Exploring the Clinical Notes of Pathology Ordering by Australian General Practitioners: a text mining perspective

Zhuang, Zoe Yan; Amarasiri, Rasika; Чурилов, Леонид; Alahakoon, Damminda; Sikaris, Ken

doi:10.1109/hicss.2007.220

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Some series of studies reported on multiple stages of a project, from AI development to pilot-testing; these projects included intended end users located in a primary care setting. [53][54][55][56][57][58][59][60] A small minority of studies (21 studies, 5.2%) had multiple purposes. Figure 2 presents all combinations.…”

Section: Study Purposementioning

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: Study Purposementioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

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“…There were series of studies that reported on multiple stages of a project, from AI development to pilot testing; these studies included intended end users located in a PC setting (49)(50)(51)(52)(53)(54)(55)(56). A minority of studies (n=21, 5.2%) had more than one study purpose.…”

Section: Searchesmentioning

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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“…GSOM is an unsupervised clustering algorithm that has been used for clustering data sets especially in bio informatics and text clustering applications [5,6]. In this paper, we employed the GSOM algorithm in order to discover any hidden patterns among stillbirths.…”

Section: Introductionmentioning

confidence: 99%

Analysing Stillbirth Data Using Dynamic Self Organizing Maps

Matharage

Alahakoon

et al. 2011

2011 22nd International Workshop on Database and Expert Systems Applications

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Even with the presence of modern obstetric care, stillbirth rate seems to stay stagnant or has even risen slightly in countries such as England and has become a significant public health concern [1]. In the light of current medical research, maternal risk factors such as diabetes and hypertensive disease were identified as possible risk factors and are taken into consideration in antenatal care. However, medical practitioners and researchers suspect possible relationships between trends in maternal demographics, antenatal care and pregnancy information of current stillbirth in consideration [2]. Although medical data and knowledge is available appropriate computing techniques to analyze the data may lead to identification of high risk groups. In this paper we use an unsupervised clustering technique called Growing Self organizing Map (GSOM) to analyse the stillbirth data and present patterns which can be important to medical researchers.

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Exploring the Clinical Notes of Pathology Ordering by Australian General Practitioners: a text mining perspective

Cited by 3 publications

References 13 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

Analysing Stillbirth Data Using Dynamic Self Organizing Maps

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