A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients. (15th February 2023)

• Record Type: Journal Article
• Authors: Wu, Zejian (Eric); Xu, Da; Hu, Paul Jen-Hwa; Huang, Ting-Shuo
• Is Part Of: Journal of the American Medical Informatics Association. Volume 30:Number 5(2023)
• Page Start: 846
• Page End: 858
• Publication Date: 2023-02-15
• Subjects: deterioration path predictions -- chronic hepatitis B patients -- deep learning -- graph attention network -- predictive analytics; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocad008
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical for physicians' decisions and patient management. A novel, hierarchical multilabel graph attention-based method aims to predict patient deterioration paths more effectively. Applied to a CHB patient data set, it offers strong predictive utilities and clinical value. Materials and Methods: The proposed method incorporates patients' responses to medications, diagnosis event sequences, and outcome dependencies to estimate deterioration paths. From the electronic health records maintained by a major healthcare organization in Taiwan, we collect clinical data about 177 959 patients diagnosed with hepatitis B virus infection. We use this sample to evaluate the proposed method's predictive efficacy relative to 9 existing methods, as measured by precision, recall, F-measure, and area under the curve (AUC). Results: We use 20% of the sample as holdouts to test each method's prediction performance. The results indicate that our method consistently and significantly outperforms all benchmark methods. It attains the highest AUC, with a 4.8% improvement over the best-performing benchmark, as well as 20.9% and 11.4% improvements in precision and F-measures, respectively. The comparative results demonstrate that our method is more effective for predicting CHB patients' deterioration paths than existing predictive methods. Discussion and Conclusion: The proposed method underscores the value of patient-medication interactions, temporal sequential patterns of distinct diagnosis, and patient outcome dependencies for capturing dynamics that underpin patient deterioration over time. Its efficacious estimates grant physicians a more holistic view of patient progressions and can enhance their clinical decision-making and patient management.