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

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.

Capturing Evolutional Knowledge Using Time Interval Tracing

2007 ◽  
Vol 11 (4) ◽  
pp. 373-380
Author(s):  
Shun-Chieh Lin ◽  
◽  
Chia-Wen Teng ◽  
Shian-Shyong Tseng ◽  
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Time Domain ◽  
Domain Knowledge ◽  
Domain Ontology ◽  
Time Interval ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Over Time

Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.

scholarly journals Prototyping a Knowledge-Based System to Identify Botanical Extracts for Plant Health in Sub-Saharan Africa

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 896
Author(s):  
Pierre J. Silvie ◽  
Pierre Martin ◽  
Marianne Huchard ◽  
Priscilla Keip ◽  
Alain Gutierrez ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Crop Protection ◽  
Plant Health ◽  
Sub Saharan Africa ◽  
Pest Species ◽  
Knowledge Based System ◽  
Pests And Diseases ◽  
Knowledge Based ◽  
Sub Saharan ◽  
The One

Replacing synthetic pesticides and antimicrobials with plant-based extracts is a current alternative adopted by traditional and family farmers and many organic farming pioneers. A range of natural extracts are already being marketed for agricultural use, but many other plants are prepared and used empirically. A further range of plant species that could be effective in protecting different crops against pests and diseases in Africa could be culled from the large volume of knowledge available in the scientific literature. To meet this challenge, data on plant uses have been compiled in a knowledge base and a software prototype was developed to navigate this trove of information. The present paper introduces this so-called Knomana Knowledge-Based System, while providing outputs related to Spodoptera frugiperda and Tuta absoluta, two invasive insect species in Africa. In early October 2020, the knowledge base hosted data obtained from 342 documents. From these articles, 11,816 uses—experimental or applied by farmers—were identified in the plant health field. In total, 384 crop pest species are currently reported in the knowledge base, in addition to 1547 botanical species used for crop protection. Future prospects for applying this interdisciplinary output to applications under the One Health approach are presented.

Ontology-Based Optimization in Search Engine

2013 ◽  
pp. 285-301
Author(s):  
Leonardo Balduzzi ◽  
Ignacio Cuesta
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Search Engine ◽  
Search Engines ◽  
Inductive Learning ◽  
System Evolution ◽  
Knowledge Based System ◽  
Learning Capacity ◽  
Knowledge Based ◽  
Learning Techniques

The major aim of the chapter is to propose and study the use of ontology-based optimization for positioning websites in search engines. In this sense, using heterogeneous inductive learning techniques and ontology for knowledge representation, a knowledge-based system which is capable of supporting the activity of SEO (Search Engine Optimization) has been designed and implemented. From its knowledge base, the system suggests the most appropriate optimization tasks for positioning a pair (keyword, website) on the first page of search engines and infers the positioning results to be obtained. The system evolution and learning capacity allows optimizing the productivity and effectiveness of the SEO process.

scholarly journals Conversational Agent Research Toolkit: An alternative for creating and managing chatbots for experimental research

2019 ◽  
Author(s):  
Theo Araujo
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Experimental Research ◽  
Experimental Studies ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Communication Environment

Conversational agents in the form of chatbots available in messaging platforms are gaining increasing relevance in our communication environment. Based on natural language processing and generation techniques, they are built to automatically interact with users in several contexts. We present here a tool, the Conversational Agent Research Toolkit (CART), aimed at enabling researchers to create conversational agents for experimental studies. CART integrates existing APIs frequently used in practice and provides functionality that allows researchers to create and manage multiple versions of a chatbot to be used as stimuli in experimental studies. This paper provides an overview of the tool and provides a step-by-step tutorial of to design an experiment with a chatbot.

scholarly journals NELL’s subcategories from a question answering environment

2018 ◽  
Author(s):  
Wesley W. O. Souza ◽  
Diorge Brognara ◽  
João A. Leite ◽  
Estevam R. Hruschka Jr.
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Data Storage ◽  
Language Processing ◽  
Processing Speed ◽  
Question Answering ◽  
Learning Algorithm ◽  
Conversational Agents ◽  
Learning Agent ◽  
The Web

With advances in machine learning, natural language processing, processing speed, and amount of data storage, conversational agents are being used in applications that were not possible to perform within a few years. NELL, a machine learning agent who learns to read the web, today has a considerably large ontology and while it can be used for multiple fact queries, it is also possible to expand it further and specialize its knowledge. One of the first steps to succeed is to refine existing knowledge in NELL’s knowledge base so that future communication between it and humans is as natural as possible. This work describes the results of an experiment where we investigate which machine learning algorithm performs best in the task of classifying candidate words to subcategories in the NELL knowledge base.

