Towards A Deep Learning Question-Answering Specialized Chatbot for Objective Structured Clinical Examinations

Zini, Julia El; Rizk, Yara; Awad, Mariette; Antoun, Jumana

doi:10.1109/ijcnn.2019.8851729

Cited by 10 publications

(12 citation statements)

References 21 publications

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“…Some were used through smartphones or tablets [4,8,9,12,20,23,24], either through custom applications, using sms messages [9], or integrated in social media applications like Telegram [20] and Line [4]. Others were web-based [10,11,16,22], run as standalone applications on personal computers [14,17,25], and even integrations in Virtual Reality systems [18,19].…”

Section: Resultsmentioning

confidence: 99%

“…One of the main ongoing challenges of chatbots is the natural interaction with the user, and their ability to understand what the user is saying. Therefore, several studies have focused more on the overall accuracy of the system, both related to the accuracy of the NLU components and understanding the user input [10,13,14,17,18], but also the dialogue management component that selects the correct responses to the users [18,21,25].…”

Section: Resultsmentioning

confidence: 99%

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An Overview of the Use of Chatbots in Medical and Healthcare Education

Frangoudes

Hadjiaros²,

Schiza³

et al. 2021

Lecture Notes in Computer Science

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Chatbots are becoming a trend in many fields such as medical, service industry and more recently in education. Especially in healthcare education, there is a growing interest in integrating chatbots in the learning and teaching processes mostly because of their portability and affordance. In this paper, we seek to explore the primary uses of chatbots in medical education, as well as how they are developed. We elaborate on current chatbot applications and research enacted in the domains of medical and healthcare education, We focus in the areas of virtual patients in medical education, patients' education related to healthcare matters but also chatbots as course assistance in for enhancing healthcare professionals' curricula. Additionally, we examine the metrics that have been used to evaluate these chatbots, which include subjective ones like the usability and acceptability by the users, and objectives ones, like their accuracy and users' skills evaluation. Overall, even though chatbots offer a flexible solution and a vast possibility to improve healthcare education, our literature review suggests that their efficacy has not been thoroughly tested. Also, limited examples of chatbots in European Healthcare curricula have been found. These call of the need for further research towards this direction.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

An Overview of the Use of Chatbots in Medical and Healthcare Education

Frangoudes

Hadjiaros²,

Schiza³

et al. 2021

Lecture Notes in Computer Science

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show abstract

“…Research in question generating systems is promising, but these generally create questions using natural language processing (NLP) methods that require underlying natural language understanding (NLU) systems. The NLU systems are either rule-based systems, such as that of Maicher et al (2017), which may be limited to very specific domains, or those based on machine learning, exemplified by Kenny et al (2010), or deep learning, exemplified by Zini et al (2019), that require a large amount of data for training and implementation.…”

Section: Problem-based Learning and Technology In Health Educationmentioning

confidence: 99%

Development of a Framework for Problem Domain Transference in Health-Related Problem Based Learning and Assessment

Summons

Harmon

Park

et al. 2021

J Probl Based Learn

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Investigate the capability of a knowledge-based framework and architecture, used in a specific health domain problem that can utilise transfer learning, to speed virtual patient development for problem-based training and assessment in other health domains. Methods: Analysis of a case study, based on a virtual patient used in the training of pharmacy students, to discover the viability of using generic, ontological knowledge capable of transfer to virtual patients in other health domains. Results: Areas of the virtual pharmacy patient knowledge-base were identified, along with corresponding expected student questions, that are generic to other health domains. Using the framework from the case study to develop a new virtual patient for problem-based learning and assessment in a new health domain, these generic target questions could be utilised to speed up the development of other learning stimuli in future projects involving different health domains, such as nurse training in pain management. Conclusions: With some modification, the framework of the case-study virtual patient was found to be capable of supporting generic expected student questions capable of re-use in virtual patients with new clinical conditions.

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“…Ni et al [40] suggested a knowledge-driven primary care chatbot system that has an analytic NLP-based engine for understanding the descriptions of patients' symptom, a reasoner for mapping symptoms to possible causes, and a question generator for creating further dialogue questions. On the other hand, Zini et al [41] developed a deep learning framework-based conversational agent to represent a virtual patient that can be used for teaching medical students on patient examination.…”

Section: Introductionmentioning

confidence: 99%

“…e integration of NLP and machine learning algorithms has also played a key role in creating chatbot application for disease prediction and treatment recommendation [48]. Deep learning framework was proposed by Zini et al [41] for enhancing virtual patients' conversational skills. e authors integrated long-short-term memory networks and CNN for sentence embeddings in a given QA script.…”

Section: Introductionmentioning

confidence: 99%

Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic

Omoregbe

Ndaman

Misra

et al. 2020

Journal of Healthcare Engineering

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The use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients’ access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluation.

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Towards A Deep Learning Question-Answering Specialized Chatbot for Objective Structured Clinical Examinations

Cited by 10 publications

References 21 publications

An Overview of the Use of Chatbots in Medical and Healthcare Education

An Overview of the Use of Chatbots in Medical and Healthcare Education

Development of a Framework for Problem Domain Transference in Health-Related Problem Based Learning and Assessment

Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic

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