Abstract
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.
Highlights
Remote diagnosis systems are becoming increasingly popular and accurate, with enormous advantages such as costeffectiveness, fast and reliable decision support for medical diagnostics, and treatment and prevention of disease, illness, injury, and other physical and mental damages in human beings. e rise in remote health services offered by healthcare institutions coincided with the evolution of assisted living systems and environments, aiming to widen the possibility for older and disadvantaged people to access appropriate healthcare services and improve their health status and clinical outcome [1]
Medical diagnostic processes carried out with the aid of computerrelated technology which is on the rise daily have improved the experience and capabilities of physicians to make an effective diagnosis of diseases while employing novel signal processing techniques for analysis of patients’ physiological data [3, 4] and deep neural networks for decision support [5]
Data Collection. e data used in this study were collected from a medical database, and an interview was conducted for extraction of text content from experts and individuals with knowledge about the various diseases. e extracted text content was stored on the local file of the system
Summary
Remote diagnosis systems are becoming increasingly popular and accurate, with enormous advantages such as costeffectiveness, fast and reliable decision support for medical diagnostics, and treatment and prevention of disease, illness, injury, and other physical and mental damages in human beings. e rise in remote health services (or telehealth) offered by healthcare institutions coincided with the evolution of assisted living systems and environments, aiming to widen the possibility for older and disadvantaged people to access appropriate healthcare services and improve their health status and clinical outcome [1]. E natural language processing (NLP) technology can serve as an interaction between computers and humans using linguistic analysis and deep learning methods to obtain knowledge from an unstructured free text [10]. The application of NLP techniques to screen patients and assist medical experts in their diagnosis would serve as a boost in successfully improving healthcare services through effective analysis of narrative text of symptoms provided by a patient. E continuous growth of mobile technology has affected every facet of human life around the globe as its support of healthcare objectives through telemedicine, telehealth, and m-health [52] has helped to diagnose and treat patients at low cost especially in the developing countries, where there are limited options of diagnosis and treatment. The medical experts need a platform to keep track of large text-based chunk of knowledge narrated by patients in a natural language, improving healthcare delivery for remote patients. E contribution of this paper is as follows: (1) we have developed a text-based medical diagnosis system which
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