A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients

Ryan, Logan; Mataraso, Samson; Siefkas, Anna; Pellegrini, Emily; Barnes, Gina L.; Green‐Saxena, Abigail; Hoffman, Jana; Calvert, Jacob; Das, Ritankar

doi:10.1177/1076029621991185

Cited by 31 publications

(25 citation statements)

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“…Successive iterations of trees use gradient descent on the prior trees to minimize the error of the next tree that was formed. XGBoosting has been shown to exhibit excellent performance for a wide range of classification problems in acute and chronic conditions [ 44 - 48 ]. For comparison with the structurally complex XGBoosting model, logistic regression and multilayered perceptron models were also trained and tested.…”

Section: Methodsmentioning

confidence: 99%

Predicting Falls in Long-term Care Facilities: Machine Learning Study

Thapa¹,

Garikipati²,

Shokouhi³

et al. 2022

JMIR Aging

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Background Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. Objective The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care. Methods This retrospective study obtained EHR data (2007-2021) from Juniper Communities’ proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities’ fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models. Results The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident’s number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone. Conclusions This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.

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Section: Methodsmentioning

confidence: 99%