Project Halo Update—Progress Toward Digital Aristotle

AI Magazine ◽  
2010 ◽  
Vol 31 (3) ◽  
pp. 33 ◽  
Author(s):  
David Gunning ◽  
Vinay K. Chaudhri ◽  
Peter E. Clark ◽  
Ken Barker ◽  
Shaw-Yi Chaw ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Advanced Placement ◽  
Language Processing ◽  
Question Answering ◽  
Knowledge Representation And Reasoning ◽  
Domain Experts ◽  
Knowledge Based ◽  
Novice Users ◽  
Applied Knowledge

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.

scholarly journals Conversational Agent Research Toolkit : An alternative for creating and managing chatbots for experimental research1

2020 ◽  
Vol 2 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Theo Araujo
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Experimental Studies ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Communication Environment

Abstract Conversational agents in the form of chatbots available in messaging platforms are gaining increasing relevance in our communication environment. Based on natural language processing and generation techniques, they are built to automatically interact with users in several contexts. We present here a tool, the Conversational Agent Research Toolkit (CART), aimed at enabling researchers to create conversational agents for experimental studies. CART integrates existing APIs frequently used in practice and provides functionality that allows researchers to create and manage multiple versions of a chatbot to be used as stimuli in experimental studies. This paper provides an overview of the tool and provides a step-by-step tutorial of to design an experiment with a chatbot.

An Object-Oriented Knowledge-Based System for Assembly of Printed Circuit Boards

1991 ◽  
Author(s):  
Sisir K. Padhy ◽  
S. N. Dwivedi
Keyword(s):  
Knowledge Base ◽  
Printed Circuit Boards ◽  
Printed Circuit Board ◽  
Object Oriented ◽  
Circuit Board ◽  
Design Stage ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Knowledge Based System ◽  
Knowledge Based

Abstract In this paper, Printed Circuit Board Assembly Advisor (PCAAD), an object-oriented knowledge-based system is described. The system aims to aid the designer by suggesting design modifications that will lead to a better design for assembly of the Printed Circuit Boards. To account for the new trends in the printed circuit board production, hybrid technology, i.e. combination of both the through-hole mounted technology and surface mounted technology, is taken into consideration in developing the knowledge base. The assembly constraints as well as various limitations of different techniques and processes are considered to formulate the rules and guidelines. Moreover, a hierarchical rule structure has been employed in creating the knowledge base. Smalltalk-80, the object-oriented language and Surface Percept Description Language (SPDL) are used for the creation of knowledge base. The system provides a high-level user interface and reasoning capability to solve complex problems. It is capable of ranking different designs and suggesting design modifications to the designer during the design stage to eliminate assembly problems in the latter phase of board production.

Repair Learning for a Working Knowledge-Based System

1995 ◽  
Vol 209 (4) ◽  
pp. 287-294
Author(s):  
P Olley ◽  
A K Kochhar
Keyword(s):  
Systematic Error ◽  
Knowledge Base ◽  
Domain Knowledge ◽  
Closed Loop ◽  
Noisy Data ◽  
Simulated Data ◽  
Learning Method ◽  
Learning Mechanism ◽  
Knowledge Based System ◽  
Knowledge Based

This paper addresses the issues of using a learning mechanism for closed-loop updating of the repair knowledge base of a working knowledge-based system (KBS). Issues addressed are stability under noisy data and errors arising from learning from cases in which several repairs are attempted. Simulated data are used to investigate the effects of the latter feature. It is shown that the learning method can cause a significant systematic error in learnt knowledge. A knowledge-based method, which aims to intelligently compensate for the systematic error using diagnostic domain knowledge, is investigated. It is shown that the method greatly reduces the systematic error in learnt repair knowledge.

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