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Relevant bibliographies by topics / Chatbot for Healthcare

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Academic literature on the topic 'Chatbot for Healthcare'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Chatbot for Healthcare"

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Inupakutika, D., M. Nadim, G. R. Gunnam, S. Kaghyan, D. Akopian, P. Chalela, and A. G. Ramirez. "Integration of NLP and Speech-to-text Applications with Chatbots." Electronic Imaging 2021, no. 3 (June 18, 2021): 35–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.3.mobmu-035.

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With the evolving artificial intelligence technology, the chatbots are becoming smarter and faster lately. Chatbots are typically available round the clock providing continuous support and services. A chatbot or a conversational agent is a program or software that can communicate using natural language with humans. The challenge of developing an intelligent chatbot still exists ever since the onset of artificial intelligence. The functionality of chatbots can range from business oriented short conversations to healthcare intervention based longer conversations. However, the primary role that the chatbots have to play is in understanding human utterances in order to respond appropriately. To that end, there is an increased emergence of Natural Language Understanding (NLU) engines by popular cloud service providers. The NLU services identify entities and intents from the user utterances provided as input. Thus, in order to integrate such understanding to a chatbot, this paper presents a study on existing major NLU platforms. Then, we present a case study chatbot integrated with Google DialogFlow and IBM Watson NLU services and discuss their intent recognition performance.

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Oguntosin, Victoria, and Ayobami Olomo. "Development of an E-Commerce Chatbot for a University Shopping Mall." Applied Computational Intelligence and Soft Computing 2021 (March 19, 2021): 1–14. http://dx.doi.org/10.1155/2021/6630326.

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Chatbots have been used in many fields ranging from education to healthcare and are also used in e-commerce settings. This research aims at developing a web-based chatbot called Hebron for the Covenant University Community Mall. The chatbot is developed using Python and React.js as the programming languages and MySQL (Structured Query Language) server as the database to give a structure to the e-commerce datasets and Admin Portal process. The e-commerce chatbot application for Covenant University Shopping Mall (CUSM) seeks to provide an easy, smart, and comfortable shopping experience for the Covenant University Community.

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Ye, Byeong Jin, Ju Young Kim, Chunhui Suh, Seong Pil Choi, Maro Choi, Dong Hyun Kim, and Byung Chul Son. "Development of a Chatbot Program for Follow-Up Management of Workers’ General Health Examinations in Korea: A Pilot Study." International Journal of Environmental Research and Public Health 18, no. 4 (February 23, 2021): 2170. http://dx.doi.org/10.3390/ijerph18042170.

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(1) Background: Follow-up management of workers’ general health examination (WGHE) is important, but it is not currently well done. Chatbot, a type of digital healthcare tool, is used in various medical fields but has never been developed for follow-up management of WGHE in Korea. (2) Methods: The database containing results and explanations related to WGHE was constructed. Then, the channel, which connects users with the database was created. A user survey regarding effectiveness was administered to 23 healthcare providers. Additionally, interviews on applicability for occupational health services were conducted with six nurses in the agency of occupational health management. (3) Results: Chatbot was implemented on a small scale on the Amazon cloud service (AWS) EC2 using KaKaoTalk and Web Chat as user channels. Regarding the effectiveness, 21 (91.30%) rated the need for chatbots as very high; however, 11 (47.83%) rated the usability as not high. Of the 23 participants, 14 (60.87%) expressed overall satisfaction. Nurses appreciated the chatbot program as a method for resolving accessibility and as an aid for explaining examination results and follow-up management. (4) Conclusions: The effectiveness of WGHE and the applicability in the occupational health service of the chatbot program for follow-up management can be confirmed.

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Sumit. "AI Health Care Chatbot." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 13, 2020): 219–24. http://dx.doi.org/10.46501/ijmtst061241.

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Healthcare bot is a technology that makes interaction between man and machine possible by using Artificial Intelligence with the support of dialog flow. Now a day people tend to seek knowledge or information from internet that concern with health through online healthcare services. To lead a good life healthcare is very much important. But it is very difficult to obtain the consultation with the doctor in case of any health issues. The basic aim of this system is to bridge the vocabulary gap between the doctors by giving self-diagnosis from the comfort of one’s place. The proposed idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. To reduce the healthcare costs and improve accessibility to medical knowledge the medical bot is built. Certain bots act as a medical reference books, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a bot only when it can diagnose all kind of disease and provide necessary information. Hence, people will have an idea about their health and have the right protection.

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Pradhan, Rahul, Jaya Shukla, and Mani Bansal. "‘K-Bot’ Knowledge Enabled Personalized Healthcare Chatbot." IOP Conference Series: Materials Science and Engineering 1116, no. 1 (April 1, 2021): 012185. http://dx.doi.org/10.1088/1757-899x/1116/1/012185.

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Nadarzynski, Tom, Oliver Miles, Aimee Cowie, and Damien Ridge. "Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study." DIGITAL HEALTH 5 (January 2019): 205520761987180. http://dx.doi.org/10.1177/2055207619871808.

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Background Artificial intelligence (AI) is increasingly being used in healthcare. Here, AI-based chatbot systems can act as automated conversational agents, capable of promoting health, providing education, and potentially prompting behaviour change. Exploring the motivation to use health chatbots is required to predict uptake; however, few studies to date have explored their acceptability. This research aimed to explore participants’ willingness to engage with AI-led health chatbots. Methods The study incorporated semi-structured interviews (N-29) which informed the development of an online survey (N-216) advertised via social media. Interviews were recorded, transcribed verbatim and analysed thematically. A survey of 24 items explored demographic and attitudinal variables, including acceptability and perceived utility. The quantitative data were analysed using binary regressions with a single categorical predictor. Results Three broad themes: ‘Understanding of chatbots’, ‘AI hesitancy’ and ‘Motivations for health chatbots’ were identified, outlining concerns about accuracy, cyber-security, and the inability of AI-led services to empathise. The survey showed moderate acceptability (67%), correlated negatively with perceived poorer IT skills OR = 0.32 [CI95%:0.13–0.78] and dislike for talking to computers OR = 0.77 [CI95%:0.60–0.99] as well as positively correlated with perceived utility OR = 5.10 [CI95%:3.08–8.43], positive attitude OR = 2.71 [CI95%:1.77–4.16] and perceived trustworthiness OR = 1.92 [CI95%:1.13–3.25]. Conclusion Most internet users would be receptive to using health chatbots, although hesitancy regarding this technology is likely to compromise engagement. Intervention designers focusing on AI-led health chatbots need to employ user-centred and theory-based approaches addressing patients’ concerns and optimising user experience in order to achieve the best uptake and utilisation. Patients’ perspectives, motivation and capabilities need to be taken into account when developing and assessing the effectiveness of health chatbots.

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Gamble, Alyson. "Artificial intelligence and mobile apps for mental healthcare: a social informatics perspective." Aslib Journal of Information Management 72, no. 4 (June 2, 2020): 509–23. http://dx.doi.org/10.1108/ajim-11-2019-0316.

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PurposeFor decades, artificial intelligence (AI) has been utilized within the field of mental healthcare. This paper aims to examine AI chatbots, specifically as offered through mobile applications for mental healthcare (MHapps), with attention to the social implications of these technologies. For example, AI chatbots in MHapps are programmed with therapeutic techniques to assist people with anxiety and depression, but the promise of this technology is tempered by concerns about the apps' efficacy, privacy, safety and security.Design/methodology/approachUtilizing a social informatics perspective, a literature review covering MHapps, with a focus on AI chatbots was conducted from the period of January–April 2019. A borrowed theory approach pairing information science and social work was applied to analyze the literature.FindingsRising needs for mental healthcare, combined with expanding technological developments, indicate continued growth of MHapps and chatbots. While an AI chatbot may provide a person with a place to access tools and a forum to discuss issues, as well as a way to track moods and increase mental health literacy, AI is not a replacement for a therapist or other mental health clinician. Ultimately, if AI chatbots and other MHapps are to have a positive impact, they must be regulated, and society must avoid techno-fundamentalism in relation to AI for mental health.Originality/valueThis study adds to a small but growing body of information science research into the role of AI in the support of mental health.

