122 research outputs found

    Statistical Machine Learning Methodology for Individualized Treatment Rule Estimation in Precision Medicine

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    Precision medicine aims to deliver optimal, individualized treatments for patients by accounting for their unique characteristics. With a foundation in reinforcement learning, decision theory, and causal inference, the field of precision medicine has seen many advancements in recent years. Significant focus has been placed on creating algorithms to estimate individualized treatment rules (ITRs), which map from patient covariates to the space of available treatments with the goal of maximizing patient outcome. In Chapter 1, we extend ITR estimation methodology in the scenario where variance of the outcome is heterogeneous with respect to treatment and covariates. Accordingly, we propose Stabilized Direct Learning (SD-Learning), which utilizes heteroscedasticity in the error term through a residual reweighting framework that models residual variance via flexible machine learning algorithms such as XGBoost and random forests. We also develop an internal cross-validation scheme which determines the best residual model among competing models. Further, we extend this methodology to multi-arm treatment scenarios. In Chapter 2, we develop ITR estimation methodology for situations where clinical decision-making involves balancing multiple outcomes of interest. Our proposed framework estimates an ITR which maximizes a combination of the multiple clinical outcomes, accounting for the fact that patients may ascribe importance to outcomes differently (utility heterogeneity). This approach employs inverse reinforcement learning (IRL) techniques through an expert-augmentation solution, whereby physicians provide input to guide the utility estimation and ITR learning processes. In Chapter 3, we apply an end-to-end precision medicine workflow to novel data from older adults with Type 1 Diabetes in order to understand the heterogeneous treatment effects of continuous glucose monitoring (CGM) and develop an interpretable ITR to reveal patients for which CGM confers a major safety benefit. The results from this analysis elucidate the demographic and clinical markers which moderate CGM's success, provide the basis for using diagnostic CGM to inform therapeutic CGM decisions, and serve to augment clinical decision-making. Finally, in Chapter 4, as a future research direction, we propose a deep autoencoder framework which simultaneously performs feature selection and ITR optimization, contributing to methodology built for direct consumption of unstructured, high-dimensional data in the precision medicine pipeline.Doctor of Philosoph

    Distinguishing Performance on Tests of Executive Functions Between Those with Depression and Anxiety

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    Objective: To see if there are differences in executive functions between those diagnosed with Major Depressive Disorder (MDD) and those with Generalized Anxiety Disorder (GAD).Participants and Methods: The data were chosen from a de-identified database at a neuropsychological clinic in South Florida. The sample used was adults diagnosed with MDD (n=75) and GAD (n=71) and who had taken the Halstead Category Test, Trail Making Test, Stroop Test, and the Wisconsin Card Sorting Test. Age (M=32.97, SD=11.75), gender (56.7% female), and race (52.7% White) did not differ between groups. IQ did not differ but education did (MDD=13.41 years, SD=2.45; GAD=15.11 years, SD=2.40), so it was ran as a covariate in the analyses. Six ANCOVAs were run separately with diagnosis being held as the fixed factor and executive function test scores held as dependent variables. Results: The MDD group only performed worse on the Category Test than the GAD group ([1,132]=4.022, p\u3c .05). Even though both WCST scores used were significantly different between the two groups, both analyses failed Levene’s test of Equality of Error Variances, so the data were not interpreted. Conclusions: Due to previous findings that those diagnosed with MDD perform worse on tests of executive function than normal controls (Veiel, 1997), this study wanted to compare executive function performance between those diagnosed with MDD and those with another common psychological disorder. The fact that these two groups only differed on the Category Test shows that there may not be much of a difference in executive function deficits between those with MDD and GAD. That being said, not being able to interpret the scores on the WCST test due to a lack of homogeneity of variance indicates that a larger sample size is needed to compare these two types of patients, as significant differences may be found. The results of this specific study, however, could mean that the Category Test could be used in assisting the diagnosis of a MDD patient

