Predicting misdiagnosed adult-onset type 1 diabetes using machine learning

Cheheltani, Rabee; King, N.S.P.; Lee, Suyin; North, Benjamin; Kovarik, Danny; Evans‐Molina, Carmella; Leavitt, Nadejda; Dutta, Sanjoy

doi:10.1016/j.diabres.2022.110029

Cited by 14 publications

(7 citation statements)

References 24 publications

(26 reference statements)

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“…The ML algorithm showed an increased efficiency of screening for HCV compared with universal screening and risk-based approaches, where fewer patients are required to be screened with the algorithm to identify the equivalent number of HCV cases. This supports existing research that found ML algorithms trained on EMR data can be used to predict patients’ risk of disease with high precision 16–18. Moreover, this study demonstrates the utility of an EMR-based ML algorithm in identifying HCV patients and evidences a potential benefit in deployment into clinical workflow.…”

Section: Discussionsupporting

confidence: 88%

“…Previous work has demonstrated how ML can accurately identify undiagnosed HCV cases using US medical insurance claims and prescription data 16. Additionally, ML techniques applied to EMRs have been used for patient finding in other disease areas, such as type 1 diabetes and sepsis 17 18. Given the promise shown in applying ML to EMRs, we investigated whether undiagnosed HCV cases could be predicted by an ML algorithm using a US EMR data set.…”

Section: Introductionmentioning

confidence: 99%

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Finding undiagnosed patients with hepatitis C virus: an application of machine learning to US ambulatory electronic medical records

Rigg

Doyle

McDonogh

et al. 2023

BMJ Health Care Inform

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Aims To develop and validate a machine learning (ML) algorithm to identify undiagnosed hepatitis C virus (HCV) patients, in order to facilitate prioritisation of patients for targeted HCV screening. Methods This retrospective study used ambulatory electronic medical records (EMR) from January 2015 to February 2020. A Gradient Boosting Trees algorithm was trained using patient records to predict initial HCV diagnosis and was validated on a temporally independent held-out cross-section of the data. The fold improvement in precision (proportion of patients identified by the algorithm who are HCV positive) over universal screening was examined and compared with risk-based screening. Results 21 508 positive (HCV diagnosed) and 28.2M unlabelled (lacking evidence of HCV diagnosis) patients met the inclusion criteria for the study. After down-sampling unlabelled patients to aid the algorithm’s learning process, 16.2M unlabelled patients entered the analysis. Performance of the algorithm was compared with universal screening on the held-out cross-section, which had an incidence of HCV diagnoses of 0.02%. The algorithm achieved a 101.0 ×, 18.0 × and 5.1 × fold improvement in precision over universal screening at 5%, 20% and 50% levels of recall. When compared with risk-based screening, the algorithm required fewer patients to be screened and improved precision. Conclusions This study presents strong evidence towards the use of ML on EMR data for the prioritisation of patients for targeted HCV testing with potential to improve efficiency of resource utilisation, thereby reducing the workload for clinicians and saving healthcare costs. A prospective interventional study would allow for further validation before use in a clinical setting.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Finding undiagnosed patients with hepatitis C virus: an application of machine learning to US ambulatory electronic medical records

Rigg

Doyle

McDonogh

et al. 2023

BMJ Health Care Inform

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“…Распространенность LADA составляет 2-12% общего количества случаев СД [14]. По данным R. Cheheltani и соавт., у 10% пациентов с СД1 первично был диагностирован СД2 [15]. Вероятно, эти данные можно распространить и на популяцию пациентов с LADA, так как именно этот тип в дебюте протекает подобно СД2.…”

Section: дифференциальная диагностика Ladaunclassified

“…В 2022 г. R. Cheheltani и соавт. предложили модель, основанную на алгоритме градиентного бустинга (XGBoost) для выявления случаев СД1 среди пациентов с диагнозом СД2 [15]. Проводился ретроспективный анализ данных более 737 000 пациентов о возрасте, демографических показателях, факторах риска, симптомах, лечении, результатах физикального осмотра, лабораторных и инструментальных исследований.…”

Section: алгоритмы диагностики Ladaunclassified

Machine learning methods in the differential diagnosis of difficult-to-classify types of diabetes mellitus

Rusyaeva,

Golodnikov,

Kononenko

et al. 2023

Diabetes mellitus

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The course of difficult-to-classify types of diabetes mellitus (DM) (slowly developing immune-mediated DM of adults (LADA), monogenic forms of DM (MODY)) has common features with both type 1 DM (T1DM) and type 2 DM (T2DM), so often remain misdiagnosed. Errors in determining the type of diabetes lead to incorrect treatment tactics, which leads to poor glycemic control, the development of complications, a decrease in the patient's quality of life, and increased mortality.The key method for diagnosing MODY is sequencing of genes associated with this disease, and LADA is an immunological blood test in combination with the features of the clinical picture. However, the exact criteria for referring patients to these studies have not yet been determined. Performing these studies on all patients without exception with risk factors can lead to unjustified economic costs, and access to them is often difficult. In this regard, various automated algorithms have been developed based on statistical methods and machine learning (deep neural networks, “decision trees”, etc.) to identify patients for whom an in-depth examination is most justified. Among them are algorithms for the differential diagnosis of T1DM and T2DM, algorithms specializing in the diagnosis of only LADA or only MODY, only one algorithm is aimed at multiclass classification of patients with diabetes. One of the algorithms is widely used, aimed at diagnosing MODY in patients under the age of 35 years. However, existing algorithms have a number of disadvantages, such as: small sample size, exclusion of patients with MODY or older patients from the study, lack of verification of the diagnosis using appropriate studies, and the use of late complications of diabetes as parameters for diagnosis. Often the research team did not include practicing physicians. In addition, none of the algorithms are publicly available and have not been tested for patients in Russia. This manuscript presents an analysis of the main automated algorithms for the differential diagnosis of diabetes, developed in recent years.

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“…An XGBoost model was developed to recognize T1DM subjects misdiagnosed as T2DM using Ambulatory Electronic Medical Records (AEMR) data [ 73 ]. The model identified BMI/weight, age, HbA1c/blood glucose values, and therapy history as top predictors of misdiagnosis.…”

Section: The Application Of ML and Dl Models For The Management Predi...mentioning

confidence: 99%

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Afsaneh¹,

Sharifdini²,

Ghazzaghi³

et al. 2022

Diabetol Metab Syndr

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Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

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Predicting misdiagnosed adult-onset type 1 diabetes using machine learning

Cited by 14 publications

References 24 publications

Finding undiagnosed patients with hepatitis C virus: an application of machine learning to US ambulatory electronic medical records

Finding undiagnosed patients with hepatitis C virus: an application of machine learning to US ambulatory electronic medical records

Machine learning methods in the differential diagnosis of difficult-to-classify types of diabetes mellitus

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

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