Abstract
The paper reports on a proof-of-concept of \(\mathsf {Mandy}\), a primary care chatbot system created to assist healthcare staffs by automating the patient intake process. The chatbot interacts with a patient by carrying out an interview, understanding their chief complaints in natural language, and submitting reports to the doctors for further analysis. The system provides a mobile-app front end for the patients, a diagnostic unit, and a doctor’s interface for accessing patient records. The diagnostic unit consists of three main modules: An analysis engine for understanding patients symptom descriptions, a symptom-to-cause mapper for reasoning about potential causes, and a question generator for deriving further interview questions. The system combines data-driven natural language processing capability with knowledge-driven diagnostic capability. We evaluate our proof-of-concept on benchmark case studies and compare the system with existing medical chatbots.
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Notes
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Florence Bot, https://florence.chat/.
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Your.MD, https://www.your.md/.
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HealthTap, https://www.healthtap.com/.
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http://13.54.91.140:8080/HealthWebApp/ To log in, the user needs to input ‘admin’ as both Username and Password.
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E.g. online databases such as http://www.diseasesdatabase.com.
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Acknowledgement
The first author is partially funded by a scholarship offered by Precision Driven Health in New Zealand, a public-private research partnership aimed at improving health outcomes through data science. Initial progress of theresearch was reported in the PDH & Orion Health Blog https://orionhealth.com/ global/knowledge-hub/blogs/meet-mandy-an-intelligent-and-interactive-medic are-system/.
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Ni, L., Lu, C., Liu, N., Liu, J. (2017). MANDY: Towards a Smart Primary Care Chatbot Application. In: Chen, J., Theeramunkong, T., Supnithi, T., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2017. Communications in Computer and Information Science, vol 780. Springer, Singapore. https://doi.org/10.1007/978-981-10-6989-5_4
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