Extending a Conventional Chatbot Knowledge Base to External Knowledge Source and Introducing User-Based Sessions for Diabetes Education

2019 ◽  
pp. 333-343
Author(s):  
Shafquat Hussain ◽  
Athula Ginige
Keyword(s):  
Knowledge Base ◽  
Local Knowledge ◽  
Health Informatics ◽  
Diabetes Management ◽  
Diabetes Education ◽  
Conversational Agents ◽  
Knowledge Source ◽  
Web Based ◽  
External Knowledge ◽  
User Query

Chatbots or conversational agents are computer programs that interact with users using natural language through artificial intelligence in a way that the user thinks he is having dialogue with a human. One of the main limits of chatbot technology is associated with the construction of its local knowledge base. A conventional chatbot knowledge base is typically hand constructed, which is a very time-consuming process and may take years to train a chatbot in a particular field of expertise. This chapter extends the knowledge base of a conventional chatbot beyond its local knowledge base to external knowledge source Wikipedia. This has been achieved by using Media Wiki API to retrieve information from Wikipedia when the chatbot's local knowledge base does not contain the answer to a user query. To make the conversation with the chatbot more meaningful with regards to the user's previous chat sessions, a user-specific session ability has been added to the chatbot architecture. An open source AIML web-based chatbot has been modified and programmed for use in the health informatics domain. The chatbot has been named VDMS – Virtual Diabetes Management System. It is intended to be used by the general community and diabetic patients for diabetes education and management.

SWFQA Semantic Web Based Framework for Question Answering

2019 ◽  
Vol 9 (1) ◽  
pp. 88-106
Author(s):  
Irphan Ali ◽  
Divakar Yadav ◽  
Ashok Kumar Sharma
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Knowledge Bases ◽  
Digital Information ◽  
Web Based ◽  
Question Answering System ◽  
User Query

A question answering system aims to provide the correct and quick answer to users' query from a knowledge base. Due to the growth of digital information on the web, information retrieval system is the need of the day. Most recent question answering systems consult knowledge bases to answer a question, after parsing and transforming natural language queries to knowledge base-executable forms. In this article, the authors propose a semantic web-based approach for question answering system that uses natural language processing for analysis and understanding the user query. It employs a “Total Answer Relevance Score” to find the relevance of each answer returned by the system. The results obtained thereof are quite promising. The real-time performance of the system has been evaluated on the answers, extracted from the knowledge base.

scholarly journals An Interactive Web Application for the Statistical Analysis of Continuous Glucose Monitoring Data in Epidemiological Studies

2021 ◽  
pp. 193229682098557
Author(s):  
Alysha M. De Livera ◽  
Jonathan E. Shaw ◽  
Neale Cohen ◽  
Anne Reutens ◽  
Agus Salim
Keyword(s):  
Continuous Glucose Monitoring ◽  
Web Application ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Epidemiological Studies ◽  
Interactive Software ◽  
Web Based ◽  
Software Applications ◽  
Visualization Of Data ◽  
User Friendly

Motivation: Continuous glucose monitoring (CGM) systems are an essential part of novel technology in diabetes management and care. CGM studies have become increasingly popular among researchers, healthcare professionals, and people with diabetes due to the large amount of useful information that can be collected using CGM systems. The analysis of the data from these studies for research purposes, however, remains a challenge due to the characteristics and large volume of the data. Results: Currently, there are no publicly available interactive software applications that can perform statistical analyses and visualization of data from CGM studies. With the rapidly increasing popularity of CGM studies, such an application is becoming necessary for anyone who works with these large CGM datasets, in particular for those with little background in programming or statistics. CGMStatsAnalyser is a publicly available, user-friendly, web-based application, which can be used to interactively visualize, summarize, and statistically analyze voluminous and complex CGM datasets together with the subject characteristics with ease.

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.

scholarly journals Translating Research into Practice: Impact of an Interprofessional Diabetes Education Model on Patient Health Outcomes

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Olayinka O. Shiyanbola ◽  
Becky Randall ◽  
Cristina Lammers ◽  
Karly A. Hegge ◽  
Michelle Anderson
Keyword(s):  
Health Promotion ◽  
Clinical Outcomes ◽  
Diabetes Management ◽  
Diabetes Education ◽  
Health Behaviours ◽  
Self Management ◽  
Health Promotion Program ◽  
Diabetes Knowledge ◽  
Foot Care ◽  
Eye Care

Background: Patient education programs encouraging diabetes self-management can improve clinical outcomes and lessen diabetes complications. This study implemented an innovative interprofessional student-led diabetes self-management and health promotion program for an underserved population and demonstrated an improvement in participant clinical outcomes and students’ understanding of interprofessional aspects of diabetes care.Methods and Findings: This community-based program was implemented at two sites that serve medically underserved individuals. Students from five health career professions led educational sessions designed to demonstrate critical components of diabetes self-management. The six-month longitudinal program covered topics within the Alphabet Strategy, including Advice, Blood pressure, Cholesterol, Diabetes control, Dental care, Diet, Eye care, Foot care, and Guardian drugs. Participants completed surveys evaluating diabetes knowledge, understanding of diabetes care, and health behaviours. Clinical values were collected before and after the program. Student surveys assessed their understanding of diabetes self-management. Upon completion of the program, all assessments were repeated to determine if there were improvements in outcomes. Thirty-eight participants and thirty students completed the study. There were significant improvements in participants’ diabetes knowledge, understanding of diabetes management, and clinical outcomes. There were significant improvements in the students’ ability to educate patients about foot care, eye care, and guardian drugs, as well as increased awareness of the role of each health profession in diabetes care.Conclusions: This interprofessional health promotion model showed significant improvements in patient and student outcomes. This innovative student-led program could be implemented in other settings and for the management of other chronic diseases.

Systematic Prioritization of the STARE-HI Reporting Items

2012 ◽  
Vol 51 (02) ◽  
pp. 104-111 ◽  
Author(s):  
J. Talmon ◽  
E. Ammenwerth ◽  
J. Brender ◽  
M. Rigby ◽  
P. Nykanen ◽  
...  
Keyword(s):  
Study Design ◽  
Health Informatics ◽  
Evaluation Studies ◽  
Evaluation Criteria ◽  
Laboratory Simulation ◽  
Conference Paper ◽  
Web Based ◽  
Measure Evaluation ◽  
Conference Papers ◽  
Prioritization Framework

SummaryBackground: We previously devised and published a guideline for reporting health informatics evaluation studies named STARE-HI, which is formally endorsed by IMIA and EFMI.Objective: To develop a prioritization framework of ranked reporting items to assist authors when reporting health informatics evaluation studies in space restricted conference papers and to apply this prioritization framework to measure the quality of recent health informatics conference papers on evaluation studies.Method: We deconstructed the STARE-HI guideline to identify reporting items. We invited a total of 111 authors of health informatics evaluation studies, reviewers and editors of health Informatics conference proceedings to score those reporting items on a scale ranging from “0 – not necessary in a conference paper” through to “10 – essential in a conference paper” by a web-based survey. From the responses we derived a mean priority score. All evaluation papers published in proceedings of MIE2006, Medinfo2007, MIE2008 and AMIA2008 were rated on these items by two reviewers. From these ratings a priority adjusted completeness score was computed for each paper.Results: We identified 104 reporting items from the STARE-HI guideline. The response rate for the survey was 59% (66 out of 111). The most important reporting items (mean score ≥ 9) were “Interpret the data and give an answer to the study question – (in Discussion)”, “Whether it is a laboratory, simulation or field study – (in Methods-study design)” and “Description of the outcome measure/evaluation criteria – (in Methods-study design)”. Per reporting area the statistically more significant important reporting items were distinguished from less important ones. Four reporting items had a mean score ≤ 6. The mean priority adjusted completeness of evaluation papers of recent health informatics conferences was 48% (range 14 –78%).Conclusion: We produced a ranked list of reporting items from STARE-HI according to their prioritized relevance for inclusion in space-limited conference papers. The priority adjusted completeness scores demonstrated room for improvement for the analyzed conference papers. We believe that this prioritization framework is an aid to improving the quality and utility of conference papers on health informatics evaluation studies.

scholarly journals A Suite of Mobile Conversational Agents for Daily Stress Management (Popbots): Mixed Methods Exploratory Study (Preprint)

2020 ◽  
Author(s):  
Matthew Louis Mauriello ◽  
Nantanick Tantivasadakarn ◽  
Marco Antonio Mora-Mendoza ◽  
Emmanuel Thierry Lincoln ◽  
Grace Hon ◽  
...  
Keyword(s):  
Stress Management ◽  
Design Guidelines ◽  
Coping Resources ◽  
Rank Test ◽  
Depression Symptoms ◽  
Conversational Agents ◽  
Daily Stress ◽  
Web Based ◽  
The Web

