Fuzzy Petri nets based decision support system for ambulatory treatment of non-severe acute diseases

Aştilean, Adina; Avram, Camelia; Folea, Silviu; Silvăşan, Iuliana; Petreuş, Dorin

doi:10.1109/aqtr.2010.5520706

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…We developed the data extraction sheet iteratively to ensure relevant and consistent information capture, performing pilot-testing and revisions for 3 and then 5 randomly selected articles. 31,[45][46][47][48][49][50][51] Remaining documents were split alphabetically and extracted independently (100 by A.L.T., 50 by D.J.L., 250 by J.K.K.). We extracted the following information: publication details, study purpose(s), author appointment(s), primary care function(s), author-intended target end user(s), target health condition(s), location of data source(s) (if any), AI subfield(s), the reviewer who performed extraction, and any reviewer notes.…”

Section: Data Extraction and Synthesismentioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

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PURPOSE Rapid increases in technology and data motivate the application of artificial intelligence (AI) to primary care, but no comprehensive review exists to guide these efforts. Our objective was to assess the nature and extent of the body of research on AI for primary care. METHODSWe performed a scoping review, searching 11 published or gray literature databases with terms pertaining to AI (eg, machine learning, bayes* network) and primary care (eg, general pract*, nurse). We performed title and abstract and then full-text screening using Covidence. Studies had to involve research, include both AI and primary care, and be published in English. We extracted data and summarized studies by 7 attributes: purpose(s); author appointment(s); primary care function(s); intended end user(s); health condition(s); geographic location of data source; and AI subfield(s). RESULTSOf 5,515 unique documents, 405 met eligibility criteria. The body of research focused on developing or modifying AI methods (66.7%) to support physician diagnostic or treatment recommendations (36.5% and 13.8%), for chronic conditions, using data from higher-income countries. Few studies (14.1%) had even a single author with a primary care appointment. The predominant AI subfields were supervised machine learning (40.0%) and expert systems (22.2%).CONCLUSIONS Research on AI for primary care is at an early stage of maturity. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed. Ann Fam Med 2020;18:250-258. https://doi.

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Section: Data Extraction and Synthesismentioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

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“…Data Extraction and Synthesis. An initial data extraction form was discussed amongst all reviewers (JKK, ALT, MZ, DJL) after pilot testing extraction of three randomly selected articles (41)(42)(43). A revised data extraction sheet was pilot tested by three reviewers (JKK, ALT, DJL) with five new randomly selected articles (25,(44)(45)(46)(47).…”

Section: Methodsmentioning

confidence: 99%

Research on Artificial Intelligence and Primary Care: A Scoping Review

Kueper

Terry

Zwarenstein

et al. 2019

Preprint

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Objective: The purpose of this study was to assess the nature and extent of the body of research on artificial intelligence (AI) and primary care. Methods:We performed a scoping review, searching 11 published and grey literature databases with subject headings and key words pertaining to the concepts of 1) AI and 2) primary care: MEDLINE, EMBASE, Cinahl, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, arXiv. Screening included title and abstract and then full text stages. Final inclusion criteria: 1) research study of any design, 2) developed or used AI, 3) used primary care data and/or study conducted in a primary care setting and/or explicit mention of study applicability to primary care; exclusion criteria: 1) narrative, editorial, or textbook chapter, 2) not applicable to primary care population or settings, 3) full text inaccessible in the English Language. We extracted and summarized seven key characteristics of included studies: overall study purpose(s), author appointments, primary care functions, author intended target end user(s), target health condition(s), location of data source(s) (if any), subfield(s) of AI.Results: Of 5,515 non-duplicate documents, 405 met our eligibility criteria. The body of literature is primarily focused on creating novel AI methods or modifying existing AI methods to support physician diagnostic or treatment recommendations, for chronic conditions, using data from higher income countries. Meaningfully more studies had at least one author with a technology, engineering, or math appointment than with a primary care appointment (57 (14%) compared to 217 (54%)). Predominant AI subfields were supervised machine learning and expert systems. Discussion:Overall, AI research associated with primary care is at an early stage of maturity with respect to widespread implementation in practice settings. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed.

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“…In (Astilean et al, 2010) a flexible support system to offer warnings, therapies and recommendations for remote patient surveillance is presented. An inference mechanism based on Petri nets and Fuzzy theory was designed and experimentally implemented for diseases evolution supervising purposes.…”

Section: Related Workmentioning

confidence: 99%

Simulation of the ambulatory processes in the biggest Brazilian cardiology hospital: a petri net approach

Lima

Cortez

Schmidt

et al. 2021

Ind. Jour. Manag. & Prod.

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This paper presents a simulation of an ambulatory processes using timed Petri net (TPN). The simulation considers the flow of patients in the biggest Brazilian cardiology hospital. The TPN is used as a decision support system (DSS) to improve the processes, to reduce the waiting time of the patients in the ambulatory and in this way to assure a high-quality service to the patients. Simulations were carried out using the software Visual Object Net++. This is a free software and therefore the presented solution is a low-cost solution. Providing a low-cost solution has a huge importance in this work since the hospital is kept from the government efforts and operates with limited financial resources. The patients’ flow in the hospital can be faced as a service and the modelling and optimization of these services bring more efficiency to the system as well as improve the human factors involved. The results proved that some changes could be made in the processes to improve the performance of the system.

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Fuzzy Petri nets based decision support system for ambulatory treatment of non-severe acute diseases

Cited by 5 publications

References 12 publications

Artificial Intelligence and Primary Care Research: A Scoping Review

Artificial Intelligence and Primary Care Research: A Scoping Review

Research on Artificial Intelligence and Primary Care: A Scoping Review

Simulation of the ambulatory processes in the biggest Brazilian cardiology hospital: a petri net approach

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