A design science framework for research in health analytics

Data analytics provide the ability to systematically identify patterns and insights from a variety of data as organizations pursue improvements in their processes, products, and services. Analytics can be classified based on their ability to: explore, explain, predict, and prescribe. When applied to the field of healthcare, analytics presents a new frontier for business intelligence. In 2013 alone, the Centers for Medicare and Medicaid Services (CMS) reported that the national health expenditure was $2.9 trillion, representing 17.4% of the total United States GDP. The Patient Protection and Affordable Care Act of 2010 (ACA) requires all hospitals to implement electronic medical record (EMR) technologies by year 2014 (Patient Protection and Affordable Care Act, 2010). Moreover, the ACA makes healthcare process and outcomes more transparent by making related data readily available for research. Enterprising organizations are employing analytics and analytical techniques to find patterns in healthcare data (I. R. Bardhan & Thouin, 2013; Hansen, Miron-Shatz, Lau, & Paton, 2014). The goal is to assess the cost and quality of care and identify opportunities for improvement for organizations as well as the healthcare system as a whole. Yet, there remains a need for research to systematically understand, explain, and predict the sources and impacts of the widely observed variance in the cost and quality of care available. This is a driving motivation for research in healthcare. This dissertation conducts a design theoretic examination of the application of advanced data analytics in healthcare. Heart Failure is the number one cause of death and the biggest contributor healthcare costs in the United States. An exploratory examination of the application of predictive analytics is conducted in order to understand the cost and quality of care provided to heart failure patients. The specific research question is addressed: How can we improve and expand upon our understanding of the variances in the cost of care and the quality of care for heart failure? Using state level data from the State Health Plan of North Carolina, a standard readmission model was assessed as a baseline measure for prediction, and advanced analytics were compared to this baseline. This dissertation demonstrates that advanced analytics can improve readmission predictions as well as expand understanding of the profile of a patient readmitted for heart failure. Implications are assessed for academics and practitioners

Smart Classifiers and Bayesian Inference for Evaluating River Sensitivity to Natural and Human Disturbances: A Data Science Approach

Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing. An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and will become more critical under a nonstationary climate, as sediment yields are expected to increase in regions of the world that will experience increased frequency, persistence, and intensity of storm events. Practical tools are needed to predict sediment erosion, transport and deposition and to characterize sediment sources within a reasonable measure of uncertainty. Water resource scientists and engineers use multidimensional data sets of varying types and quality to answer management-related questions, and the temporal and spatial resolution of these data are growing exponentially with the advent of automated samplers and in situ sensors (i.e., “big data”). Data-driven statistics and classifiers have great utility for representing system complexity and can often be more readily implemented in an adaptive management context than process-based models. Parametric statistics are often of limited efficacy when applied to data of varying quality, mixed types (continuous, ordinal, nominal), censored or sparse data, or when model residuals do not conform to Gaussian distributions. Data-driven machine-learning algorithms and Bayesian statistics have advantages over Frequentist approaches for data reduction and visualization; they allow for non-normal distribution of residuals and greater robustness to outliers. This research applied machine-learning classifiers and Bayesian statistical techniques to multidimensional data sets to characterize sediment source and flux at basin, catchment, and reach scales. These data-driven tools enabled better understanding of: (1) basin-scale spatial variability in concentration-discharge patterns of instream suspended sediment and nutrients; (2) catchment-scale sourcing of suspended sediments; and (3) reach-scale sediment process domains. The developed tools have broad management application and provide insights into landscape drivers of channel dynamics and riverine solute and sediment export

Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps

Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export

A waterbody typology derived from catchment controls using self-organising maps

Multiple catchment controls contribute to the geomorphic functioning of river systems at the reach-level, yet only a limited number are usually considered by river scientists and managers. This study uses multiple morphometric, geological, climatic and anthropogenic catchment characteristics to produce a single national typology of catchment controls in England and Wales. Self-organising maps, a machine learning technique, are used to reduce the complexity of the GIS-derived characteristics to classify 4485 Water Framework Directive waterbodies into seven types. The waterbody typology is mapped across England and Wales, primarily reflecting an upland to lowland gradient in catchment controls and secondarily reflecting the heterogeneity of the catchment landscape. The seven waterbody types are evaluated using reach-level physical habitat indices (including measures of sediment size, flow, channel modification and diversity) extracted from River Habitat Survey data. Significant differences are found between each of the waterbody types for most habitat indices suggesting that the GIS-derived typology has functional application for reach-level habitats. This waterbody typology derived from catchment controls is a valuable tool for understanding catchment influences on physical habitats. It should prove useful for rapid assessment of catchment controls for river management, especially where regulatory compliance is based on reach-level monitoring

Predictive modeling of clinical outcomes for hospitalized COVID-19 patients utilizing CyTOF and clinical data.

