Sonsoles Fuentes , Rok Hrzic , Romana Haneef , Sofiane Kab , Emmanuel Cosson , Sandrine Fosse-Edorh
{"title":"在医疗管理数据库中识别1/2型糖尿病以改善糖尿病的健康监测、医学研究和预防：算法开发和应用","authors":"Sonsoles Fuentes , Rok Hrzic , Romana Haneef , Sofiane Kab , Emmanuel Cosson , Sandrine Fosse-Edorh","doi":"10.1016/j.deman.2023.100137","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>Big data sources represent an opportunity for diabetes research. One example is the French national health data system (SNDS), gathering information on medical claims of out-of-hospital health care and hospitalizations for the entire French population (66 million). Currently, a validated algorithm based on antidiabetic drug reimbursement is able to identify people with pharmacologically-treated diabetes in the SNDS. But it cannot distinguish type 1 from type 2 diabetes. Differentiating type 1 and type 2 diabetes is crucial in diabetes surveillance, because they carry differences in their prevention, populations at risk, disease natural history, pathophysiology, management and risk of complications.</p><p>This article investigates the development of a type 1/type 2 diabetes classification algorithm using artificial intelligence and its application to estimate the prevalence of type 1 and type 2 diabetes in France.</p></div><div><h3>Methods</h3><p>The final data set comprised all diabetes cases from the CONSTANCES cohort (<em>n</em> = 951). A supervised machine learning method based on eight steps was used: final data set selection, target definition (type 1), coding features, final data set splitting into training and testing data sets, feature selection and training and validation and selection of algorithms. The selected algorithm was applied to SNDS data to estimate the type 1 and type 2 diabetes prevalence among adults 18–70 years of age.</p></div><div><h3>Results</h3><p>Among the 3481 SNDS features, 14 were selected to train the different algorithms. The final algorithm was a linear discriminant analysis model based on the number of reimbursements for fast-acting insulin, long-acting insulin and biguanides over the previous year (specificity 97% and sensitivity 100%). In 2016, after adjusting for algorithm performance, type 1 and type 2 diabetes prevalence in France was estimated to be 0.3% and 4.4%, respectively.</p></div><div><h3>Conclusion</h3><p>Our type 1/type 2 classification algorithm was found to perform well and to be applicable to any prescription or medical claims database from other countries. Artificial intelligence opens new possibilities for research and diabetes prevention.</p></div>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying type 1 / type 2 diabetes in medico-administrative database to improve health surveillance, medical research and prevention in diabetes: Algorithm development and application\",\"authors\":\"Sonsoles Fuentes , Rok Hrzic , Romana Haneef , Sofiane Kab , Emmanuel Cosson , Sandrine Fosse-Edorh\",\"doi\":\"10.1016/j.deman.2023.100137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><p>Big data sources represent an opportunity for diabetes research. One example is the French national health data system (SNDS), gathering information on medical claims of out-of-hospital health care and hospitalizations for the entire French population (66 million). Currently, a validated algorithm based on antidiabetic drug reimbursement is able to identify people with pharmacologically-treated diabetes in the SNDS. But it cannot distinguish type 1 from type 2 diabetes. Differentiating type 1 and type 2 diabetes is crucial in diabetes surveillance, because they carry differences in their prevention, populations at risk, disease natural history, pathophysiology, management and risk of complications.</p><p>This article investigates the development of a type 1/type 2 diabetes classification algorithm using artificial intelligence and its application to estimate the prevalence of type 1 and type 2 diabetes in France.</p></div><div><h3>Methods</h3><p>The final data set comprised all diabetes cases from the CONSTANCES cohort (<em>n</em> = 951). A supervised machine learning method based on eight steps was used: final data set selection, target definition (type 1), coding features, final data set splitting into training and testing data sets, feature selection and training and validation and selection of algorithms. The selected algorithm was applied to SNDS data to estimate the type 1 and type 2 diabetes prevalence among adults 18–70 years of age.</p></div><div><h3>Results</h3><p>Among the 3481 SNDS features, 14 were selected to train the different algorithms. The final algorithm was a linear discriminant analysis model based on the number of reimbursements for fast-acting insulin, long-acting insulin and biguanides over the previous year (specificity 97% and sensitivity 100%). In 2016, after adjusting for algorithm performance, type 1 and type 2 diabetes prevalence in France was estimated to be 0.3% and 4.4%, respectively.</p></div><div><h3>Conclusion</h3><p>Our type 1/type 2 classification algorithm was found to perform well and to be applicable to any prescription or medical claims database from other countries. Artificial intelligence opens new possibilities for research and diabetes prevention.</p></div>\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666970623000100\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666970623000100","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Identifying type 1 / type 2 diabetes in medico-administrative database to improve health surveillance, medical research and prevention in diabetes: Algorithm development and application
Introduction
Big data sources represent an opportunity for diabetes research. One example is the French national health data system (SNDS), gathering information on medical claims of out-of-hospital health care and hospitalizations for the entire French population (66 million). Currently, a validated algorithm based on antidiabetic drug reimbursement is able to identify people with pharmacologically-treated diabetes in the SNDS. But it cannot distinguish type 1 from type 2 diabetes. Differentiating type 1 and type 2 diabetes is crucial in diabetes surveillance, because they carry differences in their prevention, populations at risk, disease natural history, pathophysiology, management and risk of complications.
This article investigates the development of a type 1/type 2 diabetes classification algorithm using artificial intelligence and its application to estimate the prevalence of type 1 and type 2 diabetes in France.
Methods
The final data set comprised all diabetes cases from the CONSTANCES cohort (n = 951). A supervised machine learning method based on eight steps was used: final data set selection, target definition (type 1), coding features, final data set splitting into training and testing data sets, feature selection and training and validation and selection of algorithms. The selected algorithm was applied to SNDS data to estimate the type 1 and type 2 diabetes prevalence among adults 18–70 years of age.
Results
Among the 3481 SNDS features, 14 were selected to train the different algorithms. The final algorithm was a linear discriminant analysis model based on the number of reimbursements for fast-acting insulin, long-acting insulin and biguanides over the previous year (specificity 97% and sensitivity 100%). In 2016, after adjusting for algorithm performance, type 1 and type 2 diabetes prevalence in France was estimated to be 0.3% and 4.4%, respectively.
Conclusion
Our type 1/type 2 classification algorithm was found to perform well and to be applicable to any prescription or medical claims database from other countries. Artificial intelligence opens new possibilities for research and diabetes prevention.
期刊介绍：
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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