Prediction Modeling Using EHR Data

Wu, Jionglin; Roy, Jason; Stewart, Walter F.

doi:10.1097/mlr.0b013e3181de9e17

Cited by 304 publications

(100 citation statements)

References 7 publications

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“…Data-driven modelling has both practical and theoretical advantages over conventional survival modelling [5, 24]. It may require less user input, through automated variable selection.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have used machine learning on EHR data for tasks such as patient classification and diagnosis [22–24] or predicting future hospitalisation [25], but we are unaware of any systematic comparison of machine learning methods for predicting all-cause mortality in a large, richly characterised EHR cohort of patients with stable coronary artery disease.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

et al. 2018

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Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selection of variables, fine-tuning of variable transformations and interactions, and imputing missing values are time-consuming and could bias subsequent analysis, particularly given that missingness in EHR is both high, and may carry meaning. Using a cohort of 80,000 patients from the CALIBER programme, we compared traditional modelling and machine-learning approaches in EHR. First, we used Cox models and random survival forests with and without imputation on 27 expert-selected, preprocessed variables to predict all-cause mortality. We then used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input. We observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values. An elastic net Cox regression based with 586 unimputed variables with continuous values discretised achieved a C-index of 0.801 (bootstrapped 95% CI 0.799 to 0.802), compared to 0.793 (0.791 to 0.794) for a traditional Cox model comprising 27 expert-selected variables with imputation for missing values. We also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate. This demonstrates that machine-learning approaches applied to raw EHR data can be used to build models for use in research and clinical practice, and identify novel predictive variables and their effects to inform future research.

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“…Data-driven modelling has both practical and theoretical advantages over conventional survival modelling [5, 24]. It may require less user input, through automated variable selection.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

et al. 2018

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“…Wu et al, comparing the performance of logistic regression, boosting, and support vector machines to predict the subsequent development of HF, found that the former two methods had comparable performance, while the latter method had the poorest performance [34]. Maroco et al, compared ten different classifiers for predicting the evolution of mild cognitive impairment to dementia [35].…”

Section: Discussionmentioning

confidence: 99%

Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes

Austin

et al. 2013

Journal of Clinical Epidemiology

257

101

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Objective Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. Study design and Setting We compared the performance of these classification methods with those of conventional classification trees to classify patients with heart failure according to the following sub-types: heart failure with preserved ejection fraction (HFPEF) vs. heart failure with reduced ejection fraction (HFREF). We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. Results We found that modern, flexible tree-based methods from the data mining literature offer substantial improvement in prediction and classification of heart failure sub-type compared to conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared to the methods proposed in the data mining literature. Conclusion The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying heart failure subtypes in a population-based sample of patients from Ontario. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF.

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“…EHR data are routinely collected for all patients at admission, and many studies have used them for prediction of health outcomes during and after the hospital stay (14). Previous works that have used EHR data for delirium prediction, have mainly used multivariate regression models due to their ease of interpretation and analysis (12, 13).…”

Section: Introductionmentioning

confidence: 99%

Delirium Prediction using Machine Learning Models on Predictive Electronic Health Records Data

Davoudi

Ozrazgat‐Baslanti

Ebadi

et al. 2017

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)

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Electronic Health Records (EHR) are mainly designed to record relevant patient information during their stay in the hospital for administrative purposes. They additionally provide an efficient and inexpensive source of data for medical research, such as patient outcome prediction. In this study, we used preoperative Electronic Health Records to predict postoperative delirium. We compared the performance of seven machine learning models on delirium prediction: linear models, generalized additive models, random forests, support vector machine, neural networks, and extreme gradient boosting. Among the models evaluated in this study, random forests and generalized additive model outperformed the other models in terms of the overall performance metrics for prediction of delirium, particularly with respect to sensitivity. We found that age, alcohol or drug abuse, socioeconomic status, underlying medical issue, severity of medical problem, and attending surgeon can affect the risk of delirium.

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Prediction Modeling Using EHR Data

Abstract: Heart failure was predicted more than 6 months before clinical diagnosis, with AUC of about 0.76, using logistic regression and Boosting. These results were achieved even with strict model selection criteria. SVM had the poorest performance, possibly because of imbalanced data.

Cited by 304 publications

References 7 publications

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes

Delirium Prediction using Machine Learning Models on Predictive Electronic Health Records Data

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