The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review

Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes. Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing. Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models. Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision. Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data

Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure

© 2013 IEEE. Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients

Nonlinear Stochastic Dynamic Systems Approach for Personalized Prognostics of Cardiorespiratory Disorders

This research investigates an approach rooted in nonlinear stochastic dynamic systems principles for personalized prognostics of cardiorespiratory disorders in the emerging point-of-care (POC) treatment contexts. Such an approach necessitates new methods for (a) quantitative and personalized modeling of underlying cardiovascular system dynamics to serve as a virtual instrument to derive surrogate (hemodynamic) signals, (b) high-specificity diagnostics to identify and localize disorders, (c) real-time prediction to provide forecasts of impending disorder episodes, and (d) personalized prognosis of the short-term variations of the risk, necessary for effective treatment decisions, based on estimating the distribution of the times remaining till the onset of an anomaly episode. The specific contributions of the dissertation work are as follows: 1. Quantitative modeling for real-time synthesis of hemodynamic signals. Features extracted from ECG signals were used to construct atrioventricular excitation inputs to a nonlinear deterministic lumped parameter model of cardiovascular system dynamics. The model-derived hemodynamic signals, personalized to an individual's physiological and anatomical conditions, would lead to cost-effective virtual medical instruments necessary for personalized POC prognostics. 2. Random graph representation of the complex cardiac dynamics for disorder diagnostics. The quantifiers of a random walk on a network reconstructed from vectorcardiogram (VCG) were investigated for the detection and localization of cardiovascular disorders. Extensive tests with signals from PTB database of PhysioNet databank suggest that locations of myocardial infarction can be determined accurately (sensitivity of ~88% and specificity of ~92%) from tracking certain consistently estimated invariants of this random walk representation. 3. Nonparametric prediction modeling of disorder episodes. A Dirichlet process based mixture Gaussian process was utilized to track and forecast the evolution of the complex nonlinear and nonstationary cardiorespiratory dynamics underlying of the measured signal features and health states. Extensive sleep tests suggest that the method can predict an impending sleep apnea episode to accuracies (R^2) of 83% and 77% for 1 step and 3 step-ahead predictions, respectively.4. Color-coded random graph representation of the state space for personalized prognostic modeling. The prognostic model used the stochastic evolution of the transition pathways from a normal state to an anomalous state in the color-coded state space network to estimate the distribution of the remaining useful life. The prognostic model was validated using the data from ECG Apnea Database (Physionet.org). The model can predict the estimated time till a disorder (apnea episode) onset to within 15% of the observed times 1-45 min ahead of their inception.Industrial Engineering & Managemen

Personality and Longevity: Knowns, Unknowns, and Implications for Public Health and Personalized Medicine

We review evidence for links between personality traits and longevity. We provide an overview of personality for health scientists, using the primary organizing framework used in the study of personality and longevity. We then review data on various aspects of personality linked to longevity. In general, there is good evidence that higher level of conscientiousness and lower levels of hostility and Type D or “distressed” personality are associated with greater longevity. Limited evidence suggests that extraversion, openness, perceived control, and low levels of emotional suppression may be associated with longer lifespan. Findings regarding neuroticism are mixed, supporting the notion that many component(s) of neuroticism detract from life expectancy, but some components at some levels may be healthy or protective. Overall, evidence suggests various personality traits are significant predictors of longevity and points to several promising directions for further study. We conclude by discussing the implications of these links for epidemiologic research and personalized medicine and lay out a translational research agenda for integrating the psychology of individual differences into public health and medicine

2ARTs – Decision Support System for Exercise and Diet Prescriptions in Cardiac Recovery Patients

The global health care system is faced with a variety of complicated challenges, ranging from limited access and increasing expenses to an aging population causing increased pressure on healthcare systems. Healthcare professionals are seeking alternative approaches to provide fair access and sustain high-quality care for everyone as a result of these challenges. Patients have historically been restricted from accessing essential healthcare services due to traditional barriers like geographic distance, financial and resource limitations. Innovative solutions to these problems are starting to take shape, thanks to the growth of eHealth platforms that use technology to improve patient care. Through a comprehensive study of existing solutions in the healthcare domain, particularly in cardiology, we identified the need for a Decision Support System (DSS) that would empower physicians with valuable insights and facilitate informed physical and diet prescribing practices into Cardiac Rehabilitation Programmes (CRPs). The major goal of 2ARTs’ project is to create and implement a cardiac rehabilitation platform into a hospital's infrastructure. A key aspect of this platform is the integration of a decision support system designed to provide physicians with valuable information when prescribing individualized treatment prescriptions for each patient, minimizing the potential of human error. The DSS uses algorithms and predictive models to classify patients into distinct groups based on their features and medical history. This classification provides critical insights and additional knowledge to doctors, allowing them to make informed judgments regarding the most effective treatment options for each patient's cardiac rehabilitation journey. By using the power of data-driven analytics and machine learning, the DSS enables doctors to better understand each patient's needs and personalize treatment actions accordingly. In order to achieve the best possible results aligned with the goals of the project, a variety of approaches based on comprehensive studies were explored, specifically feature selection and feature reduction methods, where their performance metrics were evaluated, seeking the most effective solution. It was through this thorough analysis that Principal Component Analysis (PCA) emerged as the standout choice. PCA not only demonstrated superior outcomes in evaluation metrics, but also showcased excellent compatibility with the selected clustering algorithm along with the best results after an expert analysis. Moreover, with the analysis of the data types and features the dataset had, the K-Means algorithm produced the best results and was more adaptable to our dataset. We were able to identify useful insights and patterns within the data by employing both PCA and K-Means, opening the way for more accurate and informed decision-making in the 2ARTs project