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Jiang H, Mi Z, Xu W. Online Medical Consultation Service-Oriented Recommendations: Systematic Review. J Med Internet Res 2024; 26:e46073. [PMID: 38777810 PMCID: PMC11322685 DOI: 10.2196/46073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 12/22/2023] [Accepted: 05/21/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Online health communities have given rise to a new e-service known as online medical consultations (OMCs), enabling remote interactions between physicians and patients. To address challenges, such as patient information overload and uneven distribution of physician visits, online health communities should develop OMC-oriented recommenders. OBJECTIVE We aimed to comprehensively investigate what paradigms lead to the success of OMC-oriented recommendations. METHODS A literature search was conducted through e-databases, including PubMed, ACM Digital Library, Springer, and ScienceDirect, from January 2011 to December 2023. This review included all papers directly and indirectly related to the topic of health care-related recommendations for online services. RESULTS The search identified 611 articles, of which 26 (4.3%) met the inclusion criteria. Despite the growing academic interest in OMC recommendations, there remains a lack of consensus among researchers on the definition of e-service-oriented recommenders. The discussion highlighted 3 key factors influencing recommender success: features, algorithms, and metrics. It advocated for moving beyond traditional e-commerce-oriented recommenders to establish an innovative theoretical framework for e-service-oriented recommenders and addresses critical technical issues regarding 2-sided personalized recommendations. CONCLUSIONS This review underscores the essence of e-services, particularly in knowledge- and labor-intensive domains such as OMCs, where patients seek interpretable recommendations due to their lack of domain knowledge and physicians must balance their energy levels to avoid overworking. Our study's findings shed light on the importance of customizing e-service-oriented personalized recommendations to meet the distinct expectations of 2-sided users considering their cognitive abilities, decision-making perspectives, and preferences. To achieve this, a paradigm shift is essential to develop unique attributes and explore distinct content tailored for both parties involved.
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Affiliation(s)
- Hongxun Jiang
- School of Information, Renmin University of China, Beijing, China
| | - Ziyue Mi
- School of Information, Renmin University of China, Beijing, China
| | - Wei Xu
- School of Information, Renmin University of China, Beijing, China
- School of Smart Governance, Renmin University of China, Suzhou, China
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Shah AM, Lee KY, Hidayat A, Falchook A, Muhammad W. A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset. Int J Med Inform 2024; 184:105375. [PMID: 38367390 DOI: 10.1016/j.ijmedinf.2024.105375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 01/25/2024] [Accepted: 02/07/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND Online cancer forums (OCF) are increasingly popular platforms for patients and caregivers to discuss, seek information on, and share opinions about diseases and treatments. This interaction generates a substantial amount of unstructured text data, necessitating deeper exploration. Using time series data, our study exploits topic modeling in the novel domain of online cancer forums (OCFs) to identify meaningful topics and changing dynamics of online discussion across different lung cancer treatment intent groups. METHODS For this purpose, a dataset comprising 27,998 forum posts about lung cancer was collected from three OCFs: lungcancer.net, lungevity.org, and reddit.com, spanning the years 2016 to 2018. RESULTS The analysis reflects the public discussion on multi-intent lung cancer treatment over time, taking into account seasonal variations. Discussions on cancer symptoms and prevention garnered the most attention, dominating both curative and palliative care discussions. There were distinct seasonal peaks: curative care topics surged from winter to late spring, while palliative care topics peaked from late summer to mid-autumn. Keyword analysis highlighted that lung cancer diagnosis and treatment were primary topics, whereas cancer prevention and treatment outcomes were predominant across multi-care contexts. For the study period, curative care discussions predominantly revolved around informational support and disease syndromes. In contrast, social support and cancer prevention prevailed in the palliative care context. Notably, topics such as cancer screening and cancer treatment exhibit pronounced seasonal variations in curative care, peaking in frequency during the summers (May to August) of the study period. Meanwhile, the topic of tumor control within palliative care showed significant seasonal influence during the winters and summers of 2017 and 2018. CONCLUSION Our text analysis approach using OCF data shows potential for computational methods in this novel domain to gain insights into trends in public cancer communication and seasonal variations for a better understanding of improving personalized care, decision support, treatment outcomes, and quality of life.
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Affiliation(s)
- Adnan Muhammad Shah
- Chair of Marketing and Innovation, University of Hamburg, 20146, Germany; Department of Physics, Charles E. Schmidt College of Science, Florida Atlantic University, FL 33431-0991, United States; Department of Computer Engineering, Gachon University, Seoul 13120. Republic of Korea.
| | - Kang Yoon Lee
- Department of Computer Engineering, Gachon University, Seoul 13120. Republic of Korea.
| | - Abdullah Hidayat
- Department of Physics, Charles E. Schmidt College of Science, Florida Atlantic University, FL 33431-0991, United States.
| | - Aaron Falchook
- Department of Radiation Oncology, Memorial Hospital West, Memorial Cancer Institute (MCI), Pembroke Pines, FL, United States.
| | - Wazir Muhammad
- Department of Physics, Charles E. Schmidt College of Science, Florida Atlantic University, FL 33431-0991, United States.
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Gomes MAS, Kovaleski JL, Pagani RN, da Silva VL, Pasquini TCDS. Transforming healthcare with big data analytics: technologies, techniques and prospects. J Med Eng Technol 2023; 47:1-11. [PMID: 35852400 DOI: 10.1080/03091902.2022.2096133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In different studies in the field of healthcare, big data analytics technology has been shown to be effective in observing the behaviour of data, of which analysed to allow the discovery of relevant insights for strategy and decision making. The objective of this study is to present the results of a systematic review of the literature on big data analytics in healthcare, focussing in technologies, main areas and purposes of adoption. To reach its objective, the study conducts an exploratory research, through a systematic review of the literature, using the Methodi Ordinatio protocol supported by content analysis. The results reveal that the use of tools implies work performance at the clinical and managerial level, improving the cost-benefit ratio and reducing the time factor in the practice of the workforce in health services. Thus, this study hopes to contribute to the technological advancement of computational intelligence applied to healthcare.
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Affiliation(s)
- Myller Augusto Santos Gomes
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - João Luiz Kovaleski
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Regina Negri Pagani
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Vander Luiz da Silva
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
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Xia H, An W, Li J, Zhang ZJ. Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data. SOCIO-ECONOMIC PLANNING SCIENCES 2022; 80:100941. [PMID: 32921839 PMCID: PMC7477628 DOI: 10.1016/j.seps.2020.100941] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 08/15/2020] [Accepted: 08/28/2020] [Indexed: 05/09/2023]
Abstract
Based on complex adaptive system theory and information theory for investigating heterogeneous situations, this paper develops an outlier knowledge management framework based on three aspects-dimension, object, and situation-for dealing with extreme public health events. In the context of the COVID-19 pandemic, we apply advanced natural language processing (NLP) technology to conduct data mining and feature extraction on the microblog data from the Wuhan area and the imported case province (Henan Province) during the high and median operating periods of the epidemic. Our experiment indicates that the semantic and sentiment vocabulary of words, the sentiment curve, and the portrait of patients seeking help were all heterogeneous in the context of COVID-19. We extract and acquire the outlier knowledge of COVID-19 and incorporate it into the outlier knowledge base of extreme public health events for knowledge sharing and transformation. The knowledge base serves as a think tank for public opinion guidance and platform suggestions for dealing with extreme public health events. This paper provides novel ideas and methods for outlier knowledge management in healthcare contexts.
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Affiliation(s)
- Huosong Xia
- School of Management, Wuhan Textile University, Wuhan, 430073, China
- Research Center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province, Wuhan, 430073, China
| | - Wuyue An
- School of Management, Wuhan Textile University, Wuhan, 430073, China
| | - Jiaze Li
- School of Software, Zhengzhou University, Zhengzhou, 450000, China
| | - Zuopeng Justin Zhang
- Coggin College of Business, University of North Florida, Jacksonville, FL, 32224, USA
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Brossard PY, Minvielle E, Sicotte C. The path from big data analytics capabilities to value in hospitals: a scoping review. BMC Health Serv Res 2022; 22:134. [PMID: 35101026 PMCID: PMC8805378 DOI: 10.1186/s12913-021-07332-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/23/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND As the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges. METHODS This scoping review was conducted following Arksey and O'Malley's 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals. RESULTS Of 1,478 articles identified, 94 were included. Most of them are experimental research (n=69) published in medical (n=66) or computer science journals (n=28). The main value targets associated with the use of BDA are improving the quality of decision-making (n=56) and driving innovation (n=52) which apply mainly to care (n=67) and administrative (n=48) activities. To reach these targets, hospitals need to adequately combine BDA capabilities and value creation mechanisms (VCM) to enable knowledge generation and drive its assimilation. Benefits are endpoints of the value creation process. They are expected in all articles but realized in a few instances only (n=19). CONCLUSIONS This review confirms the value creation potential of BDA solutions in hospitals. It also shows the organizational challenges that prevent hospitals from generating actual benefits from BDAC-building efforts. The configuring of strategies, technologies and organizational capabilities underlying the development of value-creating BDA solutions should become a priority area for research, with focus on the mechanisms that can drive the alignment of BDA and organizational strategies, and the development of organizational capabilities to support knowledge generation and assimilation.
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Affiliation(s)
- Pierre-Yves Brossard
- Arènes (CNRS UMR 6051), Institut du Management, Chaire Prospective en Santé, École des Hautes Études en Santé Publique, Rennes, France
| | - Etienne Minvielle
- i3-Centre de Recherche en Gestion, Institut Interdisciplinaire de l’Innovation (UMR 9217), École polytechnique, Palaiseau, France
- Institut Gustave Roussy, Patient Pathway Department, Villejuif, France
| | - Claude Sicotte
- Arènes (CNRS UMR 6051), Institut du Management, Chaire Prospective en Santé, École des Hautes Études en Santé Publique, Rennes, France
- Department of Health Management, Evaluation and Policy, University of Montreal, Quebec, Canada
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Salman OH, Taha Z, Alsabah MQ, Hussein YS, Mohammed AS, Aal-Nouman M. A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106357. [PMID: 34438223 DOI: 10.1016/j.cmpb.2021.106357] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND With the remarkable increasing in the numbers of patients, the triaging and prioritizing patients into multi-emergency level is required to accommodate all the patients, save more lives, and manage the medical resources effectively. Triaging and prioritizing patients becomes particularly challenging especially for the patients who are far from hospital and use telemedicine system. To this end, the researchers exploiting the useful tool of machine learning to address this challenge. Hence, carrying out an intensive investigation and in-depth study in the field of using machine learning in E-triage and patient priority are essential and required. OBJECTIVES This research aims to (1) provide a literature review and an in-depth study on the roles of machine learning in the fields of electronic emergency triage (E-triage) and prioritize patients for fast healthcare services in telemedicine applications. (2) highlight the effectiveness of machine learning methods in terms of algorithms, medical input data, output results, and machine learning goals in remote healthcare telemedicine systems. (3) present the relationship between machine learning goals and the electronic triage processes specifically on the: triage levels, medical features for input, outcome results as outputs, and the relevant diseases. (4), the outcomes of our analyses are subjected to organize and propose a cross-over taxonomy between machine learning algorithms and telemedicine structure. (5) present lists of motivations, open research challenges and recommendations for future intelligent work for both academic and industrial sectors in telemedicine and remote healthcare applications. METHODS An intensive research is carried out by reviewing all articles related to the field of E-triage and remote priority systems that utilise machine learning algorithms and sensors. We have searched all related keywords to investigate the databases of Science Direct, IEEE Xplore, Web of Science, PubMed, and Medline for the articles, which have been published from January 2012 up to date. RESULTS A new crossover matching between machine learning methods and telemedicine taxonomy is proposed. The crossover-taxonomy is developed in this study to identify the relationship between machine learning algorithm and the equivalent telemedicine categories whereas the machine learning algorithm has been utilized. The impact of utilizing machine learning is composed in proposing the telemedicine architecture based on synchronous (real-time/ online) and asynchronous (store-and-forward / offline) structure. In addition to that, list of machine learning algorithms, list of the performance metrics, list of inputs data and outputs results are presented. Moreover, open research challenges, the benefits of utilizing machine learning and the recommendations for new research opportunities that need to be addressed for the synergistic integration of multidisciplinary works are organized and presented accordingly. DISCUSSION The state-of-the-art studies on the E-triage and priority systems that utilise machine learning algorithms in telemedicine architecture are discussed. This approach allows the researchers to understand the modernisation of healthcare systems and the efficient use of artificial intelligence and machine learning. In particular, the growing worldwide population and various chronic diseases such as heart chronic diseases, blood pressure and diabetes, require smart health monitoring systems in E-triage and priority systems, in which machine learning algorithms could be greatly beneficial. CONCLUSIONS Although research directions on E-triage and priority systems that use machine learning algorithms in telemedicine vary, they are equally essential and should be considered. Hence, we provide a comprehensive review to emphasise the advantages of the existing research in multidisciplinary works of artificial intelligence, machine learning and healthcare services.
