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

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