cirpublications.com-FPog-1790904315-k105027051 1790904315 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2026 6 1 Jose Martinez Carmen Lopez 2026 10.68159/k105027051 https://cirpublications.com/pub/journal/2/article/k105027051 Cooper WO, Guillamondegui O, Hines OJ, Hultman CS, Kelz RR, Shen P, et al. Use of unsolicited patient observations to identify surgeons with increased risk for postoperative complications. JAMA Surg. 2017;152(6):522-9. Cooper WO, Martinez W, Domenico HJ, Callahan ST, Kirkby BP, Finlayson AJ, et al. Unsolicited patient complaints identify physicians with evidence of neurocognitive disorders. Am J Geriatr Psychiatry. 2018;26(9):927-36. Grandizio LC, Barreto Rocha DF, Hayes D, Warnick EP, Doyle CM, Suk M, et al. An analysis of formal patient complaints, risk, and malpractice events involving orthopedic trauma surgeons during a 10-year period. Orthopedics. 2023;46(2):121-7. Wang Y, Ram S, Scahill S. Risk identification and prediction of complaints and misconduct against health practitioners: a scoping review. Int J Qual Health Care. 2024;36(1):mzad114. Elmessiry A, Cooper WO, Catron TF, Karrass J, Zhang Z, Singh MP. Triaging patient complaints: Monte Carlo cross-validation of six machine learning classifiers. JMIR Med Inform. 2017;5(3):e19. Li X, Shu Q, Kong C, Wang J, Li G, Fang X, et al. An intelligent system for classifying patient complaints using machine learning and natural language processing: development and validation study. J Med Internet Res. 2025;27:e55721. Koh SW, Wong ER, Tan JC, van der Lubbe SC, Goh JC, Ching ES, et al. Classifying patient complaints using artificial intelligence–powered large language models: cross-sectional study. J Med Internet Res. 2025;27:e74231. Khanbhai M, Anyadi P, Symons J, Flott K, Darzi A, Mayer E. Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review. BMJ Health Care Inform. 2021;28(1):e100262. Cronin RM, Fabbri D, Denny JC, Rosenbloom ST, Jackson GP. A comparison of rule-based and machine learning approaches for classifying patient portal messages. Int J Med Inform. 2017;105:110-20. Sulieman L, Gilmore D, French C, Cronin RM, Jackson GP, Russell M, et al. Classifying patient portal messages using convolutional neural networks. J Biomed Inform. 2017;74:59-70. Tafti AP. Probing patient messages enhanced by natural language processing: a top-down message corpus analysis. Health Data Sci. 2021;2021:9876543. Ren Y, Wu Y, Fan JW, Khurana A, Fu S, Wu D, et al. Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models. J Am Med Inform Assoc. 2024;31(8):1714-24. Bari V, Hirsch JS, Narvaez J, Sardinia R, Bock KR, Oppenheim MI, et al. An approach to predicting patient experience through machine learning and social network analysis. J Am Med Inform Assoc. 2020;27(12):1834-43. Liu N, Kumara S, Reich E. Gaining insights into patient satisfaction through interpretable machine learning. IEEE J Biomed Health Inform. 2020;25(6):2215-26. Matsuda S, Ohtomo T, Okuyama M, Miyake H, Aoki K. Estimating patient satisfaction through a language processing model: model development and evaluation. JMIR Form Res. 2023;7:e48534. Khaleghparast S, Maleki M, Hajianfar G, Soumari E, Oveisi M, Golandouz HM, et al. Development of a patients' satisfaction analysis system using machine learning and lexicon-based methods. BMC Health Serv Res. 2023;23(1):280. Rahim AI, Ibrahim MI, Musa KI, Chua SL, Yaacob NM, et al. Assessing patient-perceived hospital service quality and sentiment in Malaysian public hospitals using machine learning and Facebook reviews. Int J Environ Res Public Health. 2021;18(18):9912. Cooper WO, Spain DA, Guillamondegui O, Kelz RR, Domenico HJ, Hopkins J, et al. Association of coworker reports about unprofessional behavior by surgeons with surgical complications in their patients. JAMA Surg. 2019;154(9):828-34. Feizollah A, Lin CY, O’Malley L, Thompson W, Listl S, Byrne M, et al. The use of natural language processing to interpret unstructured patient feedback on health services: scoping review. J Med Internet Res. 2025;27:e72853. Shankar R, Yip A. Transforming patient feedback into actionable insights through natural language processing: knowledge discovery and action research study. JMIR Form Res. 2025;9:e69699. Azarpey A, Thomas J, Ring D, Franko O. Natural language processing of sentiments identified in patient comments associated with less than top-rated care. J Patient Exp. 2025;12:23743735251323677. Fairie P, Zhang Z, D’Souza AG, Walsh T, Quan H, Santana MJ, et al. Categorising patient concerns using natural language processing techniques. BMJ Health Care Inform. 2021;28(1):e100274. Aattouri I, Mouncif H, Rida M. Call center customer sentiment analysis using ML and NLP. In: 2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE; 2023. Habib M, Faris M, Qaddoura R, Alomari M, Alomari A, Faris H, et al. Toward an automatic quality assessment of voice-based telemedicine consultations: a deep learning approach. Sensors. 2021;21(9):3279. Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform. 2022;23(2):bbab569. Krones F, Marikkar U, Parsons G, Szmul A, Mahdi A. Review of multimodal machine learning approaches in healthcare. Inf Fusion. 2025;114:102690. Teles AS, de Moura IR, Silva F, Roberts A, Stahl D. EHR-based prediction modelling meets multimodal deep learning: a systematic review of structured and textual data fusion methods. Inf Fusion. 2025;118:102981.