16,868 research outputs found
Residual Weighted Learning for Estimating Individualized Treatment Rules
Personalized medicine has received increasing attention among statisticians,
computer scientists, and clinical practitioners. A major component of
personalized medicine is the estimation of individualized treatment rules
(ITRs). Recently, Zhao et al. (2012) proposed outcome weighted learning (OWL)
to construct ITRs that directly optimize the clinical outcome. Although OWL
opens the door to introducing machine learning techniques to optimal treatment
regimes, it still has some problems in performance. In this article, we propose
a general framework, called Residual Weighted Learning (RWL), to improve finite
sample performance. Unlike OWL which weights misclassification errors by
clinical outcomes, RWL weights these errors by residuals of the outcome from a
regression fit on clinical covariates excluding treatment assignment. We
utilize the smoothed ramp loss function in RWL, and provide a difference of
convex (d.c.) algorithm to solve the corresponding non-convex optimization
problem. By estimating residuals with linear models or generalized linear
models, RWL can effectively deal with different types of outcomes, such as
continuous, binary and count outcomes. We also propose variable selection
methods for linear and nonlinear rules, respectively, to further improve the
performance. We show that the resulting estimator of the treatment rule is
consistent. We further obtain a rate of convergence for the difference between
the expected outcome using the estimated ITR and that of the optimal treatment
rule. The performance of the proposed RWL methods is illustrated in simulation
studies and in an analysis of cystic fibrosis clinical trial data.Comment: 48 pages, 3 figure
An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automati c Prognosis of MCI Patients
Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to be
widely studied. While there is no consensus on whether MCIs actually "convert"
to AD, the more important question is not whether MCIs convert, but what is the
best such definition. We focus on automatic prognostication, nominally using
only a baseline image brain scan, of whether an MCI individual will convert to
AD within a multi-year period following the initial clinical visit. This is in
fact not a traditional supervised learning problem since, in ADNI, there are no
definitive labeled examples of MCI conversion. Prior works have defined MCI
subclasses based on whether or not clinical/cognitive scores such as CDR
significantly change from baseline. There are concerns with these definitions,
however, since e.g. most MCIs (and ADs) do not change from a baseline CDR=0.5,
even while physiological changes may be occurring. These works ignore rich
phenotypical information in an MCI patient's brain scan and labeled AD and
Control examples, in defining conversion. We propose an innovative conversion
definition, wherein an MCI patient is declared to be a converter if any of the
patient's brain scans (at follow-up visits) are classified "AD" by an
(accurately-designed) Control-AD classifier. This novel definition bootstraps
the design of a second classifier, specifically trained to predict whether or
not MCIs will convert. This second classifier thus predicts whether an
AD-Control classifier will predict that a patient has AD. Our results
demonstrate this new definition leads not only to much higher prognostic
accuracy than by-CDR conversion, but also to subpopulations much more
consistent with known AD brain region biomarkers. We also identify key
prognostic region biomarkers, essential for accurately discriminating the
converter and nonconverter groups
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Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.
Background Studies have demonstrated that the current US guidelines based on American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations Risk Calculator may underestimate risk of atherosclerotic cardiovascular disease ( CVD ) in certain high-risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events. Similarly, the guidelines may overestimate risk in low risk populations resulting in unnecessary statin therapy. We used Machine Learning ( ML ) to tackle this problem. Methods and Results We developed a ML Risk Calculator based on Support Vector Machines ( SVM s) using a 13-year follow up data set from MESA (the Multi-Ethnic Study of Atherosclerosis) of 6459 participants who were atherosclerotic CVD-free at baseline. We provided identical input to both risk calculators and compared their performance. We then used the FLEMENGHO study (the Flemish Study of Environment, Genes and Health Outcomes) to validate the model in an external cohort. ACC / AHA Risk Calculator, based on 7.5% 10-year risk threshold, recommended statin to 46.0%. Despite this high proportion, 23.8% of the 480 "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.76, specificity 0.56, and AUC 0.71. In contrast, ML Risk Calculator recommended only 11.4% to take statin, and only 14.4% of "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.86, specificity 0.95, and AUC 0.92. Similar results were found for prediction of "All CVD " events. Conclusions The ML Risk Calculator outperformed the ACC/AHA Risk Calculator by recommending less drug therapy, yet missing fewer events. Additional studies are underway to validate the ML model in other cohorts and to explore its ability in short-term CVD risk prediction
Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for joint optimization of discrimination and calibration.
Historically, probabilistic models for decision support have focused on discrimination, e.g., minimizing the ranking error of predicted outcomes. Unfortunately, these models ignore another important aspect, calibration, which indicates the magnitude of correctness of model predictions. Using discrimination and calibration simultaneously can be helpful for many clinical decisions. We investigated tradeoffs between these goals, and developed a unified maximum-margin method to handle them jointly. Our approach called, Doubly Optimized Calibrated Support Vector Machine (DOC-SVM), concurrently optimizes two loss functions: the ridge regression loss and the hinge loss. Experiments using three breast cancer gene-expression datasets (i.e., GSE2034, GSE2990, and Chanrion's datasets) showed that our model generated more calibrated outputs when compared to other state-of-the-art models like Support Vector Machine (p=0.03, p=0.13, and p<0.001) and Logistic Regression (p=0.006, p=0.008, and p<0.001). DOC-SVM also demonstrated better discrimination (i.e., higher AUCs) when compared to Support Vector Machine (p=0.38, p=0.29, and p=0.047) and Logistic Regression (p=0.38, p=0.04, and p<0.0001). DOC-SVM produced a model that was better calibrated without sacrificing discrimination, and hence may be helpful in clinical decision making
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