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

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

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Global and Local Regulation of Gene Expression in the Human Brain

Neuropsychiatric disorders are behavioral conditions marked by intellectual, social, or emotional deficits that can be linked to diseases of the nervous system. Autism spectrum disorder (ASD), schizophrenia (SCZ), bipolar disorder (BP), major depressive disorder (MDD), and attention deficit and hyperactivity disorder (ADHD) are common, heritable diseases each with a prevalence exceeding 1% of the population, none of which can be characterized by discernable anatomical or neurological pathologies. Genetic association studies have identified mutations in hundreds of genes that contribute to risk for at least one of these disorders, and have shown that a substantial fraction of the genetic liability is shared between many of these neuropsychiatric diseases. It has long been hoped that with enough genetic evidence we will identify the biological pathways, developmental time points, and brain regions that, when disrupted, give rise to neuropsychiatric disorders. However, the cellular and functional complexity of the human brain, as well as the genetic complexity of neuropsychiatric disease, make it difficult to search for such convergence. In this thesis, I investigate global and local transcriptional regulation within and across 12 regions of the human brain in order to investigate the regional specificity of neuropsychiatric disorders. I develop novel bioinformatics methods – ranging from data processing to network construction – to identify whether the transcriptional regulation of a set of genes is shared or specific. I hypothesize that local, region-specific transcriptional regulation corresponds directly to cell types and processes that are specific to, or far more prevalent in, a given region; that cross-regional transcriptional regulation corresponds to cell types that show little heterogeneity across brain regions; and that genetic disruption of region-specific transcriptional programs results in regional susceptibility. I use a systems-biology approach to summarize transcriptional regulation into reproducibly co-expressed gene sets (“co-expression modules”), which can be analyzed statistically to identify common functions, pathways, and cell types. I then integrate data from genetic association studies to ascertain gene sets conferring outsized risk for neuropsychiatric disorders, thereby implicating the corresponding pathways for further investigation in disease etiology. Finally, I use the network structure itself to investigate the genetic architecture of ASD and SCZ in terms of omnigenics and network polygenics. Chapter 1 presents the biological background for the studies and summarizes some of the major studies of neuropsychiatric disorders along with their principal methods and conclusions. In chapter 2, utilizing my multi-regional co-expression approach, I identify 12 brain-wide, 114 region-specific, and 50 cross-regional co-expression modules. Nearly 40% of expressed genes fall into brain-wide modules and correspond to major cell classes and conserved biological processes, while region-specific modules comprise 25% of expressed genes and correspond to region-specific cell types. The detailed study in chapter 3 demonstrates that neuropsychiatric risk concentrates in both brain wide and multi-regional modules, implicating major core cell types in disease etiology but not region-specific susceptibility. Chapter 4 presents a new and more general framework for defining genetic networks. Using this framework, I show that the network pattern of ASD-associated rare loss-of-function mutations, as well as the large number of significant targets for trans master regulators in BP and SCZ, support a classical polygenic architecture with thousands of directly causal genes. These results suggest that a nontrivial component of risk for neuropsychiatric disease comes from the global polygenic disruption of neuronal function and neuronal maturation

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications