{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T07:26:16Z","timestamp":1767597976650,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>Most of the existing ubiquitous clinic recommendation (UCR) systems adopt linear mechanisms to aggregate the attribute-level performances of a clinic to evaluate the overall performance. However, such linear mechanisms may not be able to explain the choices of all patients. To solve this problem, the modified mixed binary nonlinear programming (MMBNLP)\u2013feedforward neural network (FNN) approach is proposed in this study. In the proposed methodology, first, the existing MBNLP model is modified to improve the successful recommendation rate using a linear recommendation mechanism. Subsequently, an FNN is constructed to fit the relationship between the attribute-level performances of a clinic and its overall performance, thereby providing possible ways to further enhance the recommendation performance. The results of a regional experiment showed that the MMBNLP\u2013FNN approach improved the successful recommendation rate by 30%.<\/jats:p>","DOI":"10.3390\/jtaer16070178","type":"journal-article","created":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T04:01:28Z","timestamp":1637812888000},"page":"3282-3298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Ubiquitous Clinic Recommendation System Using the Modified Mixed-Binary Nonlinear Programming-Feedforward Neural Network Approach"],"prefix":"10.3390","volume":"16","author":[{"given":"Yu-Cheng","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Computer-Aided Industrial Design, Overseas Chinese University, Taichung 40721, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toly","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300093, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1007\/s10916-016-0646-7","article-title":"Model of Current Practice Regarding Prescriptions of Controlled Substances and the Perceived Benefits of E-Prescribing in an Academic Medical Center","volume":"40","author":"Kupperman","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1007\/s10916-017-0776-6","article-title":"A Systematic Review and Taxonomy of Published Quality Criteria Related to the Evaluation of User-Facing eHealth Programs","volume":"41","author":"Baumel","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.compind.2017.01.003","article-title":"Wearables data integration: Data-driven modeling to adjust for differences in Jawbone and Fitbit estimations of steps, calories, and resting heart-rate","volume":"86","author":"Shah","year":"2017","journal-title":"Comput. Ind."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s10916-018-0943-4","article-title":"Systematic Review of Real-time Remote Health Monitoring System in Triage and Priority-Based Sensor Technology: Taxonomy, Open Challenges, Motivation and Recommendations","volume":"42","author":"Albahri","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s11042-011-0919-6","article-title":"Ontology-based healthcare context information model to implement ubiquitous environment","volume":"71","author":"Kim","year":"2011","journal-title":"Multimed. Tools Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10916-016-0469-6","article-title":"Ubiquitous Multicriteria Clinic Recommendation System","volume":"40","author":"Chen","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e134","DOI":"10.2196\/jmir.6747","article-title":"Web-Based Medical Appointment Systems: A Systematic Review","volume":"19","author":"Zhao","year":"2017","journal-title":"J. Med. Internet Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s10729-019-09473-5","article-title":"Assessing sustainable effectiveness of the adjustment mechanism of a ubiquitous clinic recommendation system","volume":"23","author":"Chiu","year":"2019","journal-title":"Health Care Manag. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.urology.2020.04.079","article-title":"Preserving operational capability while building capacity during the COVID-19 pandemic: A tertiary urology centre\u2019s experience","volume":"142","author":"Tan","year":"2020","journal-title":"Urology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/S2213-8587(20)30156-X","article-title":"COVID-19 and obesity\u2014lack of clarity, guidance, and implications for care","volume":"8","author":"Flint","year":"2020","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.elerap.2017.04.003","article-title":"Ubiquitous clinic recommendation by predicting a patient\u2019s preferences","volume":"23","author":"Chen","year":"2017","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.cie.2019.05.009","article-title":"A classifying ubiquitous clinic recommendation approach for forming patient groups and recommending suitable clinics","volume":"133","author":"Chen","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1561\/1100000009","article-title":"Collaborative filtering recommender systems","volume":"4","author":"Ekstrand","year":"2011","journal-title":"Found. Trends Hum.-Comput. Interact."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1177\/0272989X09357474","article-title":"Choosing a Hospital for Surgery: The Importance of Information on Quality of Care","volume":"30","author":"Otten","year":"2010","journal-title":"Med. Decis. Mak."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zwijnenberg, N.C., Damman, O.C., Spreeuwenberg, P., Hendriks, M., and Rademakers, J.J. (2011). Different patient subgroup, different ranking? Which quality indicators do patients find important when choosing a hospital for hip- or knee arthroplasty?. BMC Health Serv. Res., 11.","DOI":"10.1186\/1472-6963-11-299"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1093\/intqhc\/mzp006","article-title":"Patient satisfaction with and recommendation of a primary care provider: Associations of perceived quality and patient education","volume":"21","author":"Tung","year":"2009","journal-title":"Int. J. Qual. Health Care"},{"key":"ref_17","unstructured":"Simanski, C., Lefering, R., Paffrath, T., Riess, P., Y\u00fccel, N., Maegele, M., Th\u00fcsing, C., and Neugebauer, E. (2006). Postoperative pain relief is an important factor for the patients\u2019 selection of a clinic. Results of an Anonymous Survey, Schmerz."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"271","DOI":"10.5392\/JKCA.2013.13.03.271","article-title":"Analysis of Selection Criteria of Consumers for Dental Clinic","volume":"13","author":"Bae","year":"2013","journal-title":"J. Korea Contents Assoc."},{"key":"ref_19","first-page":"15","article-title":"Study on selection factor in choosing dental clinic","volume":"31","author":"Lee","year":"2009","journal-title":"J. Korean Acad. Dent. Technol."},{"key":"ref_20","first-page":"22","article-title":"Selection of medical clinic for disease diagnosis by using TOPSIS method","volume":"61","author":"Zulqarnain","year":"2020","journal-title":"Int. J. Pharm. Sci. Rev. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100517","DOI":"10.1016\/j.hlpt.2021.100517","article-title":"A FAHP-FTOPSIS approach for choosing mid-term occupational healthcare measures amid the COVID-19 pandemic","volume":"10","author":"Chen","year":"2021","journal-title":"Health Policy Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.eswa.2006.10.020","article-title":"The exploration of consumers\u2019 behavior in choosing hospital by the application of neural network","volume":"34","author":"Lee","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_23","first-page":"64","article-title":"Choosing between hospitals: The influence of the experiences of other patients","volume":"32","author":"Otten","year":"2012","journal-title":"Med. Decis. Mak."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1108\/IJHCQA-06-2013-0073","article-title":"An empirical study on hospital selection in India","volume":"28","author":"Ghosh","year":"2015","journal-title":"Int. J. Health Care Qual. Assur."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6353","DOI":"10.3233\/JIFS-181172","article-title":"A novel hesitant fuzzy EDAS method and its application to hospital selection","volume":"35","author":"Kahraman","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_26","first-page":"32","article-title":"Influence of Social Media on the Patients for Choosing the Dental Clinic- A Cross-sectional Survey","volume":"14","author":"Alkadhi","year":"2020","journal-title":"J. Clin. Diagn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Khoie, M.R., Tabrizi, T.S., Khorasani, E.S., Rahimi, S., and Marhamati, N. (2017). A Hospital Recommendation System Based on Patient Satisfaction Survey. Appl. Sci., 7.","DOI":"10.3390\/app7100966"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"217","DOI":"10.17509\/ijost.v5i2.24585","article-title":"A Neural Network Aided Real-Time Hospital Recommendation System","volume":"5","author":"Paranjay","year":"2020","journal-title":"Indones. J. Sci. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chao, H., Cao, Y., Zhang, J., Xia, F., Zhou, Y., and Shan, H. (2018, January 15\u201317). Population Density-Based Hospital Recommendation with Mobile LBS Big Data. Proceedings of the 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China.","DOI":"10.1109\/BigComp.2018.00015"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tawarmalani, M., and Sahinidis, N.V. (2002). Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications, Kluwer Academic Publishers.","DOI":"10.1007\/978-1-4757-3532-1"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s10729-018-9441-y","article-title":"Mining the preferences of patients for ubiquitous clinic recommendation","volume":"23","author":"Chen","year":"2018","journal-title":"Health Care Manag. Sci."},{"key":"ref_32","unstructured":"Kulkarni, A., Vishwanath, A., and Shah, C. (2021, September 08). Implementing a Real-Time, AI-Based, Face Mask Detector Application for COVID-19. Available online: https:\/\/developer.nvidia.com\/blog\/implementing-a-real-time-ai-based-face-mask-detector-application-for-covid-19\/."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e20334","DOI":"10.2196\/20334","article-title":"Features and Functionalities of Smartphone Apps Related to COVID-19: Systematic Search in App Stores and Content Analysis","volume":"22","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s12652-014-0251-x","article-title":"CART\u2013BPN approach for estimating cycle time in wafer fabrication","volume":"6","author":"Wu","year":"2014","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.fcij.2017.05.001","article-title":"Forecasting of nonlinear time series using ANN","volume":"2","author":"Tealab","year":"2017","journal-title":"Futur. Comput. Inform. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1007\/s00170-008-1665-4","article-title":"Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM\u2013FBPN-ensemble approach with multiple buckets and partial normalization","volume":"42","author":"Chen","year":"2009","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nocedal, J., and Wright, S.J. (1999). Numerical Optimization, Springer.","DOI":"10.1007\/b98874"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, Y.-C., Tsai, H.-R., and Chen, T. (2021). A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication. Mathematics, 9.","DOI":"10.3390\/math9121430"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rcim.2015.09.011","article-title":"Estimating simulation workload in cloud manufacturing using a classifying artificial neural network ensemble approach","volume":"38","author":"Chen","year":"2016","journal-title":"Robot. Comput. Manuf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100010","DOI":"10.1016\/j.dajour.2021.100010","article-title":"A fuzzy deep predictive analytics approach for enhancing cycle time range estimation precision in wafer fabrication","volume":"1","author":"Wang","year":"2021","journal-title":"Decis. Anal. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"64270","DOI":"10.1109\/ACCESS.2018.2877890","article-title":"Benchmark Analysis of Representative Deep Neural Network Architectures","volume":"6","author":"Bianco","year":"2018","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1108\/RPJ-12-2016-0209","article-title":"Multilayer fuzzy neural network for modeling a multisource uncertain unit-cost learning process in wafer fabrication","volume":"24","author":"Chen","year":"2018","journal-title":"Rapid Prototyp. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1080\/15472450.2020.1713772","article-title":"Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory","volume":"25","author":"Tang","year":"2020","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s12652-015-0340-5","article-title":"Fuzzy neural network approach to optimizing process performance by using multiple responses","volume":"7","author":"Chen","year":"2016","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9321","DOI":"10.1007\/s00500-020-04891-y","article-title":"An intelligent personalized web blog searching technique using fuzzy-based feedback recurrent neural network","volume":"24","author":"Khatter","year":"2020","journal-title":"Soft Comput."}],"container-title":["Journal of Theoretical and Applied Electronic Commerce Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/0718-1876\/16\/7\/178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:10Z","timestamp":1760168110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/0718-1876\/16\/7\/178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,24]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["jtaer16070178"],"URL":"https:\/\/doi.org\/10.3390\/jtaer16070178","relation":{},"ISSN":["0718-1876"],"issn-type":[{"type":"electronic","value":"0718-1876"}],"subject":[],"published":{"date-parts":[[2021,11,24]]}}}