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Vineeth, R., R. Rithish, D. V. S. N. Sai Varma, and B. V. Ajay Prakash. "Smart Health Care Chatbot for Prognosis of Treatments and Disease Diagnosis Using Machine Learning." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3947–51. http://dx.doi.org/10.1166/jctn.2020.8993.

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In this present world there are various diseases for which treatments and remedies are available abundantly. It is impossible for human to remember all the precautions and remedies to cure the disease. There is no relevant platform that could exhibit all the diseases and their respective remedies. Health professionals are not always available to users on all the time. Hence, the necessity of health care Chatbot plays a major role in this current world. In the proposed idea, we have created a HealthCare Chatbot with Artificial Intelligence techniques which can process the text input and predict the diseases associated with the symptoms given by the user. The HealthCare Chatbot implemented here is a user friendly platform which predicts the probable diseases and the home remedies, we can imply to cure based on the symptoms observed by the user in their knowledge.

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Kamita, Takeshi, Tatsuya Ito, Atsuko Matsumoto, Tsunetsugu Munakata, and Tomoo Inoue. "A Chatbot System for Mental Healthcare Based on SAT Counseling Method." Mobile Information Systems 2019 (March 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/9517321.

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In recent years, mental health management of employees in companies has become increasingly important. As the number of psychotherapists is not enough, it is necessary for employees to be able to keep their mental wellness on their own. A self-guided mental healthcare course using VR devices has been developed, and its stress reduction effect has been validated previously. This study proposes a new version of the course using smartphones and chatbots to enhance its convenience for use and to maintain user motivation for daily and repeated use. The effects of stress reduction and motivation maintenance were acknowledged.

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Avramova-Todorova, Gergana, and Milen Todorov. "Digital technologies for art therapy practices used in healthcare." Medical Science Pulse 13, no. 1 (April 25, 2019): 43–47. http://dx.doi.org/10.5604/01.3001.0013.1604.

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The use of digital technologies influences practically almost all aspects of our daily life. In the field of healthcare, in particular, technology plays a very important in activities related to data collection, data storing, and data analysis. The aim of technology in healthcare is to provide a range of healthcare professionals with access to information that will help increase the cost-effectiveness of care delivery and improve the efficacy of care. Psychology counseling is an area where specific elements, such as evaluation of emotional health, could be supported by the use of appropriate technologies. Such technology could increase accessibility to this type of assistance by reducing lengthy and costly travel to specialized centers. In addition, technology may enable overburdened professionals to increase the reach of their services, and help people with physical limitations who have restricted ability to travel to receive care. So-called ‘virtual assistants’ (also known as ‘chatbots’) could help patients to identify emotional imbalance. In general, the evaluation process could include a series of questions that aim to find the emotional problem, and ultimately to propose a suitable program of art therapy. The current study aims to outline the steps needed to develop a chatbot that is capable of identifying emotional imbalance and selecting a suitable program of art therapy. We also consider the addition of virtual and augmented reality as a further possibility for improving the therapeutic process.

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Dissertations / Theses on the topic "Chatbot for Healthcare"

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Kadariya, Dipesh. "kBot: Knowledge-Enabled Personalized Chatbot for Self-Management of Asthma in Pediatric Population." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1565944979193573.

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Castellucci, Matteo. "Applicazione di tecniche AI per la progettazione di un sistema a supporto del paziente iperteso." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21654/.

