Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Natural Language Processing – Finding the Missing Link for Oncologic Data, 2022

Oncology like most medical specialties, is undergoing a data revolution at the center of which lie vast and growing amounts of clinical data in unstructured, semi-structured and structed formats. Artificial intelligence approaches are widely employed in research endeavors in an attempt to harness electronic medical records data to advance patient outcomes. The use of clinical oncologic data, although collected on large scale, particularly with the increased implementation of electronic medical records, remains limited due to missing, incorrect or manually entered data in registries and the lack of resource allocation to data curation in real world settings. Natural Language Processing (NLP) may provide an avenue to extract data from electronic medical records and as a result has grown considerably in medicine to be employed for documentation, outcome analysis, phenotyping and clinical trial eligibility. Barriers to NLP persist with inability to aggregate findings across studies due to use of different methods and significant heterogeneity at all levels with important parameters such as patient comorbidities and performance status lacking implementation in AI approaches. The goal of this review is to provide an updated overview of natural language processing (NLP) and the current state of its application in oncology for clinicians and researchers that wish to implement NLP to augment registries and/or advance research projects

Health history pattern extraction from textual medical records

Extracting patterns from medical records using temporal data mining techniques

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Developing Predictive Models for Risk of Postoperative Complications and Hemodynamic Instability in Patients Undergoing Surgery

Patients undergoing high-risk surgeries are often at higher risk of developing hemodynamic instability during surgery resulting in poor postoperative outcomes. This is usually associated with significantly increased postoperative morbidity and mortality, which therefore makes the early identification of these critical events and those patients at risk of postoperative complications crucial. With these motivations in mind, we first created a large deidentified research dataset of surgical case medical records from University of California, Irvine Medical Center (UCIMC) matched with physiological waveforms as well as intermittent vital sign values, lab values, and ventilator settings. To our knowledge, such a dataset does not currently exist for the intraoperative environment. We hope that creating a such a dataset will allow for advances in machine learning for intraoperative care. Using medical data from UCLA, we have developed deep neural network models to classify the risks of postoperative mortality, acute kidney injury, and reintubation utilizing readily available intraoperative information. Our risk scores were compared to currently commonly used risk indices ASA and Surgical Apgar as well as logistic regression. While the deep neural network models performed better than the risk scores and logistic regression, clinicians require additional information to assess what led to increased risk of complications. To address this, we also assessed the use of generalized additive neural networks (GANNs) to create a graphical look at how different features contributed to the risk of in hospital mortality. Finally, we were also interested in predicting critical intraoperative events to allow for time for the clinician to avoid such events. We focused on intraoperative hypotension as it is easier to define and has been shown to lead to increased risk of acute kidney injury, stroke, and myocardial injury. For the hypotension prediction models, we looked at the arterial pressure waveform and EMR data as inputs. Overall, these aims address a gap in current clinical decision guidance and support to reduce adverse events during surgery as well complications after

Markov Mean Properties for Cell Death-Related Protein Classification

[Abstract] The cell death (CD) is a dynamic biological function involved in physiological and pathological processes. Due to the complexity of CD, there is a demand for fast theoretical methods that can help to find new CD molecular targets. The current work presents the first classification model to predict CD-related proteins based on Markov Mean Properties. These protein descriptors have been calculated with the MInD-Prot tool using the topological information of the amino acid contact networks of the 2423 protein chains, five atom physicochemical properties and the protein 3D regions. The Machine Learning algorithms from Weka were used to find the best classification model for CD-related protein chains using all 20 attributes. The most accurate algorithm to solve this problem was K*. After several feature subset methods, the best model found is based on only 11 variables and is characterized by the Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.992 and the true positive rate (TP Rate) of 88.2% (validation set). 7409 protein chains labeled with “unknown function” in the PDB Databank were analyzed with the best model in order to predict the CD-related biological activity. Thus, several proteins have been predicted to have CD-related function in Homo sapiens: 3DRX–involved in virus-host interaction biological process, protein homooligomerization; 4DWF–involved in cell differentiation, chromatin modification, DNA damage response, protein stabilization; 1IUR–involved in ATP binding, chaperone binding; 1J7D–involved in DNA double-strand break processing, histone ubiquitination, nucleotide-binding oligomerization; 1UTU–linked with DNA repair, regulation of transcription; 3EEC–participating to the cellular membrane organization, egress of virus within host cell, class mediator resulting in cell cycle arrest, negative regulation of ubiquitin-protein ligase activity involved in mitotic cell cycle and apoptotic process. Other proteins from bacteria predicted as CD-related are 2G3V - a CAG pathogenicity island protein 13 from Helicobacter pylori, 4G5A - a hypothetical protein in Bacteroides thetaiotaomicron, 1YLK–involved in the nitrogen metabolism of Mycobacterium tuberculosis, and 1XSV - with possible DNA/RNA binding domains. The results demonstrated the possibility to predict CD-related proteins using molecular information encoded into the protein 3D structure. Thus, the current work demonstrated the possibility to predict new molecular targets involved in cell-death processes.Xunta de Galicia; 10SIN105004PRInstituto de Salud Carlos III; PI13/0028

