Learning Better Clinical Risk Models.

Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the appropriate level of treatment, for effective allocation of resources, and for fairly evaluating the performance of healthcare providers. The application and development of methods from the field of machine learning has the potential to improve patient outcomes and reduce healthcare spending with more accurate estimates of patient risk. This dissertation addresses several limitations of currently used clinical risk models, through the identification of novel risk factors and through the training of more effective models. As wearable monitors become more effective and less costly, the previously untapped predictive information in a patient’s physiology over time has the potential to greatly improve clinical practice. However translating these technological advances into real-world clinical impacts will require computational methods to identify high-risk structure in the data. This dissertation presents several approaches to learning risk factors from physiological recordings, through the discovery of latent states using topic models, and through the identification of predictive features using convolutional neural networks. We evaluate these approaches on patients from a large clinical trial and find that these methods not only outperform prior approaches to leveraging heart rate for cardiac risk stratification, but that they improve overall prediction of cardiac death when considered alongside standard clinical risk factors. We also demonstrate the utility of this work for learning a richer description of sleep recordings. Additionally, we consider the development of risk models in the presence of missing data, which is ubiquitous in real-world medical settings. We present a novel method for jointly learning risk and imputation models in the presence of missing data, and find significant improvements relative to standard approaches when evaluated on a large national registry of trauma patients.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pd

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

Clinical assessment of shock through machine learning techniques

This work attempts to identify biomarkers that can be used as indicators for mortality prediction in patients with shock. It uses machine learning techniques to analyze the clinical data, impute missing values in it, select features and reveal causal relationships between them

Machine learning approaches to optimise the management of patients with sepsis

The goal of this PhD was to generate novel tools to improve the management of patients with sepsis, by applying machine learning techniques on routinely collected electronic health records. Machine learning is an application of artificial intelligence (AI), where a machine analyses data and becomes able to execute complex tasks without being explicitly programmed. Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals, but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids and vasopressors are suboptimal and likely induce harm in a proportion of patients. This represents a key clinical challenge and a top research priority. The main contribution of the research has been the development of a reinforcement learning framework and algorithms, in order to tackle this sequential decision-making problem. The model was built and then validated on three large non-overlapping intensive care databases, containing data collected from adult patients in the U.S.A and the U.K. Our agent extracted implicit knowledge from an amount of patient data that exceeds many-fold the life-time experience of human clinicians and learned optimal treatment by having analysed myriads of (mostly sub-optimal) treatment decisions. We used state-of-the-art evaluation techniques (called high confidence off-policy evaluation) and demonstrated that the value of the treatment strategy of the AI agent was on average reliably higher than the human clinicians. In two large validation cohorts independent from the training data, mortality was the lowest in patients where clinicians’ actual doses matched the AI policy. We also gained insight into the model representations and confirmed that the AI agent relied on clinically and biologically meaningful parameters when making its suggestions. We conducted extensive testing and exploration of the behaviour of the AI agent down to the level of individual patient trajectories, identified potential sources of inappropriate behaviour and offered suggestions for future model refinements. If validated, our model could provide individualized and clinically interpretable treatment decisions for sepsis that may improve patient outcomes.Open Acces

Predictive Risk Modelling of Hospital Emergency Readmission, and Temporal Comorbidity Index Modelling Using Machine Learning Methods

This thesis considers applications of machine learning techniques in hospital emergency readmission and comorbidity risk problems, using healthcare administrative data. The aim is to introduce generic and robust solution approaches that can be applied to different healthcare settings. Existing solution methods and techniques of predictive risk modelling of hospital emergency readmission and comorbidity risk modelling are reviewed. Several modelling approaches, including Logistic Regression, Bayes Point Machine, Random Forest and Deep Neural Network are considered. Firstly, a framework is proposed for pre-processing hospital administrative data, including data preparation, feature generation and feature selection. Then, the Ensemble Risk Modelling of Hospital Readmission (ERMER) is presented, which is a generative ensemble risk model of hospital readmission model. After that, the Temporal-Comorbidity Adjusted Risk of Emergency Readmission (T-CARER) is presented for identifying very sick comorbid patients. A Random Forest and a Deep Neural Network are used to model risks of temporal comorbidity, operations and complications of patients using the T-CARER. The computational results and benchmarking are presented using real data from Hospital Episode Statistics (HES) with several samples across a ten-year period. The models select features from a large pool of generated features, add temporal dimensions into the models and provide highly accurate and precise models of problems with complex structures. The performances of all the models have been evaluated across different timeframes, sub-populations and samples, as well as previous models

Using machine learning methods to improve healthcare delivery in diabetes management

This dissertation includes three studies, all focusing on Analytics and Patients information for improving diabetes management, namely educating patients and early detection of comorbidities. In these studies, we develop topic modeling and artificial neural network to acquire, preprocess, model, and predict to minimize the burden on diabetic patients and healthcare providers.The first essay explores the usage of Text Analytics, an unsupervised machine learning model, utilizing the vast data available on social media to improve diabetes education of the patients in managing the condition. Mainly we show the applicability of topic modeling to identify the gaps in diabetes education content and the information and knowledge needs of the patients. While traditional methods of the content decision were based on a group of experts' contributions, our proposed methodology considers the questions raised on social forums for support to extend the education content.The second essay implements Deep Neural Networks on EHR data to assist the clinicians in rank ordering the potential comorbidities that the specific patient may develop in the future. This essay helps prioritize regular screening for comorbidities and rationalize the screening process to improve adherence and effectiveness. Our model prediction helps identify diabetic retinopathy and nephropathy patients with very high precision compared to other traditional methods. Essays 1 and 2 focus on Data Analytics as a research tool for managing a chronic disease in the healthcare environment.The third essay goes through the challenges and best practices of data preprocessing for Analytics studies in healthcare. This study explores the standard preprocessing methodologies and their impact in the case of healthcare data analytics. Highlights the relevant modifications and adaptations to the standards CRISP_DM process. The suggestions are based on past research and the experience obtained in the projects discussed earlier in the thesis.Overall, the dissertation highlights the importance of data analytics in healthcare for better managing and diagnosing chronic diseases. It unfolds the economic value of implementing state-of-the-art IT methods in healthcare, where EHR & IT are predominantly costly and difficult to implement. The dissertation covers ANN and text mining implementation for diabetes management

Developing Clinical Decision Support Systems for Sepsis Prediction Using Temporal and Non-Temporal Machine Learning Methods

In healthcare, diagnostic errors represent the biggest challenge to synthesize accurate treatments. In the United States, patient deaths due to misdiagnoses are estimated at 40,000 to 80,000 per year. It was also found that 30% of the annual healthcare spending was consumed on unnecessary services and other inefficiencies. The diagnostic errors could be reduced, and public health can be improved by applying machine learning and artificial intelligence in healthcare problems. This dissertation is an attempt to formulate clinical decision support systems and to develop new algorithms to reduce clinical errors.This dissertation aims at developing clinical decision support systems to diagnose sepsis in the early stages. The key feature of our work is that we captured the dynamics among body organs using Bayesian networks. The richness of the proposed model is measured not only by achieving high accuracy but also by utilizing fewer lab results.To further improve the accuracy of the clinical decision support system, we utilize longitudinal data to develop a mortality progression model. This part of the dissertation proposes a hidden Markov model (HMM) framework to model the mortality progression. In comparison to existing approaches, the proposed framework leverages the longitudinal data available in the electronic health records (EHR).In addition, this dissertation proposes an initialization procedure to train the parameters of HMM efficiently. The current HMM learning algorithms are sensitive to initialization. The proposed method computes an initial set of parameters by relaxing the time dependency in sequential time series data and incorporating the multinomial logistic regression.Finally, this dissertation compares the prognostic accuracy of two popularly used early sepsis diagnostic criteria: Systemic Inflammatory Response Syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA). Using statistical and machine learning methods, we found that qSOFA is a better diagnostic criteria than SIRS. These findings will guide healthcare providers in selecting the best bedside diagnostic criteria

Advancing Environmental Human Health Risk Assessment through Bayesian Network Analysis

Regulatory agencies rely on quantitative risk assessment to design policies, such as environmental quality standards, to protect public health. Although risk assessment forms the foundation of important policy decisions, recent reviews have indicated the need for technical and practical improvements to risk assessment. This dissertation advances the application of Bayesian networks (BNs) in environmental human health risk assessment in response to this need. BNs were developed to support causal inference in artificial intelligence applications but are not currently used by environmental regulatory agencies. First, a proof-of-concept BN is developed to test BN performance in predicting the effect of maternal exposure to arsenic in drinking water on the risk of newborn lower birthweight for gestational age. The network is the first of its kind to model a dose-response relationship connecting an environmental hazard to a human health outcome. In addition, unlike prevailing regulatory risk assessment approaches, it accounts for inter-individual metabolic differences. The BN is shown to outperform current regulatory risk assessment methods in balancing predictive sensitivity and specificity. Second, a BN is developed to predict the effect of arsenic exposure in drinking water on the risk of diabetes and prediabetes, while accounting for inter-individual differences in arsenic metabolism and body mass index. In addition, the BN’s utility to risk managers is demonstrated by using the model to predict the population-level health consequences of reduced arsenic exposure (including decreased diabetes prevalence). These predictions demonstrate the importance of considering both cancer and non-cancer outcomes when making policy. BNs’ ability to facilitate cost-benefit calculations in regulatory contexts is highlighted. Finally, improvements to risk assessment utility by using BNs are illustrated through a model developed to quantify risk to wastewater treatment workers of contracting Ebola virus disease from contact with contaminated wastewater during an outbreak. The model is used to identify key factors affecting risk and captures risk under different mitigation strategies. These results suggest that BNs offer a quantitatively sophisticated, flexible, and transparent method that addresses key challenges in current risk assessment practice in support of policymaking.Doctor of Philosoph