An Exploration Of Data Mining Approach In Prediction Of The Use Of Physical And Occupational Therapy In Us Adults
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An Exploration of Data Mining Approach in Prediction of the Use of Physical and Occupational Therapy in US Adults
Physical (PT) and Occupational Therapists (OT) appear to be in over- or undersupply in proportion to the adults at the county and state levels in the US. The supply of PT/OT services can be more precisely optimized if the utilization of PT/OT could be predicted based on characteristics of an adult. Prior studies in prediction analyses for the utilization of PT/OT services are either outdated or limited by sampling. With publicly available survey data on national, yearly samples of US adults, there is an opportunity to address this gap in knowledge. This opportunity can be better leveraged with the emergent methods of data mining or machine learning that use computation in combination with statistics. Data mining can allow for future automation of forecasts for the utilization of PT/OT and consequently provide a dynamic support for business and policy decisions to optimize the supply of PT/OT services. Therefore, the aim of this dissertation is to build and validate machine learning models to predict the use of PT/OT services in the US adult population using publicly available survey data. Methods: Using the 2012 National Health Interview Survey (NHIS) data on US adults (n = 34,083), logistic regression, neural network, and decision tree were initially trained and compared for the prediction of whether a sampled adult used PT/OT services. Seeking further gains in generalizability of predictive modeling, averaged models based on ensemble theory were built and compared next. These models included decision tree variants that use bootstrapping (bagging, random forest) and gradient boosting. Stability of explanatory variables was examined across models and variables important for prediction were identified. Finally, the best of these models were empirically tested on NHIS samples of 2013 (n = 34,296) and 2014 (n = 36,359) for their predictive accuracy. Results: Models built on 2012 data showed promising Receiver Operator Characteristic Curve Indexes ranging from 0.722 to 0.823. The best model was the ensemble model that averaged logistic regression, neural network, and decision tree. This model performed consistently well when empirically tested for 2013 (misclassification rate = 9.32%) and 2014 (10.35%) data as well, though there were only small differences across models overall. Important input variables that were significant for their predictive association with the use of PT/OT across more than half of the models included having seen a medical specialist, higher numbers of office visits, being hospitalized, having health problem that requires special equipment, higher frequency of strength activity, surgery, joint pain/aching/stiffness, difficulty standing 2 hours without special equipment, difficulty pushing large objects without special equipment, and having low back pain. Conclusions: The data mining approach deploying multiple-models and model averaging to predict whether an adult will use PT/OT can potentially be translated to practice to support business and policy decisions toward optimizing the supply of PT/OT services to the needs of the population units in the US. Future research may explore local-level data sources like Electronic Health Records, consistent with privacy protection laws, to drive prediction analyses.
The Social Determinants of Mental Health
Author: Michael T. Compton
language: en
Publisher: American Psychiatric Pub
Release Date: 2015-04-01
The Social Determinants of Mental Health aims to fill the gap that exists in the psychiatric, scholarly, and policy-related literature on the social determinants of mental health: those factors stemming from where we learn, play, live, work, and age that impact our overall mental health and well-being. The editors and an impressive roster of chapter authors from diverse scholarly backgrounds provide detailed information on topics such as discrimination and social exclusion; adverse early life experiences; poor education; unemployment, underemployment, and job insecurity; income inequality, poverty, and neighborhood deprivation; food insecurity; poor housing quality and housing instability; adverse features of the built environment; and poor access to mental health care. This thought-provoking book offers many beneficial features for clinicians and public health professionals: Clinical vignettes are included, designed to make the content accessible to readers who are primarily clinicians and also to demonstrate the practical, individual-level applicability of the subject matter for those who typically work at the public health, population, and/or policy level. Policy implications are discussed throughout, designed to make the content accessible to readers who work primarily at the public health or population level and also to demonstrate the policy relevance of the subject matter for those who typically work at the clinical level. All chapters include five to six key points that focus on the most important content, helping to both prepare the reader with a brief overview of the chapter's main points and reinforce the "take-away" messages afterward. In addition to the main body of the book, which focuses on selected individual social determinants of mental health, the volume includes an in-depth overview that summarizes the editors' and their colleagues' conceptualization, as well as a final chapter coauthored by Dr. David Satcher, 16th Surgeon General of the United States, that serves as a "Call to Action," offering specific actions that can be taken by both clinicians and policymakers to address the social determinants of mental health. The editors have succeeded in the difficult task of balancing the individual/clinical/patient perspective and the population/public health/community point of view, while underscoring the need for both groups to work in a unified way to address the inequities in twenty-first century America. The Social Determinants of Mental Health gives readers the tools to understand and act to improve mental health and reduce risk for mental illnesses for individuals and communities. Students preparing for the Medical College Admission Test (MCAT) will also benefit from this book, as the MCAT in 2015 will test applicants' knowledge of social determinants of health. The social determinants of mental health are not distinct from the social determinants of physical health, although they deserve special emphasis given the prevalence and burden of poor mental health.
Artificial Intelligence in Healthcare
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data