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In questa tesi si è cercato di trovare le soluzioni più efficaci a supporto delle questioni legate all'ipertensione di seguito descritte attraverso l'uso di tecniche riguardanti l'intelligenza artificiale e l'Internet of Things. Uno tra i compiti dei medici che si occupano di curare i malati di ipertensione è quello di elaborare protocolli per quanto riguarda la prevenzione e la cura di questa malattia, i quali vengono periodicamente aggiornati. Per supportare ciò, il primo progetto sviluppato è consistito in un'analisi dei dati sul dataset ottenuto a partire dall'elaborazione delle risposte date ai questionari che sono stati distribuiti durante la Giornata Mondiale dell'Ipertensione. A partire da questo, si è cercato di evidenziare la classe di persone che con più probabilità sono malate di ipertensione in modo tale che le linee guida aggiornate si concentrino maggiormente su costoro. La seconda questione affrontata è che non sempre le cure che vengono prescritte sono efficaci, talvolta a causa del medico, talvolta a causa del paziente. Si rende perciò necessario fornire ai pazienti degli strumenti che li aiutino direttamente nella cura della loro malattia. Devono avere anche lo scopo di aiutare il medico nel suo lavoro di monitoraggio periodico delle condizioni di salute del paziente, perché possa avere realmente il polso della situazione. Per fare questo, il secondo progetto ha riguardato lo sviluppo di un chatbot disponibile sulla piattaforma di messaggistica istantanea Telegram ad uso dei malati di ipertensione. Questo assistente virtuale permette loro di registrare le misurazioni di pressione che settimanalmente devono effettuare e ricorda loro di farlo quando passa troppo tempo dall'ultima misurazione. Il sistema permette inoltre di visualizzare medie e grafici delle misurazioni che sono state raccolte cosicché il medico può affidarsi ad uno strumento più evoluto del semplice libretto diario in cui il paziente annota tutte le misurazioni.

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Hoppe, Niklas. "Disruptive innovation in the healthcare sector : the advent of AI chatbots." Master's thesis, 2020. http://hdl.handle.net/10400.14/31219.

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Over the last several decades, the healthcare sector has faced many challenges. These include a shortage of doctors, especially in rural areas, high clinical costs, and an increasing number of diseases needing to be treated. This thesis focuses on the potential and the limitations of an innovative way to solve problems in healthcare – use of AI chatbots. We highlight the user’s perspective concerning AI healthcare chatbot technology. Based on qualitative and quantitative research, we conclude that this novel technology offers new opportunities for diagnostics, enables work to be carried out more efficiently, and gives the patient the power to “self-diagnose”. AI chatbots have not yet reached their full potential due to legal restrictions, insufficient data, and the lack of capacity to integrate them into different systems. Even though the number of AI chatbot users is increasing, people trust chatbots less than doctors. To enhance user engagement and create a higher level of trust, credible entities such as doctors and the government could recommend the use of AI chatbots. The general acceptance of chatbots has to be analyzed per country since it is explained by socio-economic factors (education, age, income), personality-related factors (attitude to new things, curiosity) and communication behavior factors.
Nas últimas décadas, o setor da saúde enfrentou muitos desafios. Nestes podem destacar-se a escassez de médicos, especialmente nas zonas rurais, custos de tratamento elevados e um número crescente de doenças a precisarem de ser tratadas. Esta tese foca-se no potencial e nas limitações de uma forma revolucionária de resolver problemas na área da saúde – o uso de chatbots de IA. Destacamos a perspetiva do utilizador em relação à assistência médica através da tecnologia de chatbot de IA. Com base em pesquisas qualitativas e quantitativas, concluímos que esta tecnologia inovadora oferece novas oportunidades para diagnósticos, permite que o trabalho seja realizado com mais eficiência e oferece ao paciente a capacidade de se autodiagnosticar. Os chatbots de IA ainda não atingiram todo o seu potencial devido a restrições legais, dados insuficientes e à falta de capacidade de integrá-los em diferentes sistemas. Ainda que o número de utilizadores de chatbot de IA esteja a aumentar, as pessoas confiam menos nos chatbots do que nos médicos. Para encorajar um maior envolvimento do utilizador e criar um nível mais alto de confiança, entidades credíveis como médicos e o governo podem recomendar o uso de chatbots de IA. A aceitação generalizada dos chatbots deve ser analisada por país, uma vez que é explicada por fatores socioeconómicos (educação, idade, rendimento), fatores relacionados com a personalidade (atitude perante coisas novas, curiosidade) e fatores de comportamento na comunicação.

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Books on the topic "Chatbot for Healthcare"

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Lee, Jyh-An, Reto Hilty, and Kung-Chung Liu, eds. Artificial Intelligence and Intellectual Property. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198870944.001.0001.

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This book explores artificial intelligence (AI), which has become omnipresent in today's business environment: from chatbots to healthcare services to various ways of creating useful information. While AI has been increasingly used to optimize various creative and innovative processes, the integration of AI into products, services, and other operational procedures raises significant concerns across virtually all areas of intellectual property (IP) law. Thus, AI has drawn extensive attention from IP experts globally and there have been some works on specific issues in the intersection between AI and IP. Surprisingly, however, there has not been a book providing a broad and comprehensive picture from the perspectives of the very nature of AI technology, its commercial implications, its interaction with different kinds of IP, IP administration, software and data, its social and economic impact on the innovation policy, and ultimately AI's eligibility as a legal entity. The book aims to fill the gap.

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Book chapters on the topic "Chatbot for Healthcare"

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Bandyopadhyay, Sivaji, Abdullah Faiz Ur Rahman Khilji, Sahinur Rahman Laskar, Partha Pakray, Rabiah Abdul Kadir, and Maya Silvi Lydia. "HealFavor: A Chatbot Application in Healthcare." In Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare, 41–61. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003146810-3.

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Müller, Lea, Jens Mattke, Christian Maier, and Tim Weitzel. "Conversational Agents in Healthcare: Using QCA to Explain Patients’ Resistance to Chatbots for Medication." In Chatbot Research and Design, 3–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39540-7_1.

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Ouerhani, Nourchène, Ahmed Maalel, and Henda Ben Ghézela. "Towards a Chatbot Based Smart Pervasive Healthcare Medical Emergency Cases." In Digital Health in Focus of Predictive, Preventive and Personalised Medicine, 149–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49815-3_17.

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Rakib, Afsana Binte, Esika Arifin Rumky, Ananna J. Ashraf, Md Monsur Hillas, and Muhammad Arifur Rahman. "Mental Healthcare Chatbot Using Sequence-to-Sequence Learning and BiLSTM." In Brain Informatics, 378–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86993-9_34.

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Yu, Hong Qing. "Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System." In Advances in Intelligent Systems and Computing, 30–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63092-8_3.

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Hussain, Syed Ali, Folu Ogundimu, and Shirish Bhattarai. "Mobile Phone-Based Chatbot for Family Planning and Contraceptive Information." In Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Healthcare Applications, 342–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22219-2_26.

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Fogliano, Fernando, Fernando Fabbrini, André Souza, Guilherme Fidélio, Juliana Machado, and Rachel Sarra. "Edgard, the Chatbot: Questioning Ethics in the Usage of Artificial Intelligence Through Interaction Design and Electronic Literature." In Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Healthcare Applications, 325–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22219-2_25.

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Lu, Guang, Martin Kubli, Richard Moist, Xiaoxiao Zhang, Nan Li, Ingo Gächter, Thomas Wozniak, and Matthes Fleck. "Tough Times, Extraordinary Care: A Critical Assessment of Chatbot-Based Digital Mental Healthcare Solutions for Older Persons to Fight Against Pandemics Like COVID-19." In Proceedings of Sixth International Congress on Information and Communication Technology, 735–43. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2377-6_68.

