kBot: Knowledge-Enabled Personalized Chatbot for Asthma Self-Management

Kadariya, Dipesh; Venkataramanan, Revathy; Yip, Hong Yung; Kalra, Maninder; Thirunarayanan, Krishnaprasad; Sheth, Amit P.

doi:10.1109/smartcomp.2019.00043

Cited by 79 publications

(70 citation statements)

References 8 publications

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“…All studies (n=12) were nonexperimental [ 59 - 70 ], that is, they did not include any experimental manipulation. A total of 4 papers [ 61 , 66 , 68 , 70 ] did not explicitly specify the study design they used, whereas the other papers provided labels. One study stated conducting a feasibility evaluation [ 63 ], 1 a focus group study [ 65 ], 1 a qualitative assessment of effectiveness and satisfaction [ 62 ], and 1 a case study [ 69 ].…”

Section: Resultsmentioning

confidence: 99%

“…Of the 12 studies, 7 [ 59 , 60 , 62 , 65 - 67 , 69 ] reported technology acceptance findings, whereas the others (n=5) did not [ 61 , 63 , 64 , 68 , 70 ]. A total of 3 studies [ 60 , 66 , 67 ] reported technology acceptance through a System Usability Survey (SUS). One study [ 67 ] reported a relatively high usability score (SUS score of mean 88/100), whereas 1 study [ 60 ] described better usability of its VCA for human papillomavirus (HPV) in comparison with industry standards (ie, SUS score of mean 72/100).…”

Section: Resultsmentioning

confidence: 99%

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Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

et al. 2021

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Background Chronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable manner. However, evidence on VCAs dedicated to the prevention and management of chronic and mental health conditions is unclear. Objective This study provides a better understanding of the current methods used in the evaluation of health interventions for the prevention and management of chronic and mental health conditions delivered through VCAs. Methods We conducted a systematic literature review using PubMed MEDLINE, Embase, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention or management of chronic or mental health conditions through a VCA and reporting an empirical evaluation of the system either in terms of system accuracy, technology acceptance, or both. A total of 2 independent reviewers conducted the screening and data extraction, and agreement between them was measured using Cohen kappa. A narrative approach was used to synthesize the selected records. Results Of 7170 prescreened papers, 12 met the inclusion criteria. All studies were nonexperimental. The VCAs provided behavioral support (n=5), health monitoring services (n=3), or both (n=4). The interventions were delivered via smartphones (n=5), tablets (n=2), or smart speakers (n=3). In 2 cases, no device was specified. A total of 3 VCAs targeted cancer, whereas 2 VCAs targeted diabetes and heart failure. The other VCAs targeted hearing impairment, asthma, Parkinson disease, dementia, autism, intellectual disability, and depression. The majority of the studies (n=7) assessed technology acceptance, but only few studies (n=3) used validated instruments. Half of the studies (n=6) reported either performance measures on speech recognition or on the ability of VCAs to respond to health-related queries. Only a minority of the studies (n=2) reported behavioral measures or a measure of attitudes toward intervention-targeted health behavior. Moreover, only a minority of studies (n=4) reported controlling for participants’ previous experience with technology. Finally, risk bias varied markedly. Conclusions The heterogeneity in the methods, the limited number of studies identified, and the high risk of bias show that research on VCAs for chronic and mental health conditions is still in its infancy. Although the results of system accuracy and technology acceptance are encouraging, there is still a need to establish more conclusive evidence on the efficacy of VCAs for the prevention and management of chronic and mental health conditions, both in absolute terms and in comparison with standard health care.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

et al. 2021

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“…The medical knowledge source can be local, such as obtaining it from medical personnel, or it can be collected from online sources such as Wikipedia or other websites. The medical knowledge can be presented to the user in the context of educational chatbots (17/45) [ 12 - 14 , 19 , 23 - 25 , 27 , 32 , 34 , 41 , 46 , 48 - 51 , 53 ], or it can be used to train machine learning algorithms (5/45) [ 39 , 40 , 43 , 44 , 51 ]. Many of the developed approaches store users’ data and use the data to customize the chatbot response and improve its functionality (11/45).…”

Section: Resultsmentioning

confidence: 99%

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

et al. 2020

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Background Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways. Objective This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work. Methods We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module. Results The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations. Conclusions Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning–based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.

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“…This study shows that patients desire personalized focused support and care [52]. Kbot is a knowledge enabled personalized chatbot introduced for asthma self-management [53]. Nurse Chatbot can provide chronic disease self-management support.…”

Section: Patient Carementioning

confidence: 87%

Machine Learning in Healthcare Communication

Siddique

Chow

2021

Encyclopedia

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Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

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kBot: Knowledge-Enabled Personalized Chatbot for Asthma Self-Management

Cited by 79 publications

References 8 publications

Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

Machine Learning in Healthcare Communication

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