Development and Validation of an Electronic Health Record–Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment

Wong, Andrew; Young, A. E.; Liang, April S.; Gonzales, Ralph; Douglas, Vanja C.; Hadley, Dexter

doi:10.1001/jamanetworkopen.2018.1018

Cited by 120 publications

(109 citation statements)

References 32 publications

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“…Electronic health records are becoming platforms for deploying prognostic ML models, which have already been used in clinical care for sepsis, acute kidney injury, and delirium. [47][48][49] The next steps would be an electronic health record based study that would reliably identify patients presenting with acute UGIB, use structured datafields as predictive variables to develop models based on local patterns of disease and outcomes, and then prospectively validate the models in patients presenting in the emergency department with acute UGIB.…”

Section: Future Directionsmentioning

confidence: 99%

Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

et al. 2020

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Section: Future Directionsmentioning

confidence: 99%

Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

et al. 2020

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“…There are many applied studies comparing the performance of classic models to different machine learning algorithms [23][24][25][26][27][28][29][30][31][32][33][34] but their findings are inconsistent. Many such comparison studies have limitations; not all use non-default parameter settings (hyperparameter tuning) or have validated performance on external data [35].…”

Section: Introductionmentioning

confidence: 99%

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

et al. 2020

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Background: There is much interest in the use of prognostic and diagnostic prediction models in all areas of clinical medicine. The use of machine learning to improve prognostic and diagnostic accuracy in this area has been increasing at the expense of classic statistical models. Previous studies have compared performance between these two approaches but their findings are inconsistent and many have limitations. We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors is often limited, and externally validate the models. Methods: We trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes). We used seven predictor variables (age, BMI, GADA islet-autoantibodies, sex, total cholesterol, HDL cholesterol and triglyceride) using a UK cohort of adult participants (aged 18-50 years) with clinically diagnosed diabetes recruited from primary and secondary care (n = 960, 14% with type 1 diabetes). Discrimination performance (ROC AUC), calibration and decision curve analysis of each approach was compared in a separate external validation dataset (n = 504, 21% with type 1 diabetes). Results: Average performance obtained in internal validation was similar in all models (ROC AUC ≥ 0.94). In external validation, there were very modest reductions in discrimination with AUC ROC remaining ≥ 0.93 for all methods. Logistic regression had the numerically highest value in external validation (ROC AUC 0.95). Logistic regression had good performance in terms of calibration and decision curve analysis. Neural network and gradient boosting machine had the best calibration performance. Both logistic regression and support vector machine had good decision curve analysis for clinical useful threshold probabilities.

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“…Given the vast amount of data currently being accumulated in electric health records (EHR), the importance of ML is expected to increase. In other clinical areas, EHR is already used to deploy an ML approach for decision support [ 25 , 26 ]. Second, an ML approach can use more variables than is possible with conventional clinical scores.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Adverse Events in Stable Non-Variceal Gastrointestinal Bleeding Using Machine Learning

Seo

Park

et al. 2020

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Clinical risk-scoring systems are important for identifying patients with upper gastrointestinal bleeding (UGIB) who are at a high risk of hemodynamic instability. We developed an algorithm that predicts adverse events in patients with initially stable non-variceal UGIB using machine learning (ML). Using prospective observational registry, 1439 out of 3363 consecutive patients were enrolled. Primary outcomes included adverse events such as mortality, hypotension, and rebleeding within 7 days. Four machine learning algorithms, namely, logistic regression with regularization (LR), random forest classifier (RF), gradient boosting classifier (GB), and voting classifier (VC), were compared with the Glasgow–Blatchford score (GBS) and Rockall scores. The RF model showed the highest accuracies and significant improvement over conventional methods for predicting mortality (area under the curve: RF 0.917 vs. GBS 0.710), but the performance of the VC model was best in hypotension (VC 0.757 vs. GBS 0.668) and rebleeding within 7 days (VC 0.733 vs. GBS 0.694). Clinically significant variables including blood urea nitrogen, albumin, hemoglobin, platelet, prothrombin time, age, and lactate were identified by the global feature importance analysis. These results suggest that ML models will be useful early predictive tools for identifying high-risk patients with initially stable non-variceal UGIB admitted at an emergency department.

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Development and Validation of an Electronic Health Record–Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment

Cited by 120 publications

References 32 publications

Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

Prediction of Adverse Events in Stable Non-Variceal Gastrointestinal Bleeding Using Machine Learning

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