An intelligent medical guidance and recommendation model driven by patient-physician communication data

Liu, Jusheng; Li, Chaoran; Huang, Yulin; Han, Jingti

doi:10.3389/fpubh.2023.1098206

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…Therefore, using professional descriptions to create doctor profiles does not semantically match well with patient's questions. Some studies have attempted to extract doctor features using textual questions from patients [5,11,12].…”

Section: Challenges and Solutions For Online Triage Systemsmentioning

confidence: 99%

“…For instance, Ju and Zhang [11] integrated geographical location and patients' questions to generate personalized recommendations. Liu et al [12] proposed a recommended doctor model that considers characteristics of patients and doctors. Lu et al [5] proposed a self-adaptive doctor recommendation system that considered doctor activity and patient feedback.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng,

Cai,

Yan

et al. 2024

JMIR Hum Factors

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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 F1-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 F1-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.

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Section: Challenges and Solutions For Online Triage Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zheng,

Cai,

Yan

et al. 2024

JMIR Hum Factors

View full text Add to dashboard Cite

show abstract

“…Reciprocal recommendation is to provide recommendations based on the common preferences of both users [25]. Reciprocal recommendations have been successfully used in various domains, including online dating systems [26], online mentoring systems [26], and online recruitment systems [27]. Reciprocity-based job recommendation implies that the recommended job not only aligns with the preferences of the job seeker but also meets the requirements and preferences of the recruiter [12,28].…”

Section: Research On Job Recommendations Based On Reciprocitymentioning

confidence: 99%

A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking

Yao,

Xu,

Gao

2023

Applied Sciences

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With the surge in college graduate numbers, a disparity has emerged where the supply of jobs falls short of demand, intensifying employment pressures annually. College graduates, due to their lack of historical employment data compared with job seekers in the broader society, encounter a ‘cold start’ issue in the job recommendation process. Additionally, the nature of job recommendations, which differs fundamentally from unilateral recommendations, requires consideration of reciprocity between both parties involved. This article introduces a new approach to job recommendations using college graduates as the object of study. In the screening stage, a semantic keyword iterative algorithm is applied to compute the similarity between the resume and recruitment texts. This algorithm enhances the intersectionality of keywords in the calculation process, maximizing the utilization of resume information to enhance the accuracy of text similarity calculations. The ranking phase utilizes in-school data to build a social network between college graduates and graduated students and solves the system’s cold-start problem using the social network to recommend jobs for college graduates where graduated students are employed. We introduce a dual-dimensional matching approach that incorporates both specialty and salary, building upon the amalgamated semantic keyword iterative algorithm and the social network job recommendation method, to enhance the reciprocity of job recommendations. The job recommendation method introduced herein outperforms other methods in terms of the average satisfaction rate (AR) and normalized discounted cumulative gain (NDCG), thereby confirming its superior ability to meet the job-seeking preferences of graduates and the recruitment criteria of employers. This job recommendation method offers effective assistance to graduates lacking employment experience and historical employment data, facilitating their search for more suitable job opportunities.

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Research on resource allocation methods for traditional Chinese medicine services based on deep reinforcement learning

Ma,

Fang,

et al. 2024

Neural Comput & Applic

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An intelligent medical guidance and recommendation model driven by patient-physician communication data

Cited by 8 publications

References 37 publications

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

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

A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking

Research on resource allocation methods for traditional Chinese medicine services based on deep reinforcement learning

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