ArticleJMIR human factors2024
Novel Approach to Personalized Physician Recommendations Using Semantic Features and Response Metrics: Model Evaluation Study.
Article in JMIR human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThis study aims to develop and validate a patient-physician hybrid recommendation (PPHR) model with response metrics for better triage performance.
methodsA 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.
resultsThe PPHR model reaches the best recommendation performance when the number of recommended physicians is 14. At this point, the model has an F
conclusionsThe PPHR model uses semantic features and response metrics to enable patients to accurately find the physician who best suits their needs.
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