Prediction of progression from pre‐diabetes to diabetes: Development and validation of a machine learning model

Cahn, Avivit; Shoshan, Avi; Sagiv, Tal; Yesharim, Rachel; Goshen, Ran; Shalev, Varda

doi:10.1002/dmrr.3252

Cited by 76 publications

(61 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Machine learning has unique advantages, including scalability and flexibility, making it applicable to various tasks, such as classification, risk stratification, diagnosis and survival predictions (46). Besides, it handles large multidimensional sets of time-to-event data without the need for assumptions of normality of distributions, linearity of risk prediction, and overfitting of models (47). As yet, machine learning techniques have been applied to a broad range of areas within diabetes, some of which are used to build risk prediction models for incident diabetes (20,21,(48)(49)(50)(51)(52).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults

Chen

et al. 2021

Front. Public Health

View full text Add to dashboard Cite

Purpose: We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese adults.Methods: A 3-year cohort study was performed on 15,928 Chinese adults without diabetes at baseline. All participants were randomly divided into a training set (n = 7,940) and a validation set (n = 7,988). XGBoost method is an effective machine learning technique used to select the most important variables from candidate variables. And we further established a stepwise model based on the predictors chosen by the XGBoost model. The area under the receiver operating characteristic curve (AUC), decision curve and calibration analysis were used to assess discrimination, clinical use and calibration of the model, respectively. The external validation was performed on a cohort of 11,113 Japanese participants.Result: In the training and validation sets, 148 and 145 incident diabetes cases occurred. XGBoost methods selected the 10 most important variables from 15 candidate variables. Fasting plasma glucose (FPG), body mass index (BMI) and age were the top 3 important variables. And we further established a stepwise model and a prediction nomogram. The AUCs of the stepwise model were 0.933 and 0.910 in the training and validation sets, respectively. The Hosmer-Lemeshow test showed a perfect fit between the predicted diabetes risk and the observed diabetes risk (p = 0.068 for the training set, p = 0.165 for the validation set). Decision curve analysis presented the clinical use of the stepwise model and there was a wide range of alternative threshold probability spectrum. And there were almost no the interactions between these predictors (most P-values for interaction >0.05). Furthermore, the AUC for the external validation set was 0.830, and the Hosmer-Lemeshow test for the external validation set showed no statistically significant difference between the predicted diabetes risk and observed diabetes risk (P = 0.824).Conclusion: We established and validated a risk assessment system for characterizing the 3-year risk of incident diabetes.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults

Chen

et al. 2021

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…A detailed description of our approach to developing a model based on EMR data is given elsewhere 7,8 . For training the ILI-based model, a training set of all MHS members at September 1 st of every calendar year who were not vaccinated during the following flu-season.…”

Section: Methodsmentioning

confidence: 99%

Predicting individual risk for COVID19 complications using EMR data

Kinar

Lanyado

Shoshan

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: The global pandemic of COVID-19 has challenged healthcare organizations and caused numerous deaths and hospitalizations worldwide. The need for data-based decision support tools for many aspects of controlling and treating the disease is evident but has been hampered by the scarcity of real-world reliable data. Here we describe two approaches: a. the use of an existing EMR-based model for predicting complications due to influenza combined with available epidemiological data to create a model that identifies individuals at high risk to develop complications due to COVID-19 and b. a preliminary model that is trained using existing real world COVID-19 data. Methods: We have utilized the computerized data of Maccabi Healthcare Services a 2.3 million member state-mandated health organization in Israel. The age and sex matched matrix used for training the XGBoost ILI-based model included, circa 690,000 rows and 900 features. The available dataset for COVID-based model included a total 2137 SARS-CoV-2 positive individuals who were either not hospitalized (n=1658), or hospitalized and marked as mild (n=332), or as having moderate (n=83) or severe (n=64) complications. Findings: The AUC of our models and the priors on the 2137 COVID-19 patients for predicting moderate and severe complications as cases and all other as controls, the AUC for the ILI-based model was 0.852[0.824-0.879] for the COVID19-based model - 0.872[0.847-0.879].. Interpretation: These models can effectively identify patients at high-risk for complication, thus allowing optimization of resources and more focused follow up and early triage these patients if once symptoms worsen.

show abstract

“…So wurde z. B. ein "boosted trees model" unter Verwendung klinischer Daten aus einer Patientenkohorte erstellt, die aus > 800.000 Individuen bestand, um das Risiko der Entwicklung von Diabetes ab dem Prädiabetesstadium mit hoher Genauigkeit über viele Datensätze hinweg vorherzusagen [44]. Des Weiteren wurden mehrere lineare und nichtlineare ML-Klassifikatoren zur Vorhersage der Wiederein-weisungswahrscheinlichkeitvonDiabetespatienten erstellt [45], von welchen wichtiger Risikofaktoren abgeleitet wurden, die zu einem hohen Wiedereinweisungsrisiko beitragen.…”

Section: Risikovorhersage Und Diagnoseunclassified

Künstliche Intelligenz und maschinelles Lernen in der Diabetesforschung

et al. 2021

View full text Add to dashboard Cite

In diesem Beitrag-Merkmale von Diabetes -Künstliche Intelligenz und maschinelles Lernen -Künstliche Intelligenz/maschinelles Lernen in der Diabetesforschung Risikovorhersage und Diagnose • Ergebnisvorhersage und Prognose • Mechanistisches Verständnis durch Omics-Studien in Menschen und Modellsystemen • Vorhersage von Diabeteskomplikationen -Herausforderungen und Zukunftsaussichten QR-Code scannen & Beitrag online lesen Zusammenfassung Hintergrund: Diabetes mellitus entwickelt sich zu einem globalen Gesundheitsproblem, das eine Transformation der Forschung und der medizinischen Praxis für ein besseres Patientenmanagement erfordert. Diesbezüglich bieten die Fülle an Daten und die Fortschritte in der Technologie und der künstlichen Intelligenz Möglichkeiten für ein solches Unterfangen. Ziele: Diese Übersichtsarbeit soll einen Überblick über künstliche Intelligenz und die aktuelle Forschung in ihrer Anwendung im Bereich Diabetes geben, insbesondere zur Risikovorhersage, Diagnose, Prognose und Vorhersage von Komplikationen. Fazit: Künstliche Intelligenz transformiert die Diabetesforschung in vielen technischen und organisatorischen Aspekten. Obwohl ihr Einsatz noch begrenzt und mit vielen Herausforderungen konfrontiert ist, wird sie wahrscheinlich künftig die medizinische Behandlung beeinflussen, indem sie eine automatisierte und personalisierte Gesundheitsversorgung für Erkrankte bietet. Schlüsselwörter Computergestützte Intelligenz • Algorithmen • Biostatistik • Computeranwendungen in der Biologie • Diabetes mellitus

show abstract

Prediction of progression from pre‐diabetes to diabetes: Development and validation of a machine learning model

Cited by 76 publications

References 22 publications

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults

Predicting individual risk for COVID19 complications using EMR data

Künstliche Intelligenz und maschinelles Lernen in der Diabetesforschung

Contact Info

Product

Resources

About