    Effects of Diversity and Neuropsychological Performance in an NFL Cohort

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    Objective: The aim of this study was to examine the effect of ethnicity on neuropsychological test performance by comparing scores of white and black former NFL athletes on each subtest of the WMS. Participants and Methods: Data was derived from a de-identified database in South Florida consisting of 63 former NFL white (n=28, 44.4%) and black (n=35, 55.6%) athletes (Mage= 50.38; SD= 11.57). Participants completed the following subtests of the WMS: Logical Memory I and II, Verbal Paired Associates I and II, and Visual Reproduction I and II. Results: A One-Way ANOVA yielded significant effect between ethnicity and performance on several subtests from the WMS-IV. Black athletes had significantly lower scores compared to white athletes on Logical Memory II: F(1,61) = 4.667, p= .035, Verbal Paired Associates I: F(1,61) = 4.536, p = .037, Verbal Paired Associates: II F(1,61) = 4.677, p = .034, and Visual Reproduction I: F(1,61) = 6.562, p = .013. Conclusions: Results suggest significant differences exist between white and black athletes on neuropsychological test performance, necessitating the need for proper normative samples for each ethnic group. It is possible the differences found can be explained by the psychometric properties of the assessment and possibility of a non-representative sample for minorities, or simply individual differences. Previous literature has found white individuals to outperform African-Americans on verbal and non-verbal cognitive tasks after controlling for socioeconomic and other demographic variables (Manly & Jacobs, 2002). This highlights the need for future investigators to identify cultural factors and evaluate how ethnicity specifically plays a role on neuropsychological test performance. Notably, differences between ethnic groups can have significant implications when evaluating a sample of former athletes for cognitive impairment, as these results suggest retired NFL minorities may be more impaired compared to retired NFL white athletes

    The Effect of Ethnicity on Neuropsychological Test Performance of Former NFL Athletes

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    Objective: To investigate the effect of ethnicity on neuropsychological test performance by specifically exploring differences between white and black former NFL athletes on subtests of the WAIS-IV. Participants and Methods: Data was derived from a de-identified database in Florida consisting of 63 former NFL athletes (Mage=50.38; SD=11.57); 28 white and 35 black. Participants completed the following subtests of the WAIS-IV: Block Design, Similarities, Digit Span, Matrix Reasoning, Arithmetic, Symbol Search, Visual Puzzles, Coding, and Cancellation. Results: One-Way ANOVA yielded a significant effect between ethnicity and performance on several subtests. Black athletes had significantly lower scaled scores than white athletes on Block Design F(1,61)=14.266, p\u3c.001, Similarities F(1,61)=5.904, p=.018, Digit Span F(1,61)=8.985, p=.004, Arithmetic F(1,61)=16.07, p\u3c.001 and Visual Puzzles F(1,61)=16.682, p\u3c .001. No effect of ethnicity was seen on performance of Matrix Reasoning F(1,61)=2.937, p=.092, Symbol Search F(1,61)=3.619, p=.062, Coding F(1,61)=3.032, p=.087 or Cancellation F(1,61)=2.289, p=.136. Conclusions: Results reveal significant differences between white and black athletes on all subtests of the WAIS-IV but those from the Processing Speed Scale and Matrix Reasoning. These findings align with previous literature that found white individuals to outperform African-Americans on verbal and non-verbal tasks after controlling for socioeconomic and demographic variables (Manly & Jacobs, 2002). These differences may also be a reflection of the WAIS-IV’s psychometric properties and it is significant to consider the normative sample used may not be appropriate for African-Americans. This study highlights the need for future research to identify how ethnicity specifically influences performance, sheds light on the importance of considering cultural factors when interpreting test results, and serves as a call to action to further understand how and why minorities may not be accurately represented in neuropsychological testing

    Regional Cerebral Blood Flow Patterns in Children vs. Adults with ADHD Combined and Inattentive Types: A SPECT Study

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    Objective: The current study sought to determine whether ADHD Combined Type (ADHD-C) and ADHD Primarily Inattentive Type (ADHD-PI) showed differential regional cerebral blood flow (rCBF) patterns in children vs. adults. Participants and Methods: The overall sample (N=1484) was effectively split into four groups: adults with ADHD-PI (n=519), adults with ADHD-C (n=405), children with ADHD-PI (n=192), children with ADHD-C (n=368). All participants were void of bipolar, schizophrenia, autism, neurocognitive disorders, and TBI. The data were collected from a de-identified archival database of individuals who underwent SPECT scans at rest. Results: Using αConclusions: Overall, the current study suggested that children may show rCBF differences between different ADHD subtypes, but adults may not. The current study did not find significance in any of the 17 brain regions examined when comparing adults with ADHD-C to adults with ADHD-PI. All significant findings were attributed to the children with ADHD-C group showing aberrant blood flow rate than at least one other group. Previous research has supported that the differentiation of these subtypes as distinctive disorders is difficult to make in adults (Sobanski et al., 2006). Other research has indicated the potential of imaging techniques to differentiate the two in children (Al-Amin, Zinchenko, & Geyer, 2018). The current findings support nuanced ways in which rCBF patterns of ADHD-C and ADHD-PI differ between children and adults

    A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

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    Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision
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