Dataset Information

Predicting misdiagnosed adult-onset type 1 diabetes using machine learning.

ABSTRACT:

Aims

It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes.

Methods

In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical history.

Results

The model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in AEMR.

Conclusions

This algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.

SUBMITTER: Cheheltani R

PROVIDER: S-EPMC10631495 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Predicting misdiagnosed adult-onset type 1 diabetes using machine learning.

Cheheltani Rabee R King Nicholas N Lee Suyin S North Benjamin B Kovarik Danny D Evans-Molina Carmella C Leavitt Nadejda N Dutta Sanjoy S

Diabetes research and clinical practice 20220805

<h4>Aims</h4>It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes.<h4>Methods</h4>In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes pat ...[more]

PMID: 35940302

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Project description:ImportanceSystems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions.ObjectiveTo develop and validate a population-level machine learning model for predicting type 2 diabetes 5 years before diabetes onset using administrative health data.Design, setting, and participantsThis decision analytical model study used linked administrative health data from the diverse, single-payer health system in Ontario, Canada, between January 1, 2006, and December 31, 2016. A gradient boosting decision tree model was trained on data from 1 657 395 patients, validated on 243 442 patients, and tested on 236 506 patients. Costs associated with each patient were estimated using a validated costing algorithm. Data were analyzed from January 1, 2006, to December 31, 2016.ExposuresA random sample of 2 137 343 residents of Ontario without type 2 diabetes was obtained at study start time. More than 300 features from data sets capturing demographic information, laboratory measurements, drug benefits, health care system interactions, social determinants of health, and ambulatory care and hospitalization records were compiled over 2-year patient medical histories to generate quarterly predictions.Main outcomes and measuresDiscrimination was assessed using the area under the receiver operating characteristic curve statistic, and calibration was assessed visually using calibration plots. Feature contribution was assessed with Shapley values. Costs were estimated in 2020 US dollars.ResultsThis study trained a gradient boosting decision tree model on data from 1 657 395 patients (12 900 257 instances; 6 666 662 women [51.7%]). The developed model achieved a test area under the curve of 80.26 (range, 80.21-80.29), demonstrated good calibration, and was robust to sex, immigration status, area-level marginalization with regard to material deprivation and race/ethnicity, and low contact with the health care system. The top 5% of patients predicted as high risk by the model represented 26% of the total annual diabetes cost in Ontario.Conclusions and relevanceIn this decision analytical model study, a machine learning model approach accurately predicted the incidence of diabetes in the population using routinely collected health administrative data. These results suggest that the model could be used to inform decision-making for population health planning and diabetes prevention.

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Dataset Information

Predicting misdiagnosed adult-onset type 1 diabetes using machine learning.

Aims

Methods

Results

Conclusions

Publications

Predicting misdiagnosed adult-onset type 1 diabetes using machine learning.

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