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Affiliation(s)
- Omar H Salman
- Network Department, Faculty of Engineering, AL Iraqia University, Baghdad, Iraq.
| | - Zahraa Taha
- Network Department, Faculty of Engineering, AL Iraqia University, Baghdad, Iraq
| | - Muntadher Q Alsabah
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4ET, United Kingdom
| | - Yaseein S Hussein
- Information Systems and Computer Science Department, Ahmed Bin Mohammed Military College (ABMMC), P.O. Box: 22988, Doha Qatar
| | - Ahmed S Mohammed
- Information Systems and Computer Science Department, Ahmed Bin Mohammed Military College (ABMMC), P.O. Box: 22988, Doha Qatar
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Jelodar H, Wang Y, Rabbani M, Xiao G, Zhao R. A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums. J Med Syst 2020; 44:101. [PMID: 32266484 DOI: 10.1007/s10916-020-01547-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 02/17/2020] [Indexed: 11/28/2022]
Abstract
Medical data in online groups and social media contain valuable information, which is provided by both healthcare professionals and patients. In fact, patients can talk freely and share their personal experiences. These resources are a valuable opportunity for health professionals who can access patients' opinions, as well as discussions between patients. Recently, the data processing of the health community and, how to extract knowledge is a significant technical challenge. There are many online group and forums that users can discuss on healthcare issues. Therefore, we can examine these text documents for discovering knowledge and evaluating patients' behavior based on their opinions and discussions. For example, there are many questions and answering groups on Twitter or Facebook. Given the importance of the research, in this paper, we present a semantic framework based on topic model (LDA) and Random forest(RF) to predict and retrieval latent topics of healthcare text-documents from an online forum. We extract our healthcare records (patient-questions) from patient.info website as a real dataset. Experiments on our dataset show that social media forums could help for detecting significant patient safety problems on healthcare issues.
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Affiliation(s)
- Hamed Jelodar
- School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Yongli Wang
- School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Mahdi Rabbani
- School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Gang Xiao
- Science and Technology on Complex Systems Simulation Laboratory, Beijing, 100101, China
| | - Ruxin Zhao
- School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China
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Kumar M, Singh JB, Chandwani R, Gupta A. “Context” in healthcare information technology resistance: A systematic review of extant literature and agenda for future research. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2020. [DOI: 10.1016/j.ijinfomgt.2019.102044] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Study on Differences between Patients with Physiological and Psychological Diseases in Online Health Communities: Topic Analysis and Sentiment Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17051508. [PMID: 32111045 PMCID: PMC7084206 DOI: 10.3390/ijerph17051508] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 02/23/2020] [Accepted: 02/24/2020] [Indexed: 11/17/2022]
Abstract
The development of online social platforms has promoted the improvement of online health communities (OHCs). However, OHCs often ignore differences in user discussions caused by the characteristics of diseases. The purpose of this research was to study differences in the topics and emotions of patients with physiological and psychological diseases by mining the text that they posted in OHCs as well as to discuss how to satisfy these differences. The data came from Baidu Post Bar, the world's biggest Chinese forum. We collected 50,230 posts from heart disease, hypertension, depression and obsessive-compulsive bars. Then, we used topic modeling and sentiment analysis techniques on these posts. The results indicate that there are significant differences in the preferences of discussion and emotion between patients with physiological and psychological diseases. First, people with physiological diseases are more likely to discuss treatment of their illness, while people with psychological diseases are more likely to discuss feelings and living conditions. Second, psychological disease patients' posts included more extreme and negative emotions than those of physiological disease patients. These results are helpful for society to provide accurate medical assistance based on disease type to different patients, perfecting the national medical service system.
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Big data analytics for financial Market volatility forecast based on support vector machine. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2020. [DOI: 10.1016/j.ijinfomgt.2019.05.027] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Baig MI, Shuib L, Yadegaridehkordi E. Big data adoption: State of the art and research challenges. Inf Process Manag 2019. [DOI: 10.1016/j.ipm.2019.102095] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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