Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science\u27s ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploratio

Prediction Of Heart Failure Decompensations Using Artificial Intelligence - Machine Learning Techniques

Los apartados 4.41, 4.4.2 y 4.4.3 del capítulo 4 están sujetos a confidencialidad por la autora. 203 p.Heart failure (HF) is a major concern in public health. Its total impact is increased by its high incidence and prevalence and its unfavourable medium-term prognosis. In addition, HF leads to huge health care resource consumption. Moreover, efforts to develop a deterministic understanding of rehospitalization have been difficult, as no specific patient or hospital factors have been shown to consistently predict 30-day readmission after hospitalization for HF.Taking all these facts into account, we wanted to develop a project to improve the assistance care of patients with HF. Up to know, we were using telemonitoring with a codification system that generated alarms depending on the received values. However, these simple rules generated large number of false alerts being, hence, not trustworthy. The final aims of this work are: (i) asses the benefits of remote patient telemonitoring (RPT), (ii) improve the results obtained with RPT using ML techniques, detecting which parameters measured by telemonitoring best predict HF decompensations and creating predictive models that will reduce false alerts and detect early decompensations that otherwise will lead to hospital admissions and (iii) determine the influence of environmental factors on HF decompensations.All in all, the conclusions of this study are:1. Asses the benefits of RPT: Telemonitoring has not shown a statistically significant reduction in the number of HF-related hospital admissions. Nevertheless, we have observed a statistically significant reduction in mortality in the intervention group with a considerable percentage of deaths from non-cardiovascular causes. Moreover, patients have considered the RPT programme as a tool that can help them in the control of their chronic disease and in the relationship with health professionals.2. Improve the results obtained with RPT using machine learning techniques: Significant weight increases, desaturation below 90%, perception of clinical worsening, including development of oedema, worsening of functional class and orthopnoea are good predictors of heart failure decompensation. In addition, machine learning techniques have improved the current alerts system implemented in our hospital. The system reduces the number of false alerts notably although it entails a decrement on sensitivity values. The best results are achieved with the predictive model built by applying NB with Bernoulli to the combination of telemonitoring alerts and questionnaire alerts (Weight + Ankle + well-being plus the yellow alerts of systolic blood pressure, diastolic blood pressure, O2Sat and heart rate). 3. Determine the influence of environmental factors on HF decompensations: Air temperature is the most significant environmental factor (negative correlation) in our study, although some other attributes, such as precipitation, are also relevant. This work also shows a consistent association between increasing levels SO2 and NOX air and HF hospitalizations

Prediction Of Heart Failure Decompensations Using Artificial Intelligence - Machine Learning Techniques

Los apartados 4.41, 4.4.2 y 4.4.3 del capítulo 4 están sujetos a confidencialidad por la autora. 203 p.Heart failure (HF) is a major concern in public health. Its total impact is increased by its high incidence and prevalence and its unfavourable medium-term prognosis. In addition, HF leads to huge health care resource consumption. Moreover, efforts to develop a deterministic understanding of rehospitalization have been difficult, as no specific patient or hospital factors have been shown to consistently predict 30-day readmission after hospitalization for HF.Taking all these facts into account, we wanted to develop a project to improve the assistance care of patients with HF. Up to know, we were using telemonitoring with a codification system that generated alarms depending on the received values. However, these simple rules generated large number of false alerts being, hence, not trustworthy. The final aims of this work are: (i) asses the benefits of remote patient telemonitoring (RPT), (ii) improve the results obtained with RPT using ML techniques, detecting which parameters measured by telemonitoring best predict HF decompensations and creating predictive models that will reduce false alerts and detect early decompensations that otherwise will lead to hospital admissions and (iii) determine the influence of environmental factors on HF decompensations.All in all, the conclusions of this study are:1. Asses the benefits of RPT: Telemonitoring has not shown a statistically significant reduction in the number of HF-related hospital admissions. Nevertheless, we have observed a statistically significant reduction in mortality in the intervention group with a considerable percentage of deaths from non-cardiovascular causes. Moreover, patients have considered the RPT programme as a tool that can help them in the control of their chronic disease and in the relationship with health professionals.2. Improve the results obtained with RPT using machine learning techniques: Significant weight increases, desaturation below 90%, perception of clinical worsening, including development of oedema, worsening of functional class and orthopnoea are good predictors of heart failure decompensation. In addition, machine learning techniques have improved the current alerts system implemented in our hospital. The system reduces the number of false alerts notably although it entails a decrement on sensitivity values. The best results are achieved with the predictive model built by applying NB with Bernoulli to the combination of telemonitoring alerts and questionnaire alerts (Weight + Ankle + well-being plus the yellow alerts of systolic blood pressure, diastolic blood pressure, O2Sat and heart rate). 3. Determine the influence of environmental factors on HF decompensations: Air temperature is the most significant environmental factor (negative correlation) in our study, although some other attributes, such as precipitation, are also relevant. This work also shows a consistent association between increasing levels SO2 and NOX air and HF hospitalizations

The Impact of Big Data on Chronic Disease Management

Introduction: Population health management – and specifically chronic disease management – depend on the ability of providers to identify patients at high risk of developing costly and harmful conditions such as diabetes, heart failure, and chronic kidney disease (CKD). The advent of big data analytics could help identify high-risk patients which is really beneficial to healthcare practitioners and patients to make informed decisions in a timelier manner with much more evidence in hand. It would allow doctors to extend effective treatment but also reduces the costs of extending improved care to patients. Purpose: The purpose of this study was to identify current applications of big data analytics in healthcare for chronic disease management and to determine its real-world effectiveness in improving patient outcomes and lessening financial burdens. Methodology: The methodology for this study was a literature review. Six electronic databases were utilized and a total of 49 articles were referenced for this research. Results: Improvement in diagnostic accuracy and risk prediction and reduction of hospital readmissions has resulted in significant decrease in health care cost. Big data analytic studies regarding care management and wellness programs have been largely positive. Also, Big data analytics guided better treatment leading to improved patient outcomes. Discussion/Conclusion: Big data analytics shows initial positive impact on quality of care, patient outcomes and finances, and could be successfully implemented in chronic disease management

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

Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures