Predicting misdiagnosed adult-onset type 1 diabetes using machine learning
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 identif...
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| Veröffentlicht in: | Diabetes research and clinical practice Jg. 191; S. 110029 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Ireland
Elsevier B.V
01.09.2022
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| ISSN: | 0168-8227, 1872-8227, 1872-8227 |
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| Abstract | 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.
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.
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.
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. |
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| AbstractList | 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.
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.
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.
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. 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.AIMSIt 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.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.METHODSIn 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.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.RESULTSThe 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.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.CONCLUSIONSThis 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. |
| ArticleNumber | 110029 |
| Author | Cheheltani, Rabee Kovarik, Danny Leavitt, Nadejda Dutta, Sanjoy Lee, Suyin North, Benjamin Evans-Molina, Carmella King, Nicholas |
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| Cites_doi | 10.1007/s00125-014-3225-9 10.1007/s00125-019-4863-8 10.1093/bioinformatics/btn141 10.1016/S0168-8227(00)00173-X 10.1038/s41598-017-17433-8 10.1136/bmjhci-2021-100510 10.1038/nrg3208 10.1186/s41512-020-00075-2 10.1038/s41598-020-67013-6 10.1016/j.csbj.2016.12.005 10.2337/dc21-1752 10.2337/db18-1263 10.4258/hir.2019.25.4.248 10.1145/2939672.2939785 10.1373/clinchem.2010.148221 10.2337/cd18-0088 10.2337/dc12-0931 10.1016/j.cmpb.2017.09.004 10.1001/jamanetworkopen.2021.11315 10.1038/nrendo.2017.99 10.1089/big.2015.0020 10.2337/db12-0207 10.3346/jkms.2020.35.e425 |
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| Keywords | Misdiagnosis AI Clinical decision support tool Adult-onset type 1 diabetes Predictive algorithm Machine learning |
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| SubjectTerms | Adult Adult-onset type 1 diabetes Blood Glucose Clinical decision support tool Diabetes Mellitus, Type 1 - diagnosis Diabetes Mellitus, Type 1 - epidemiology Diabetes Mellitus, Type 2 - diagnosis Diabetes Mellitus, Type 2 - drug therapy Diabetes Mellitus, Type 2 - epidemiology Diagnostic Errors Glycated Hemoglobin Humans Machine Learning Misdiagnosis Predictive algorithm Retrospective Studies |
| Title | Predicting misdiagnosed adult-onset type 1 diabetes using machine learning |
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