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Denecke, Kerstin, Alaa Abd-Alrazaq, and Mowafa Househ. "Artificial Intelligence for Chatbots in Mental Health: Opportunities and Challenges." In Multiple Perspectives on Artificial Intelligence in Healthcare, 115–28. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67303-1_10.

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Dolianiti, Foteini, Iraklis Tsoupouroglou, Panagiotis Antoniou, Stathis Konstantinidis, Savvas Anastasiades, and Panagiotis Bamidis. "Chatbots in Healthcare Curricula: The Case of a Conversational Virtual Patient." In Brain Function Assessment in Learning, 137–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60735-7_15.

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Conference papers on the topic "Chatbot for Healthcare"

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G, Megarajan. "Mega Bot – The Healthcare Chatbot." In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2021. http://dx.doi.org/10.1109/icesc51422.2021.9533025.

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Shinde, Nikita Vijay, Aniket Akhade, Pranali Bagad, Harshit Bhavsar, S. K. Wagh, and Amol Kamble. "Healthcare Chatbot System using Artificial Intelligence." In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2021. http://dx.doi.org/10.1109/icoei51242.2021.9452902.

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Badlani, Sagar, Tanvi Aditya, Meet Dave, and Sheetal Chaudhari. "Multilingual Healthcare Chatbot Using Machine Learning." In 2021 2nd International Conference for Emerging Technology (INCET). IEEE, 2021. http://dx.doi.org/10.1109/incet51464.2021.9456304.

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Athota, Lekha, Vinod Kumar Shukla, Nitin Pandey, and Ajay Rana. "Chatbot for Healthcare System Using Artificial Intelligence." In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2020. http://dx.doi.org/10.1109/icrito48877.2020.9197833.

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Gabrielli, Silvia, Kate Marie, and Carolina Della Corte. "SLOWBot (chatbot) Lifestyle Assistant." In PervasiveHealth '18: 12th EAI International Conference on Pervasive Computing Technologies for Healthcare. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240925.3240953.

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Kandpal, Prathamesh, Kapil Jasnani, Ritesh Raut, and Siddharth Bhorge. "Contextual Chatbot for Healthcare Purposes (using Deep Learning)." In 2020 Fourth World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). IEEE, 2020. http://dx.doi.org/10.1109/worlds450073.2020.9210351.

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Ur Rahman Khilji, Abdullah Faiz, Sahinur Rahman Laskar, Partha Pakray, Rabiah Abdul Kadir, Maya Silvi Lydia, and Sivaji Bandyopadhyay. "HealFavor: Dataset and A Prototype System for Healthcare ChatBot." In 2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA). IEEE, 2020. http://dx.doi.org/10.1109/databia50434.2020.9190281.

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Hwang, Tae-Ho, JuHui Lee, Se-Min Hyun, and KangYoon Lee. "Implementation of interactive healthcare advisor model using chatbot and visualization." In 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2020. http://dx.doi.org/10.1109/ictc49870.2020.9289621.

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Rahman, Md Moshiur, Ruhul Amin, Md Nazmul Khan Liton, and Nahid Hossain. "Disha: An Implementation of Machine Learning Based Bangla Healthcare Chatbot." In 2019 22nd International Conference on Computer and Information Technology (ICCIT). IEEE, 2019. http://dx.doi.org/10.1109/iccit48885.2019.9038579.

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Huang, Chin-Yuan, Ming-Chin Yang, Chin-Yu Huang, Yu-Jui Chen, Meng-Lin Wu, and Kai-Wen Chen. "A Chatbot-supported Smart Wireless Interactive Healthcare System for Weight Control and Health Promotion." In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2018. http://dx.doi.org/10.1109/ieem.2018.8607399.

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