BACKGROUND Approximately 60%-80% of the primary care visits have a psychological stress component, but only 3% of patients receive stress management advice during these visits. Given recent advances in natural language processing, there is renewed interest in mental health chatbots. Conversational agents that can understand a user’s problems and deliver advice that mitigates the effects of daily stress could be an effective public health tool. However, such systems are complex to build and costly to develop. OBJECTIVE To address these challenges, our aim is to develop and evaluate a fully automated mobile suite of shallow chatbots—we call them Popbots—that may serve as a new species of chatbots and further complement human assistance in an ecosystem of stress management support. METHODS After conducting an exploratory Wizard of Oz study (N=14) to evaluate the feasibility of a suite of multiple chatbots, we conducted a web-based study (N=47) to evaluate the implementation of our prototype. Each participant was randomly assigned to a different chatbot designed on the basis of a proven cognitive or behavioral intervention method. To measure the effectiveness of the chatbots, the participants’ stress levels were determined using self-reported psychometric evaluations (eg, web-based daily surveys and Patient Health Questionnaire-4). The participants in these studies were recruited through email and enrolled on the web, and some of them participated in follow-up interviews that were conducted in person or on the web (as necessary). RESULTS Of the 47 participants, 31 (66%) completed the main study. The findings suggest that the users viewed the conversations with our chatbots as helpful or at least neutral and came away with increasingly positive sentiment toward the use of chatbots for proactive stress management. Moreover, those users who used the system more often (ie, they had more than or equal to the median number of conversations) noted a decrease in depression symptoms compared with those who used the system less often based on a Wilcoxon signed-rank test (W=91.50; Z=−2.54; <i>P</i>=.01; <i>r</i>=0.47). The follow-up interviews with a subset of the participants indicated that half of the common daily stressors could be discussed with chatbots, potentially reducing the burden on human coping resources. CONCLUSIONS Our work suggests that suites of shallow chatbots may offer benefits for both users and designers. As a result, this study’s contributions include the design and evaluation of a novel suite of shallow chatbots for daily stress management, a summary of benefits and challenges associated with random delivery of multiple conversational interventions, and design guidelines and directions for future research into similar systems, including authoring chatbot systems and artificial intelligence–enabled recommendation algorithms.

scholarly journals Exploring the interpersonal collaboration during the integration of diabetes teams into primary care.

2021 ◽  
Author(s):  
Enza Gucciardi ◽  
Sherry Espin ◽  
Antonia Morganti ◽  
Linda Dorado
Keyword(s):  
Primary Care ◽  
Primary Care Physicians ◽  
Diabetes Management ◽  
Interprofessional Collaboration ◽  
Diabetes Education ◽  
Knowledge Exchange ◽  
Management Training ◽  
New Ways Of Working ◽  
Positive Effects ◽  
Diabetes Educators

Background Specialised diabetes teams, specifically certified nurse and dietitian diabetes educator teams, are being integrated part-time into primary care to provide better care and support for Canadians living with diabetes. This practice model is being implemented throughout Canada in an effort to increase patient access to diabetes education, self-management training, and support. Interprofessional collaboration can have positive effects on both health processes and patient health outcomes, but few studies have explored how health professionals are introduced to and transition into this kind of interprofessional work. Method Data from 18 interviews with diabetes educators, 16 primary care physicians, 23 educators’ reflective journals, and 10 quarterly debriefing sessions were coded and analysed using a directed content analysis approach, facilitated by NVIVO software. Results Four major themes emerged related to challenges faced, strategies adopted, and benefits observed during this transition into interprofessional collaboration between diabetes educators and primary care physicians: (a) negotiating space, place, and role; (b) fostering working relationships; (c) performing collectively; and (d) enhancing knowledge exchange. Conclusions Our findings provide insight into how healthcare professionals who have not traditionally worked together in primary care are collaborating to integrate health services essential for diabetes management. Based on the experiences and personal reflections of participants, establishing new ways of working requires negotiating space and place to practice, role clarification, and frequent and effective modes of formal and informal communication to nurture the development of trust and mutual respect, which are vital to success.

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