Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Recent Trends in Computational Research on Diseases

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level

Simulation study for improving patient treatment services

This paper describes a simulation study conducted on patients' waiting time and the optimum use of doctors utilization at the Department of Medicine (MOPD) Hospital Alor Setar (HAS) with the aim of improving the operational performance of the hospital.It incorporates the use of Arena to help MOPD develop a model for the analyses of different alternatives to enhance the doctor utilizations and to improve on the patients"waiting time.Two different scenarios were considered.The first scenario, which was changing patients' scheduling capacity resulted in the reduction of patients' waiting time while the second scenario, which was increasing patients' appointment capacity by 10% resulted an increase in the use of doctors but at the same time increased patients' waiting time. Results of the analyses showed that management could reduce patients' waiting time and increase the use of doctors' by changing the scheduling strategy and by redefining the waiting time itself

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 180 reports, articles and other documents introduced into the NASA scientific and technical information system in February 1985

A systematic review of the prediction of hospital length of stay:Towards a unified framework

Hospital length of stay of patients is a crucial factor for the effective planning and management of hospital resources. There is considerable interest in predicting the LoS of patients in order to improve patient care, control hospital costs and increase service efficiency. This paper presents an extensive review of the literature, examining the approaches employed for the prediction of LoS in terms of their merits and shortcomings. In order to address some of these problems, a unified framework is proposed to better generalise the approaches that are being used to predict length of stay. This includes the investigation of the types of routinely collected data used in the problem as well as recommendations to ensure robust and meaningful knowledge modelling. This unified common framework enables the direct comparison of results between length of stay prediction approaches and will ensure that such approaches can be used across several hospital environments. A literature search was conducted in PubMed, Google Scholar and Web of Science from 1970 until 2019 to identify LoS surveys which review the literature. 32 Surveys were identified, from these 32 surveys, 220 papers were manually identified to be relevant to LoS prediction. After removing duplicates, and exploring the reference list of studies included for review, 93 studies remained. Despite the continuing efforts to predict and reduce the LoS of patients, current research in this domain remains ad-hoc; as such, the model tuning and data preprocessing steps are too specific and result in a large proportion of the current prediction mechanisms being restricted to the hospital that they were employed in. Adopting a unified framework for the prediction of LoS could yield a more reliable estimate of the LoS as a unified framework enables the direct comparison of length of stay methods. Additional research is also required to explore novel methods such as fuzzy systems which could build upon the success of current models as well as further exploration of black-box approaches and model interpretability

Physiologic Capacity as a Predictor of Postoperative Complications and Associated Costs in Three Types of Oncological Surgeries

An estimated 12 million individuals undergoing non-cardiac surgery in the United States each year will experience postoperative complications. The costs of complications are manifested in the growing healthcare economic burden and patients\u27 reduced quality of life, future economic productivity, and shortened long-term survival. This research is grounded in a conceptual framework derived from literature in physiologic capacity and stress. The purpose of the study was to test the hypothesis that physiologic capacity is a predictor of postoperative complications and associated costs in three types of oncological surgery (esophagectomy, hepatectomy, and radical cystectomy). Data analysis strategies included forward step-wise binary logistic regression. Results showed a peak oxygen uptake (PVO 2 ) of \u3e20 mL/min/kg, plus a heart rate time (HRTIME) of(for the heart rate to fall at or below 100 bmp after stop test) as the multivariate predictive model (67% sensitivity and 92% specificity) for complications in the hepatectomy group. Conversely, an anaerobic threshold (AT) of \u3e10 mL/min/kg was found to be the univariate predictive model (33% sensitivity and 91% specificity) for the radical cystectomy group. No predictor was found for the esophagectomy group. Each predictive model also predicted between 89%-100% of actual length of stay and hospital costs. Lastly, trends in complications showed esophagectomy with 60 events over 60 days, radical cystectomy with 21 events over 12 days, and hepatectomy with 36 events over 7 days. Implications for positive social change included a paradigm shift from subjective to objective phenotypic physiologic risk assessment affecting standards of care, policies, procedures, and decision-making changes in the healthcare industries and surgeon practice, resulting in better patient outcomes, fewer surgical complications, and increased quality of life

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

Translational and Mechanistic Study about Beta-1-Adrenergic Receptor Modulation on Neutrophils as a Therapy against Ischemia/Reperfusion Injury

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Bioquímica. Fecha de Lectura: 24-02-2023This work received funding from the Instituto de Salud Carlos III (ISCIII; PI16/02110 and PT20/00044), the European Regional Development Fund (ERDF) “A way of making Europe", the Comunidad de Madrid (S2017/BMD-3867 RENIM-CM) cofunded with European structural and investment funds and by Agencia Estatal de Investigación (PID2019‐110369RB‐I00). The CNIC is supported by the ISCIII, the Ministerio de Ciencia e Innovación and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (CEX2020-001041-S

Cardiac rehabilitation and physical activity levels in heart failure

Background Maintenance of adequate physical activity (PA) is a key recommendation for people with and without chronic disease, with well-established health benefits. However, there is uncertainty in the level of objectively assessed PA in people with heart failure (HF) and how exercise-based cardiac rehabilitation (CR) interventions can impact upon PA levels (chapter 1). Methods Four linked research studies were undertaken. A systematic review and meta-analysis to determine whether participation in exercise-based CR increases PA levels of patients with coronary heart disease and HF (chapter 2). A laboratory-based calibration study to estimate HF specific accelerometer intensity thresholds for moderate-to-vigorous PA (MVPA) and inactivity (chapter 3). A cross-sectional study to quantify the PA levels of 247 HF patients participating in a randomised controlled trial of a home-based CR intervention (REACH-HF) in HF patients (chapter 4). A pooled analysis study to assess the effects on PA of the REACH-HF intervention in HF patients and explore the patient characteristics associated with a change in PA level (chapter 5). Results The systematic review and meta-analysis identified 40 randomised controlled trials (6480 patients). Moderate evidence was found to support that CR positively impacts PA levels of patients with coronary heart disease and HF compared to control. The calibration study determined HF specific accelerometer values relating to inactivity (right wrist: 18.6mg (95% CI 8.8 to 28.4mg), left wrist: 16.7mg (95% CI 7.8 to 25.6mg), waist: 7.6mg (95% CI -3.1 to 18.4mg)) and moderate intensity PA (right wrist: 45.5mg (95% CI 31.9 to 59.1mg), left wrist: 43.6 (95% CI 38.5 to 56.3mg), waist: 40.6mg (95% CI 24.3 to 57.0)), lower than the non-specific thresholds used in most HF patient studies based on healthy adults. PA levels of 247 HF patients were examined and 45% were found to meet current PA recommendations of 150 minutes per week of MVPA. However, MVPA ranged widely from 0 to 375.2 minutes per week. HF patient age, body composition, employment status, New York Heart Association class, smoking status, NT-proBNP level, and exercise tolerance were associated (P18 years were associated with a change in PA. Conclusions Objective measurement of PA in HF remains under researched. This thesis discusses methodological, and clinical implications for the future measurement of PA, and exercise-based CR interventions in people with HF (chapter 6)