PREDICTION OF SEPSIS DISEASE BY ARTIFICIAL NEURAL NETWORKS

Sepsis is a fatal condition, which affects at least 26 million people in the world every year that is resulted by an infection. For every 100,000 people, sepsis is seen in 149-240 of them and it has a mortality rate of 30%. The presence of infection in the patient is determined in order to diagnose the sepsis disease. Organ dysfunctions associated with an infection is diagnosed as sepsis. With the increased usage of artificial intelligence in the field of medicine, the early prediction and treatment of many diseases are provided with these methods. Considering the learning, reasoning and decision making abilities of artificial neural networks, which are the sub field of artificial intelligence are inferred to be used in predicting early stages of sepsis disease and determining the sepsis level is assessed. In this study, it is aimed to help sepsis diagnosis by using multi-layered artificial neural network.In construction of artificial neural network model, feed forward back propagation network structure and Levenberg-Marquardt training algorithm were used. The input and output variables of the model were the parameters which doctors use to diagnose the sepsis disease and determine the level of sepsis. The proposed method aims to provide an alternative prediction model for the early detection of sepsis disease

Doctor of Philosophy

dissertationTemporal reasoning denotes the modeling of causal relationships between different variables across different instances of time, and the prediction of future events or the explanation of past events. Temporal reasoning helps in modeling and understanding interactions between human pathophysiological processes, and in predicting future outcomes such as response to treatment or complications. Dynamic Bayesian Networks (DBN) support modeling changes in patients' condition over time due to both diseases and treatments, using probabilistic relationships between different clinical variables, both within and across different points in time. We describe temporal reasoning and representation in general and DBN in particular, with special attention to DBN parameter learning and inference. We also describe temporal data preparation (aggregation, consolidation, and abstraction) techniques that are applicable to medical data that were used in our research. We describe and evaluate various data discretization methods that are applicable to medical data. Projeny, an opensource probabilistic temporal reasoning toolkit developed as part of this research, is also described. We apply these methods, techniques, and algorithms to two disease processes modeled as Dynamic Bayesian Networks. The first test case is hyperglycemia due to severe illness in patients treated in the Intensive Care Unit (ICU). We model the patients' serum glucose and insulin drip rates using Dynamic Bayesian Networks, and recommend insulin drip rates to maintain the patients' serum glucose within a normal range. The model's safety and efficacy are proven by comparing it to the current gold standard. The second test case is the early prediction of sepsis in the emergency department. Sepsis is an acute life threatening condition that requires timely diagnosis and treatment. We present various DBN models and data preparation techniques that detect sepsis with very high accuracy within two hours after the patients' admission to the emergency department. We also discuss factors affecting the computational tractability of the models and appropriate optimization techniques. In this dissertation, we present a guide to temporal reasoning, evaluation of various data preparation, discretization, learning and inference methods, proofs using two test cases using real clinical data, an open-source toolkit, and recommend methods and techniques for temporal reasoning in medicine

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

Data mining and analysis of lung cancer data.

Lung cancer is the leading cause of cancer death in the United States and the world, with more than 1.3 million deaths worldwide per year. However, because of a lack of effective tools to diagnose Lung Cancer, more than half of all cases are diagnosed at an advanced stage, when surgical resection is unlikely to be feasible. The main purpose of this study is to examine the relationship between patient outcomes and conditions of the patients undergoing different treatments for lung cancer and to develop models to predict the mortality of lung cancer. This study will identify the demographic, finance, and clinical factors related to the diagnosis or mortality of Lung Cancer to help physicians and patients in their decision-making. We combined Text Miner and Cluster analysis to identify the claim data for Lung Cancer and to determine the category of diagnosis, treatment procedures and medication treatments for those patients. Moreover, the claims data were used to define severity level and treatment categories. Compared with using diagnosis codes directly, the combination of text mining and cluster analysis is more efficient and captures more useful information for further analysis. In order to analyze the mortality of Lung Cancer, we also found that survival analysis is appropriate to preprocess the data for the relationship between a predictor variable of interest and the time of an event. The proportional hazard model examined the effects of different treatment clusters using a hazard ratio and the proportional effect of a treatment cluster (treatment procedure or medication treatment) may vary with time. A decision tree was built to generate rules for identifying high risk lung cancer cases among the regular inpatient population. Two primary data sets have been used in this study, the Nationwide Inpatient Sample (NIS) and the Thomson MedStat MarketScan data. Kernel density estimation was used for NIS to examine the relationship between Age, Length of stay, Diagnosis Categories, Total Cost and Lung Cancer by visualization. The Kaplan-Meier method and Cox proportional hazard model are used for the Medstat data to discover the relationship between the factors and the target variable for more detail. Time series and predictive modeling are used to predict the total cost for hospital decision making, the mortality of Lung cancer based on the historical data and to generate rules to identify the diagnosis of Lung cancer. Older patients are more likely to have lung cancers that would lead to a higher probability of longer stay and higher costs for the treatment. Within 7 defined clusters of diagnosis for Lung Cancer, the malignant neoplasm of lobe, bronchus or lung is under higher risk. Age, length of stay, admit type, clusters of diagnosis, and clusters of treatment procedures and Major Diagnostic Categories (MDC) were identified as significant factors for the mortality of lung cancer

A New Scalable, Portable, and Memory-Efficient Predictive Analytics Framework for Predicting Time-to-Event Outcomes in Healthcare

Time-to-event outcomes are prevalent in medical research. To handle these outcomes, as well as censored observations, statistical and survival regression methods are widely used based on the assumptions of linear association; however, clinicopathological features often exhibit nonlinear correlations. Machine learning (ML) algorithms have been recently adapted to effectively handle nonlinear correlations. One drawback of ML models is that they can model idiosyncratic features of a training dataset. Due to this overlearning, ML models perform well on the training data but are not so striking on test data. The features that we choose indirectly influence the performance of ML prediction models. With the expansion of big data in biomedical informatics, appropriate feature engineering and feature selection are vital to ML success. Also, an ensemble learning algorithm helps decrease bias and variance by combining the predictions of multiple models. In this study, we newly constructed a scalable, portable, and memory-efficient predictive analytics framework, fitting four components (feature engineering, survival analysis, feature selection, and ensemble learning) together. Our framework first employs feature engineering techniques, such as binarization, discretization, transformation, and normalization on raw dataset. The normalized feature set was applied to the Cox survival regression that produces highly correlated features relevant to the outcome.The resultant feature set was deployed to “eXtreme gradient boosting ensemble learning” (XGBoost) and Recursive Feature Elimination algorithms. XGBoost uses a gradient boosting decision tree algorithm in which new models are created sequentially that predict the residuals of prior models, which are then added together to make the final prediction. In our experiments, we analyzed a cohort of cardiac surgery patients drawn from a multi-hospital academic health system. The model evaluated 72 perioperative variables that impact an event of readmission within 30 days of discharge, derived 48 significant features, and demonstrated optimum predictive ability with feature sets ranging from 16 to 24. The area under the receiver operating characteristics observed for the feature set of 16 were 0.8816, and 0.9307 at the 35th, and 151st iteration respectively. Our model showed improved performance compared to state-of-the-art models and could be more useful for decision support in clinical settings

Decision support continuum paradigm for cardiovascular disease: Towards personalized predictive models

Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.Open Acces

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

Development of a hospital readmission reduction program for patients discharged to skilled nursing facilities: An application of artificial intelligence and machine learning techniques

Background Hospital readmissions within 30 days after discharge have drawn national policy attention as they are a reflection of suboptimal patient care. Readmissions are costly, accounting for more than $17 billion in potentially avoidable Medicare expenditures - nearly 78% of readmissions may be avoidable. Rich electronic data from medical records, growing computing capacities, and open source machine learning algorithms offer new opportunities to predict patients at high risk for readmission and prevent readmission through focused interventions. Prediction models might also serve to provide a more nuanced context of patient characteristics that lead to variations in readmission rates. Furthermore, transitional care between hospitals and skilled nursing facilities is a critical component of patient readmission prevention management. Successful transitional care must include the development of a comprehensive care plan and the availability of experienced health practitioners who are provided relevant medical information on patients’ readmission risk. Methods Predictive models were developed using statistical and machine learning algorithms to identify patients at risk for readmission as well as readmissions associated with pneumonia, sepsis and urinary tract infections after discharge to skilled nursing facilities. Over 3,000 features associated with patients discharged to skilled nursing facilities were extracted from NYU Langone Heath’s electronic health record system, and analyzed using logistic regression, gradient boosting trees, support vector machine, and neural network algorithms. A time split-sample approach was used to partition the data into training, validation, and test sets according to year: 2012-2017 data for training (n = 9,725), 2018 data for validation (n=3,878) and 2019 data for test data (n = 4,342). The most accurate model was selected based on discrimination and calibration performance. The selected model for overall readmission risk was compared to previously published index score models using discrimination and calibration performance. A variable importance algorithm was used to determine the important features of the selected models for overall readmission and readmissions associated with infections. Lastly, using the risk estimates from the models with the four readmission outcomes, a notification and reporting system for key stakeholders was created, including a standardized readmission ratio comparing the observed to the expected number of readmissions by discharging provider and skilled nursing facility. Results A gradient boosting model was selected as the best model to predict overall readmission risk using only real-time data. Discrimination performance was better or similar to previously published index score models that rely on coded data, and calibration was superior. Gradient boosting models were also used to classify readmission risk associated with sepsis, pneumonia, and urinary tract infections. Risk estimates from the models were successfully used to calculate a Readmission Risk Ratio metric. This metric was incorporated into an email to notify key stakeholders and develop risk-adjusted reports. Conclusions Hospitals can leverage the rich data found in electronic health records to generate readmission prediction models optimized for their patient population. This study builds several predictions models, develops an artificial intelligence notification tool, and explores potential interventions as part of a broader program. It does not however asses the effectiveness of the tool nor the interventions’ effect on readmission rates. Validated models can be deployed to target resources for patients at high risk for readmission with proven interventional programs and facilitate collaboration among transitional care teams

GENERALIZABLE MODELS FOR PREDICTION OF PHYSIOLOGICAL DECOMPENSATION FROM MULTIVARIATE AND MULTISCALE PHYSIOLOGICAL TIME SERIES USING DEEP LEARNING AND TRANSFER LEARNING TECHNIQUES

The goal of this thesis is to develop generalizable machine learning models for early prediction of physiological decomposition from multivariate and multiscale physiological time series data. A combination of recent advances in machine learning and the increased availability of more granular physiological time series data (due to increased adoption of electronic medical records in US hospitals) has encouraged the development of more accurate prediction models for the critically ill patients. One such physiological decompensation prediction task we consider in our work is the early prediction of onset of sepsis. Sepsis is a syndromic, life-threatening condition that arises when the body's response to infection injures its own internal organs. While there are effective protocols for treating sepsis (e.g. administration of broad-spectrum antibiotics, Intravenous fluids, and vasopressors) once it has been diagnosed, there still exists challenges in reliably identifying septic patients early in their course. The purpose of this work is to explore the feasibility of utilizing low-resolution electronic medical record data and high-resolution physiological time series data to develop accurate prediction models for onset of sepsis in critically ill patients. To achieve this objective - We first investigate the connection between heart rate (HR) and blood pressure (MAP) time series - as captured through quantification of the structure of their corresponding network representation - for early signs of sepsis. We will then explore the utility of recurrent neural network models for accurate prediction of onset of sepsis. Finally, we combine ideas from adversarial domain adaptation, representation learning and conformal prediction to develop a generalizable prediction model that can adapt well to new target populations (without the requirement of obtaining gold-standard labels).Ph.D