Novel Approach to Personalized Physician Recommendations Using Semantic Features and Response Metrics: Model Evaluation Study.

Autor: Zheng Y; Biomedical Big Data Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen City, China., Cai Y; Meteorological Disaster Prevention Technology Center, Xiamen Meteorological Bureau, Xiamen City, China., Yan Y; Biomedical Big Data Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen City, China., Chen S; Meteorological Disaster Prevention Technology Center, Xiamen Meteorological Bureau, Xiamen City, China., Gong K; Biomedical Big Data Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen City, China.
Jazyk: angličtina
Zdroj: JMIR human factors [JMIR Hum Factors] 2024 Aug 15; Vol. 11, pp. e57670. Date of Electronic Publication: 2024 Aug 15.
DOI: 10.2196/57670
Abstrakt: Background: The rapid growth of web-based medical services has highlighted the significance of smart triage systems in helping patients find the most appropriate physicians. However, traditional triage methods often rely on department recommendations and are insufficient to accurately match patients' textual questions with physicians' specialties. Therefore, there is an urgent need to develop algorithms for recommending physicians.
Objective: This study aims to develop and validate a patient-physician hybrid recommendation (PPHR) model with response metrics for better triage performance.
Methods: A total of 646,383 web-based medical consultation records from the Internet Hospital of the First Affiliated Hospital of Xiamen University were collected. Semantic features representing patients and physicians were developed to identify the set of most similar questions and semantically expand the pool of recommended physician candidates, respectively. The physicians' response rate feature was designed to improve candidate rankings. These 3 characteristics combine to create the PPHR model. Overall, 5 physicians participated in the evaluation of the efficiency of the PPHR model through multiple metrics and questionnaires as well as the performance of Sentence Bidirectional Encoder Representations from Transformers and Doc2Vec in text embedding.
Results: The PPHR model reaches the best recommendation performance when the number of recommended physicians is 14. At this point, the model has an F 1 -score of 76.25%, a proportion of high-quality services of 41.05%, and a rating of 3.90. After removing physicians' characteristics and response rates from the PPHR model, the F 1 -score decreased by 12.05%, the proportion of high-quality services fell by 10.87%, the average hit ratio dropped by 1.06%, and the rating declined by 11.43%. According to whether those 5 physicians were recommended by the PPHR model, Sentence Bidirectional Encoder Representations from Transformers achieved an average hit ratio of 88.6%, while Doc2Vec achieved an average hit ratio of 53.4%.
Conclusions: The PPHR model uses semantic features and response metrics to enable patients to accurately find the physician who best suits their needs.
(©Yingbin Zheng, Yunping Cai, Yiwei Yan, Sai Chen, Kai Gong. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 15.08.2024.)
Databáze: MEDLINE
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