH, or cpHCFMH), all ICD codes. We have conducted comprehensive experiments to evaluate the proposed model. These experiments demonstrate that our model outperforms state-of-the-art baseline methods for top-N search term recommendation on different data sets.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Registros Electrónicos de Salud Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: J Biomed Inform Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Registros Electrónicos de Salud Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: J Biomed Inform Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article