Investigating the impact of health analytics on the cost and quality of care for patients with heart failure

The healthcare industry is under tremendous pressure to improve the quality of care and provide more patient centric care, while reducing costs. The potential use of data analytics to address these health system issues has raised significant interest in both research and practice. Health Analytics is central to informing and realizing the systematic quality improvements and cost reductions required by healthcare reform. Fundamentally, the contribution of IS and analytics research in healthcare is to identify and study the impact of interventions that can make a significant difference to the quality and cost of care. This dissertation is concentrated on patients with heart failure (HF). HF is the number one killer in the world, and is the largest contributor to healthcare costs in the United States. Moreover, HF is one of the six conditions used by the Centers for Medicare and Medicaid Services (CMS) to exercise fiduciary control over health systems by monitoring both the quality and cost of care. Specifically, my larger research question is “How can we identify and inform impactful transition of care interventions that manage costs and improve resource allocation efficiencies while providing improved quality of care for heart failure patients?” We adopted a mixed-method approach to study the impact of transitional care in a healthcare system for patients with heart failure. This dissertation includes three essays. In the first essay, I use qualitative methods to study the nature, sources and impacts of information coordination problems as HF patients’ transition through the patient flow in a health system. I propose a set of interventions based on my analysis of information and control errors along the continuum of care to inform the design of appropriate interventions that improve the cost and quality of care. In the second essay, I empirically evaluate the impact of these interventions on cost and quality of care measures such as all cause readmissions, heart failure readmissions, ER visits, length of stay, and cost of care. Analysis suggests that multicomponent complex transitional interventions have significant impact on reducing 30-day readmission and ER visits. The third essay is dedicated to understanding the impact of heart failure patient’s self-care behaviors. I developed and validated an assessment tool for patients with heart failure to monitor and score their condition accurately. Together, these essays investigate impactful transition of care interventions that can help healthcare organizations improve quality of care and manage costs from the clinical, administrative and patient perspectives

Opportunities and Challenges in the Use of Big Data in Healthcare: A Literature Review

Digitalisation and the use of technology are pushing the spread of new business models and improving the efficiency of processes. The demand for innovative and revolutionary applications is increasing, along with the use of big data (BD). The proliferation of large quantities of data is receiving considerable attention in all sectors due to the possibility of using these data in decision-making processes. In the healthcare sector, the role of BD is prominent, especially regarding patient diagnostics, fast epidemic recognition and patient management improvement. To ensure personalised care, the health system must transform individual medical services into electronic forms and favour complete and systemic automation based on the advanced technologies of Industry 4.0. This paper consists of a systematic literature review of the use of BD in the healthcare sector, focusing on the opportunities and challenges. To this end, we selected articles from the Scopus and Web of Science databases. Providing a deep understanding of the state of the art, this paper aims to reveal the implications of the use of BD and offer valuable insights to address future research and identify emerging issues. Keywords: big data, healthcare, digitalisation, internet of things, artificial intelligenc

Clinical metagenomics.

Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health