Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review

Chaki, Jyotismita; Ganesh, S. Thillai; Cidham, S.K; Theertan, S. Ananda

doi:10.1016/j.jksuci.2020.06.013

Cited by 146 publications

(97 citation statements)

References 137 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, adherence to T2D medications to achieve euglycemia is demonstrably driven by cultural beliefs, values, social factors, religion, health literacy, and language barriers [ 95 , 96 ]. Similar issues are likely to follow in the T1D space, where AI algorithms are currently focused on automated insulin delivery systems but will likely shift toward the above dimensions in the near future [ 63 , 97 , 98 ].…”

Section: Discussionmentioning

confidence: 99%

The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations

et al. 2021

View full text Add to dashboard Cite

There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.

show abstract

Section: Discussionmentioning

confidence: 99%

The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Additionally, four reviews included primary studies based on imaging datasets and databanks, assessing different parameters of accuracy [15,29,31,36]. Other reviews focused on genetic databases [28,35], data from assisted reproductive technologies [30], or publicly available data [11,14,22,32]. Four studies lacked precision about the origin of the datasets used in their analysis or did not specifically use patient data in the investigation [23,37,39,41].…”

Section: Data Sources and Purposes Of Included Studiesmentioning

confidence: 99%

“…Most of the studies assessed the effects of big data analytics on noncommunicable diseases [12][13][14][15]17,21,22,24,27,31,32,34,36,38,[40][41][42][43][44]. Furthermore, three reviews covered mental health, associated with the indicator "suicide mortality rate" [19,25,45]; three studies were related to the indicator "probability of dying from any of cardiovascular, cancer, diabetes, or chronic renal disease" [16,18,20,28,29]; and two studies were related to the indicator "proportion of bloodstream infections due to antimicrobial-resistant organisms" [26,33].…”

Section: Who Indicators and Core Prioritiesmentioning

confidence: 99%

“…AI tools associated with big data analytics in the care of patients with diabetes mellitus (DM) were assessed in six reviews that included 345 primary studies [15,20,32,38,40]. Three studies reviewed AI in screening and diagnosing type 1 or type 2 DM, providing varied ranges of accuracy, sensitivity, and specificity [20,32,40]. Variables included systolic blood pressure, body mass index, triglyceride levels, and others.…”

Section: Diabetes Mellitusmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Nascimento¹,

Marcolino²,

Abdulazeem³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Background Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. Objective The aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health. Methods Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. Results The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. Conclusions Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048

show abstract

“…In line with this challenge, more researchers are working hard to address and create a system and tools that will predict diabetes more accurately. Prediction through Machine Learning (ML) which is a branch of Artificial Intelligence, "is a system that allows the computers to learn and gain intelligence based on experience with the development of algorithms" [11], [14]. AI has played of a lot of roles in various fields and now a "key point of focus applied in various medical specialized areas" [12].…”

Section: Introductionmentioning

confidence: 99%

Classifier Algorithms and Ensemble Models for Diabetes Mellitus Prediction: A Review

2021

IJATCSE

View full text Add to dashboard Cite

Timely disease prediction means a lot to the improvement of the health care services and this will go a long way to assist populaces to avoid unsafe health circumstances before resulting in complex medical situations. Diabetes Mellitus as one of the deadly diseases that is described by hyperglycemia taking place due to defects in insulin secretion which allow an irregular increase in glucose level. Diabetes Mellitus can lead to loss of sight, non-traumatic lower extremity amputation, chronic kidney disease, coronary heart disease, stroke, etc. Hence, prompt diagnosis of the Diabetes Mellitus disease has to pay more attention to in the recent area of research. Presently, there is a great and wide work carried out on Machine Learning with a focus on medical and its application. This paper reviewed recent journalsthat made use of Artificial Intelligencetechniques, different classifiers and ensemble methods to assist in the management of diabetes. Classifiers algorithms such as Naive Bayes, Decision Tree, Artificial Neural Network, Support Vector Machine, K-Nearest Neighbour and Multi-Layer Perceptron. The results from over 1,137most related reviewed journalsreveled that Ensemble Models have the highest average accuracy of 87.09 % in respect to prediction of diabetes mellitus

show abstract

Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review

Cited by 146 publications

References 137 publications

The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations

The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Classifier Algorithms and Ensemble Models for Diabetes Mellitus Prediction: A Review

Contact Info

Product

Resources

About