Multi-stream Longitudinal Data Analysis using Deep Learning

Longitudinal healthcare data encompasses all tasks where patients information are collected at multiple follow-up times. Analyzing this data is critical in addressing many real world problems in healthcare such as disease prediction and prevention. In this thesis, technical challenges in analyzing longitudinal administrative claims data are addressed and novel deep learning based models are proposed for multi-stream data analysis and disease prediction tasks. These algorithms and frameworks are assessed mainly on substance use disorders prediction tasks and specifically designed to tackled these disorders. Substance use disorder is a public health crisis costing the US an estimated $740 billion annually in healthcare, lost workplace productivity, and crime. Early identification and engagement of individuals at risk of developing a substance use disorder is a critical unmet need in healthcare which can be achieved by producing automatic artificial intelligence based tools trained using big healthcare data. In fact, healthcare data can be harnessed together with artificial intelligence and machine learning to advance our understanding of factors that increase the propensity for developing different diseases as well as those that aid in the treatment of these disorders. Here in, a disease prediction framework is first proposed based on recurrent neural networks. This framework includes three components: 1) data pre-processing, 2) disease prediction using long short term memory models, and 3) hypothesis exploration by varying the models and the inputs. This framework is assessed using two use cases: substance use disorder prediction and mild cognitive impairment prediction. Experimental results show that this proposed model can efficiently analyze patients\u27 data and creates efficient disease prediction tools. Second, the limitationsof current deep learning models including long short term memory models in claimsdata analysis are detected and addressed, and a novel model based on the transformer models is proposed. In fact, leveraging the real-world longitudinal claims data, a novel multi-stream transformer model is proposed for predicting opioid use disorder as an important case of substance use disorders. This model is designed to simultaneously analyze multiple types of data streams, such as medications, diagnoses, procedures and demographics, by attending to segments within and across these data streams. The proposed model tested on the IBM MarketScan data showed significantly better performance than the traditional models and recently developed deep learning models

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

Joining the dots: hydrology, freshwater ecosystem values and adaptation options

AbstractThe objective of this research was to investigate and test the necessary steps in developing an adaptation planning framework for freshwater biodiversity. We used Tasmania as a test case to demonstrate how downscaled climate model outputs could be integrated with spatially resolved hydrological models and freshwater biodiversity data. This enabled us to scope adaptation actions at local, regional and state scales for Tasmania, and to explore how priorities might be set.To achieve this integration we quantified how different climate change scenarios could affect the risks to biodiversity and ecosystem values (‘biodiversity assets’) in freshwaters, the scope and types of adaptation actions, and assessed the strengths and weaknesses of the policy and planning instruments in responding to climate change.We concluded that downscaled climate modelling, linked with modelling of catchment and hydrological processes, refines projections for climate-driven risks to aquatic environments. Spatial and temporal hazards and risks can now be compared at a variety of scales, as well as comparisons between biodiversity assets (e.g. relative risk to riparian vegetation v. in-stream biota). Uncertainties can be identified and built into adaptation processes. Notwithstanding this progress, we identified a number of issues that need to be addressed in order to increase confidence in this process.The main issues for improved and timely modelling are: frameworks for using and downscaling outputs from improved global climate models as they become available; better data on thermal tolerances of freshwater biota; and, improved methods for predicting key water temperature variables from air temperature and other biophysical predictors. Improvements are also needed in updating and maintaining high quality biodiversity data sets, and better spatially explicit information on the contributions of groundwater to surface waters and rates of recharge.The list of adaptation options available is extensive, but the key challenge is to organise these options so that stakeholders are not overwhelmed. Scenario modelling that incorporates explicit tools for comparing costs, benefits, feasibility and social acceptability should help with setting priorities but require further development.A review of current Australian policies revealed a variety of responses driven by both water reform and climate change agendas. Many agencies are actively revising their policies to accommodate adaptation. However, we note that much of the reform of the water sector in the last 10–15 years has aimed to improve certainty for non-environmental water uses. Under the National Water Initiative, governments have agreed that entitlement holders should bear the risks of reduced volumes or reliability of their water allocations as a result of changes in climate. The key opportunity for adaptive uptake of climate adaptations is by developing and periodically reviewing water management planning tools. Pathways need to be developed for integrating the traditional evolution of planning and policy with the needs for climate change adaptation for aquatic ecosystems. Formal mechanisms for the uptake of knowledge about identified risks into policy and legislative instruments remain under-developed. An even bigger challenge is to integrate multiple adaptation strategies (sometimes at different scales) to achieve specific adaptation objectives within regions or catchments—especially where a mix of water management and non-water management is required

The Prevention Of Depression: A Machine Learning Approach

Behavioral health disorders, specifically depression, are a serious health concern in the United States and worldwide. The consequences of unaddressed behavioral health conditions are multifaceted and have impact at the individual, relational, communal, and societal level. Despite the number of individuals who could benefit from treatment for behavioral health concerns, their difficulties are often unidentified and unaddressed through treatment. Technology carries unrealized potential to identify people at risk for behavioral health conditions and to inform prevention and intervention strategies. Drawing upon data from the National Longitudinal Study of Adolescent Health (Add Health, n=3782), this study has two aims related to advancing understanding of technology’s potential value in behavioral health: 1) to develop a forecasting procedure that can be used to identify youth who are at risk of reporting a depression diagnosis as adults based on a set of input variables; and 2) to understand the developmental trajectories of depression for youth. To address the first aim of this study, random forest methodology was used to derive the forecasting algorithm. The second aim was pursued with Generalized Additive Model analysis to estimate relationships between presence of a reported depression diagnosis as an adult and youth characteristics. Findings from this study indicate that it is feasible to use a forecasting tool to identify individuals at risk of being diagnosed with depression, which can facilitate early intervention and improved outcomes. Gender, race, and receiving counseling as a youth were the most important predictors of having a reported depression diagnosis as an adult. This dissertation addresses the role of health disparities, specifically gender and race, related to depression and mental health treatment. In sum, this dissertation highlights how a machine learning forecasting tool could be used to inform prevention strategies and understanding of factors associated with receiving a depression diagnosis. This study presents and discusses these findings in addition to offering important implications for future research and practice to identify and prevent behavioral health conditions such as depression

Assessment Of largemouth Bass (Micropterus Salmoides) Condition Using Liver Histopathology, Health Parameters and Water Quality Of The Central Chattahoochee River Watershed

The health of Largemouth bass from west-central Georgia was assessed using liver histopathology and the degree of tissue change index (DTC), hepatosomatic indices (HSI), relative weight, condition factors, and intersex. These health parameters as well as water chemistry data were compared across four bodies of water, three within the Chattahoochee watershed, and one pseudo-control located in the Flint watershed. Fish from Lake Oliver, Lake Harding, and West Point Lake had severely impacted liver health compared to Warm Springs National Fish Hatchery, with DTC rankings over 100 for the three lakes within the Chattahoochee watershed, demonstrating severely damaged livers. There was a significant difference between the hepatosomatic index and condition factor for fish when grouped into either high (\u3e100) DTC or low (\u3c 58) DTC categories, but not for relative weight. DTC scores between males and females in the study showed no significant difference. Males who had intersex had significantly higher DTC averages. Water chemistry analysis found 12 compounds that are known endocrine disruptors that can cause liver damage, including several types of phthalates in the Chattahoochee Watershed

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions