Development and validation of an automated algorithm for identifying patients at high risk for drug-induced hypoglycemia

Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients

“…We extracted candidate predictors from the EMR based on clinical knowledge and prior studies. 14,16,[18][19][20] All predictor variables were collected from the index admission, although some variables were from prior admissions. eTable 1 in the Supplement gives the definitions of each candidate predictor variable.…”

“…To our knowledge, this prediction model is the first to use such a nearterm prediction horizon without reliance on continuous BG monitoring, increasing the generalizability of the model for use in a large number of hospitalized patients. Unlike other machine learning models that have been developed for prediction of inpatient hypoglycemia,14,19,20 theFigure 3. Prediction Horizon for a Sample Patient in the Data Set, Showing Only 7 of the 43 Predictor Variables in the Model…”

Identifying patients at increased risk of hypoglycaemia in primary care: Development of a machine learning‐based screening tool

“…Several models have been developed for predicting hypoglycaemia. 24 , 25 , 26 , 27 , 28 , 29 , 30 However, these models mainly focus on the inpatient setting or do not make a distinction between type 1 and T2D. Our model is intended for primary care using demographic and medication data that are widely available to screen for T2D patients with an increased risk of hypoglycaemia.…”

“…The performance of our model, which had an AUC of 0.71, is considered acceptable 42 and comparable with several previously developed models, 24 , 25 , 26 although some models have shown higher AUCs than ours. 28 , 29 , 30 , 43 The higher performance of these models may be due to the availability of richer data in clinical trials and inpatient settings in comparison to data that are routinely available in outpatient settings. For example, daily glucose measurements may be available for diabetes patients who are admitted to a hospital but not for T2D patients in primary care.…”

“…Surprisingly, lower age contributed to an increased risk of hypoglycaemia in our models, whereas in other models, higher age was predictive of hypoglycaemic events. 26 , 28 , 29 , 30 This difference might be due to differences in the severity of the hypoglycaemia events used as outcomes. Previous studies mainly used severe hypoglycaemic events as the outcome, while we included any events regardless of severity.…”