OlloBot - Towards A Text-Based Arabic Health Conversational Agent: Evaluation and Results

Proceedings of Recent Advances in Natural Language Processing, pages 295–303, Varna, Bulgaria, Sep 2–4, 2019. https://doi.org/10.26615/978-954-452-056-4_034 295 OlloBot - Towards A Text-Based Arabic Health Conversational Agent: Evaluation and Results Ahmed Fadhil Department of Computer Science Universita Degli Studi di Trento Trento, Italy ahmed.fadhil@unitn.it Ahmed AbuRa’ed Information & Communication Technologies Universitat Pompeu Fabra Barcelona, Spain ahmed.aburaed@upf.edu Abstract We introduce OlloBot, an Arabic conver- sational agent that assists physicians and supports patients with the care process. It doesn’t replace the physicians, instead provides health tracking and support and assists physicians with the care delivery through a conversation medium. The cur- rent model comprises healthy diet, phys- ical activity, mental health, in addition to food logging. Not only OlloBot tracks user daily food, it also offers useful tips for healthier living. We will discuss the de- sign, development and testing of OlloBot, and highlight the findings and limitations arose from the testing. 1 Introduction According to World Health Organisation (WHO), poor diet and physical inactivity have tremendous implications on individuals health (Michie et al., 2009; Hard et al., 2012). Healthy diet helps pro- tect against malnutrition in all its forms, as well as noncommunicable diseases (NCDs) (Michie et al., 2009). Middle eastern countries are among the mostly affected nations by poor diet and physi- cal inactivity and its effect on cardiovascular dis- eases (Organization et al., 2010, 2012). According to WHO report, total deaths caused by NCDs are among the highest in countries such as, Saudi Ara- bia, Iraq, Qatar, and Bahrain (See Table-1). This is an evident for a global risk and there is a need for nationwide approach to help mitigate or prevent this escalation. By 2025, AI systems could be involved in ev- erything from population health management, to virtual assistants capable of answering specific pa- tient queries. Overall, AI has the potential to im- prove outcomes by 30 to 40 percent while cutting The NCDs and Middle East (WHO) Countries % of total deaths by NCDs Saudi Arabia Cardiovascular diseases = 46% Qatar Cardiovascular diseases = 24% Iraq Cardiovascular diseases = 33% Bahrain Cardiovascular diseases = 26% Table 1: Middle East Countries and NCD Burden. treatment costs by as much as 50 percent (Koh et al., 2011). Conversational agents, an example of AI powered systems can communicate with users through an intelligent conversation using a natural language, they can aid doctors in enhancing pro- ductivity and enabling them to respond to patients quickly. For example, doctors could interact with a diet chatbot to recommend the appropriate food to their patients. Patients could also engage with this chatbot to get instant information about their dietary choices. Currently, some ways to use healthcare chatbots include: scheduling doctor appointments based on the severity of the symptoms, monitoring the health status and notifying a human nurse imme- diately if the parameters are out of control, help- ing home-care assistants stay informed about pa- tients evolution. However, current health conver- sational agents are mature for English language but still in their infancy for specific …

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