In December 2019, an outbreak of a novel coronavirus initiated a global pandemic. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that causes the disease coronavirus disease 2019 (COVID-19). Symptoms of infection with COVID-19 vary widely between individuals. While some infected individuals are asymptomatic, others need more extensive care and require hospitalization. Indeed, the COVID-19 pandemic was characterized by a shortage of hospital beds which presented additional complications in providing adequate care for patients. In this study, we used a combination of T cell population data collected from mass cytometry analysis and clinical markers to form a predictive model of clinical outcomes for hospitalized COVID19 patients. This thesis details the steps and analysis towards the design of the final model including data acquirement and preprocessing, missing data handling via multiple imputation, and repeated imputations inferences

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Fuzzy Clustering Image Segmentation Based on Particle Swarm Optimization

Image segmentation refers to the technology to segment the image into different regions with different characteristics and to extract useful objectives, and it is a key step from image processing to image analysis. Based on the comprehensive study of image segmentation technology, this paper analyzes the advantages and disadvantages of the existing fuzzy clustering algorithms; integrates the particle swarm optimization (PSO) with the characteristics of global optimization and rapid convergence and fuzzy clustering (FC) algorithm with fuzzy clustering effects starting from the perspective of particle swarm and fuzzy membership restrictions and gets a PSO-FC image segmentation algorithm so as to effectively avoid being trapped into the local optimum and improve the stability and reliability of clustering algorithm. The experimental results show that this new PSO-FC algorithm has excellent image segmentation effects

Data Fusion and Systems Engineering Approaches for Quality and Performance Improvement of Health Care Systems: From Diagnosis to Care to System-level Decision-making

abstract: Technology advancements in diagnostic imaging, smart sensing, and health information systems have resulted in a data-rich environment in health care, which offers a great opportunity for Precision Medicine. The objective of my research is to develop data fusion and system informatics approaches for quality and performance improvement of health care. In my dissertation, I focus on three emerging problems in health care and develop novel statistical models and machine learning algorithms to tackle these problems from diagnosis to care to system-level decision-making. The first topic is diagnosis/subtyping of migraine to customize effective treatment to different subtypes of patients. Existing clinical definitions of subtypes use somewhat arbitrary boundaries primarily based on patient self-reported symptoms, which are subjective and error-prone. My research develops a novel Multimodality Factor Mixture Model that discovers subtypes of migraine from multimodality imaging MRI data, which provides complementary accurate measurements of the disease. Patients in the different subtypes show significantly different clinical characteristics of the disease. Treatment tailored and optimized for patients of the same subtype paves the road toward Precision Medicine. The second topic focuses on coordinated patient care. Care coordination between nurses and with other health care team members is important for providing high-quality and efficient care to patients. The recently developed Nurse Care Coordination Instrument (NCCI) is the first of its kind that enables large-scale quantitative data to be collected. My research develops a novel Multi-response Multi-level Model (M3) that enables transfer learning in NCCI data fusion. M3 identifies key factors that contribute to improving care coordination, and facilitates the design and optimization of nurses’ training, workload assignment, and practice environment, which leads to improved patient outcomes. The last topic is about system-level decision-making for Alzheimer’s disease early detection at the early stage of Mild Cognitive Impairment (MCI), by predicting each MCI patient’s risk of converting to AD using imaging and proteomic biomarkers. My research proposes a systems engineering approach that integrates the multi-perspectives, including prediction accuracy, biomarker cost/availability, patient heterogeneity and diagnostic efficiency, and allows for system-wide optimized decision regarding the biomarker testing process for prediction of MCI conversion.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201