Developing an IoT-based Conversational AI Recommender Assistant for Vital Sign Predicted Anomalies

Publication Type

Conference Proceeding

Publication Date (Issue Year)

2023

Journal Name

ACM International Conference Proceeding Series

Abstract

In most real-time scenarios such as emergency first response or a patient self-monitoring using a wearable device, likely, accessing a healthcare physician for assessing potential vital sign anomalies and providing a recommendation will be impossible; thus potentially putting the patient at risk. Leveraging the latest advances in Natural Language Processing (NLP), this paper presents a research-driven design and development of a cloud-based conversational AI platform trained to predict vital signs anomalies and provides recommendations from a dataset created by physicians. To reinforce the learning of the virtual assistant, the Conversation Driven Development (CDD) methodology has been adopted to involve end users in the testing process in the early phase. The proposed platform will help to manage the consequences of low physician-patient ratios, especially in developing countries.

Keywords

IoT-based Conversational, AI, Vital Sign Predicted, Anomalies

Rsif Scholar Name

Theofrida Maginga

Rsif Scholar Nationality

Tanzania

Cohort

Cohort 3

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

University of Rwanda (UR), Rwanda

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