Building a Working Alliance with a Knowledge Based System Through an Embodied Conversational Agent

Knowledge Management and Acquisition for Intelligent Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-319-42706-5_16 ◽

2016 ◽

pp. 213-227 ◽

Cited By ~ 1

Author(s):

Deborah Richards ◽

Patrina Caldwell

Keyword(s):

Working Alliance ◽

Conversational Agent ◽

Embodied Conversational Agent ◽

Knowledge Based System ◽

Knowledge Based

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Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

10.2196/preprints.33665 ◽

2021 ◽

Author(s):

Marciane Mueller ◽

Rejane Frozza ◽

Liane Mählmann Kipper ◽

Ana Carolina Kessler

Keyword(s):

Medical Care ◽

Knowledge Base ◽

Language Processing ◽

Emergency Care ◽

Risk Classification ◽

Conversational Agent ◽

Conversational Agents ◽

Hospital Emergency ◽

Knowledge Based System ◽

Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

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