![]() The neural network 24 can ‘learn’ to classify samples without manually designed task-specific rules. The ability of random forests to learn non-linear and complex functions contributes to its predictive performance. Each observation is classified by each tree, and the majority classification over all trees is the predicted class. Many such trees are grown, creating a ‘forest’. Instead of considering all predictors to determine the splitting criterion at a node, the split variable is chosen from a random subset of variables in order to reduce the correlation between different trees. 22 23 For each decision tree, the original data are bootstrapped to create a new data set of the same size and the tree is fit to the new data. Random forests is an ensemble machine learning method that aggregates the results of multiple decision trees fit on bootstrap samples of the original data. Statistical analysis and model development The development of this database was reviewed and approved by the Veterans Ann Arbor Healthcare System’s Institutional Review Board.įour versions of the data set were created for each hospitalisation on admission: (A) raw lab values extracted using only lab test names, (B) raw lab values extracted using only Logical Observation Identifiers Names and Codes (LOINC), (C) cleaned lab values extracted using both LOINC 16 17 and searched text lab test names and (D) cleaned lab values converted to Acute Physiology And Chronic Health Evaluation (APACHE) points, extracted using both LOINC and lab test names. Full details of the VAPD have been published elsewhere. Here, we included data from all ICU hospitalisations on day 1 of each hospitalisation. The VAPD includes patient demographics, laboratory results and diagnoses that are commonly used to predict 30-day mortality from the day of admission. We systematically applied these approaches in a 70% development sample and tested the results in an independent 30% testing sample to provide real-world comparisons to inform future pragmatic implementation of risk scores.ĭata were drawn from the Veterans Affairs Patient Database (VAPD), which contains daily patient physiology for acute hospitalisations between 1 January 2014 and 31 December 2017. 12–14 Using the same set of real ICU admissions, we systematically varied three parameters: the approach used to extract and clean physiologic variables from the electronic health record the approach used to handle missing data and the approach used to compute the risk. ![]() To address such questions, we compared the performance of an array of methods on a single-standardised problem-the prediction of 30-day mortality based on demographics, day 1 laboratory results, comorbidities and diagnoses among patients admitted to the intensive care unit (ICU) at any hospital in the nationwide Veterans Health Administration system. 11 Yet questions remain on fundamental pragmatic issues: How clean does the data have to be to prevent the so-called ‘garbage in, garbage out (GIGO)’ phenomenon? How sensitive are methods to missing data and how should it be handled? Do these analytic decisions interact? 5–9 The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines offer standardisation of reporting. Rules of thumb have developed and existed long enough to be critiqued. Many statistical tools have been promoted. 4Īs a result, there has been a proliferation of risk scores and missing data imputation tools both for the common task of short-term mortality prediction and for more specialised tasks. 3 It is routinely measured, even in clinical trials, to assess confounder balance between arms and may form part of a randomized clinical trial(RCT) enrollment or drug approval criteria. It is used to stratify the care of patients for treatments and track quality improvement efforts over time. 1 2 Statistical adjustment, including the handling of missing data, is essential for many performance measurements as well as pay-for-performance and shared savings systems. ![]() Risk adjustment plays an increasingly central role in the organisation, care of and science about critically ill patients. ![]()
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