What’s So Different about Big Data?. A Primer for Clinicians Trained to Think Epidemiologically

Iwashyna, Theodore J.; Liu, Ming

doi:10.1513/annalsats.201405-185as

Cited by 32 publications

(23 citation statements)

References 4 publications

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“…5,[12][13][14] Modern methods for prediction include non-parametric and tree-based methods that can handle large amounts of data available in the electronic health record (EHR). 15,16 These methods are comparable to parametric approaches with respect to their prediction performance. [16][17][18] This study aimed to identify a parsimonious predictive model of future COT among hospitalized patients not on COT in the 1 year preceding their hospitalization.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Future Chronic Opioid Use Among Hospitalized Patients

et al. 2018

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Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.

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Section: Introductionmentioning

confidence: 99%

Prediction of Future Chronic Opioid Use Among Hospitalized Patients

et al. 2018

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“…” Companies like Google, Amazon, and Netflix have leveraged big data in concert with complex algorithms to improve predictions of human behavior and events (2). These algorithms, known as machine learning in computer science, are flexible techniques designed to learn and generalize from data.…”

Section: Introductionmentioning

confidence: 99%

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards

Churpek

Yuen

Winslow

et al. 2016

Critical Care Medicine

477

437

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OBJECTIVE Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. DESIGN Observational cohort study. SETTING Five hospitals, from November 2008 until January 2013. PATIENTS Hospitalized ward patients INTERVENTIONS None MEASUREMENTS AND MAIN RESULTS Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score (MEWS), a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC 0.80 [95% CI 0.80–0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC 0.77 vs 0.74; p<0.01), and all models were more accurate than the MEWS (AUC 0.70 [95% CI 0.70–0.70]). CONCLUSIONS In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards.

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“…The availability of larger and more detailed databases of critically ill adults is heralding a big-data revolution in critical care [ 6 , 29 , 30 ]. These highly granular clinical data might significantly bolster observational research in critical care by improving the accuracy of clinical measurement.…”

Section: Discussionmentioning

confidence: 99%

When do confounding by indication and inadequate risk adjustment bias critical care studies? A simulation study

et al. 2015

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IntroductionIn critical care observational studies, when clinicians administer different treatments to sicker patients, any treatment comparisons will be confounded by differences in severity of illness between patients. We sought to investigate the extent that observational studies assessing treatments are at risk of incorrectly concluding such treatments are ineffective or even harmful due to inadequate risk adjustment.MethodsWe performed Monte Carlo simulations of observational studies evaluating the effect of a hypothetical treatment on mortality in critically ill patients. We set the treatment to have either no association with mortality or to have a truly beneficial effect, but more often administered to sicker patients. We varied the strength of the treatment’s true effect, strength of confounding, study size, patient population, and accuracy of the severity of illness risk-adjustment (area under the receiver operator characteristics curve, AUROC). We measured rates in which studies made inaccurate conclusions about the treatment’s true effect due to confounding, and the measured odds ratios for mortality for such false associations.ResultsSimulated observational studies employing adequate risk-adjustment were generally able to measure a treatment’s true effect. As risk-adjustment worsened, rates of studies incorrectly concluding the treatment provided no benefit or harm increased, especially when sample size was large (n = 10,000). Even in scenarios of only low confounding, studies using the lower accuracy risk-adjustors (AUROC < 0.66) falsely concluded that a beneficial treatment was harmful. Measured odds ratios for mortality of 1.4 or higher were possible when the treatment’s true beneficial effect was an odds ratio for mortality of 0.6 or 0.8.ConclusionsLarge observational studies confounded by severity of illness have a high likelihood of obtaining incorrect results even after employing conventionally “acceptable” levels of risk-adjustment, with large effect sizes that may be construed as true associations. Reporting the AUROC of the risk-adjustment used in the analysis may facilitate an evaluation of a study’s risk for confounding.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-015-0923-8) contains supplementary material, which is available to authorized users.

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What’s So Different about Big Data?. A Primer for Clinicians Trained to Think Epidemiologically

Cited by 32 publications

References 4 publications

Prediction of Future Chronic Opioid Use Among Hospitalized Patients

Prediction of Future Chronic Opioid Use Among Hospitalized Patients

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards

When do confounding by indication and inadequate risk adjustment bias critical care studies? A simulation study

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