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

Prognostic Predictive Model to Estimate the Risk of Multiple Chronic Diseases: Constructing Copulas Using Electronic Medical Record Data

Introduction: Multimorbidity, the presence of two or more chronic diseases in an individual, is a pressing medical condition. Novel prevention methods are required to reduce the incidence of multimorbidity. Prognostic predictive models estimate a patient’s risk of developing chronic disease. This thesis developed a single predictive model for three diseases associated with multimorbidity: diabetes, hypertension, and osteoarthritis. Methods: Univariate logistic regression models were constructed, followed by an analysis of the dependence that existed using copulas. All analyses were based on data from the Canadian Primary Care Sentinel Surveillance Network. Results: All univariate models were highly predictive, as demonstrated by their discrimination and calibration. Copula models revealed the dependence between each disease pair. Discussion: By estimating the risk of multiple chronic diseases, prognostic predictive models may enable the prevention of chronic disease through identification of high-risk individuals or delivery of individualized risk assessments to inform patient and health care provider decision-making

Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts

Background Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study. Methods and Results We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p<0.0001) after adjusting for traditional cardiovascular risk factors. Conclusions We developed and validated a CAD algorithm that performed well across diverse patient populations. The addition of NLP into the CAD algorithm improved the sensitivity of the algorithm, particularly in cohorts where the prevalence of CAD was low. Preliminary data suggest that CAD risk was significantly lower in RA and IBD compared to DM.National Institutes of Health (U.S.). Informatics for Integrating Biology and the Bedside Project (U54LM008748

A Pilot Study of the Impact of a Pulmonary Clinical Decision Unit on Outcomes in Patients with Chronic Obstructive Pulmonary Disease

The Affordable Care Act has transformed our nation’s healthcare system and reimbursement structure. The paradigm shift to improve efficiency, quality and to lower has created evolving models to rapidly diagnose and treat patients presenting to the emergency department with chronic disease. Clinical decision units (CDU) have become a leading strategy for treating patients at an appropriate level of care. CDU use evidence-based protocol driven care according to different disease status. Patients assigned to these areas, are provided with rapid and safe evaluations for patients identified at low-risk. Reducingthe number ofpatients who arereadmitted to thehospital with COPD is a prioritywithin NHC. Hospitals must make certainthatsystemsand structures arein placeto ensure that patientsreceive the rightlevel ofcareat the right time andrightplace. Diagnosis specificobservation unitsare an innovativeapproach for providing an alternative level of care in which patients could benefit from an extended observation period. Studieshaveshown that OUs reduce re-admission rates, control cost, reduce the LOS, and impact the utilization of ED visits. Further research is recommended to identify other deliberate practices that can contribute to better outcomes

Applications of Machine Learning in Medical Prognosis Using Electronic Medical Records

Approximately 84 % of hospitals are adopting electronic medical records (EMR) In the United States. EMR is a vital resource to help clinicians diagnose the onset or predict the future condition of a specific disease. With machine learning advances, many research projects attempt to extract medically relevant and actionable data from massive EMR databases using machine learning algorithms. However, collecting patients\u27 prognosis factors from Electronic EMR is challenging due to privacy, sensitivity, and confidentiality. In this study, we developed medical generative adversarial networks (GANs) to generate synthetic EMR prognosis factors using minimal information collected during routine care in specialized healthcare facilities. The generated prognosis variables used in developing predictive models for (1) chronic wound healing in patients diagnosed with Venous Leg Ulcers (VLUs) and (2) antibiotic resistance in patients diagnosed with Skin and soft tissue infections (SSTIs). Our proposed medical GANs, EMR-TCWGAN and DermaGAN, can produce both continuous and categorical features from EMR. We utilized conditional training strategies to enhance training and generate classified data regarding healing vs. non-healing in EMR-TCWGAN and susceptibility vs. resistance in DermGAN. The ability of the proposed GAN models to generate realistic EMR data was evaluated by TSTR (test on the synthetic, train on the real), discriminative accuracy, and visualization. We analyzed the synthetic data augmentation technique\u27s practicality in improving the wound healing prognostic model and antibiotic resistance classifier. We achieved the area under the curve (AUC) of 0.875 in the wound healing prognosis model and an average AUC of 0.830 in the antibiotic resistance classifier by using the synthetic samples generated by GANs in the training process. These results suggest that GANs can be considered a data augmentation method to generate realistic EMR data

A PROBABILISTIC APPROACH TO DATA INTEGRATION IN BIOMEDICAL RESEARCH: THE IsBIG EXPERIMENTS

Indiana University-Purdue University Indianapolis (IUPUI)Biomedical research has produced vast amounts of new information in the last decade but has been slow to find its use in clinical applications. Data from disparate sources such as genetic studies and summary data from published literature have been amassed, but there is a significant gap, primarily due to a lack of normative methods, in combining such information for inference and knowledge discovery. In this research using Bayesian Networks (BN), a probabilistic framework is built to address this gap. BN are a relatively new method of representing uncertain relationships among variables using probabilities and graph theory. Despite their computational complexity of inference, BN represent domain knowledge concisely. In this work, strategies using BN have been developed to incorporate a range of available information from both raw data sources and statistical and summary measures in a coherent framework. As an example of this framework, a prototype model (In-silico Bayesian Integration of GWAS or IsBIG) has been developed. IsBIG integrates summary and statistical measures from the NIH catalog of genome wide association studies (GWAS) and the database of human genome variations from the international HapMap project. IsBIG produces a map of disease to disease associations as inferred by genetic linkages in the population. Quantitative evaluation of the IsBIG model shows correlation with empiric results from our Electronic Medical Record (EMR) – The Regenstrief Medical Record System (RMRS). Only a small fraction of disease to disease associations in the population can be explained by the linking of a genetic variation to a disease association as studied in the GWAS. None the less, the model appears to have found novel associations among some diseases that are not described in the literature but are confirmed in our EMR. Thus, in conclusion, our results demonstrate the potential use of a probabilistic modeling approach for combining data from disparate sources for inference and knowledge discovery purposes in biomedical research

Artificial Intelligence in Oncology Drug Discovery and Development

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence

Systematic data-querying of large pediatric biorepository identifies novel Ehlers-Danlos Syndrome variant

BACKGROUND: Ehlers Danlos Syndrome is a rare form of inherited connective tissue disorder, which primarily affects skin, joints, muscle, and blood cells. The current study aimed at finding the mutation that causing EDS type VII C also known as "Dermatosparaxis" in this family. METHODS: Through systematic data querying of the electronic medical records (EMRs) of over 80,000 individuals, we recently identified an EDS family that indicate an autosomal dominant inheritance. The family was consented for genomic analysis of their de-identified data. After a negative screen for known mutations, we performed whole genome sequencing on the male proband, his affected father, and unaffected mother. We filtered the list of non-synonymous variants that are common between the affected individuals. RESULTS: The analysis of non-synonymous variants lead to identifying a novel mutation in the ADAMTSL2 (p. Gly421Ser) gene in the affected individuals. Sanger sequencing confirmed the mutation. CONCLUSION: Our work is significant not only because it sheds new light on the pathophysiology of EDS for the affected family and the field at large, but also because it demonstrates the utility of unbiased large-scale clinical recruitment in deciphering the genetic etiology of rare mendelian diseases. With unbiased large-scale clinical recruitment we strive to sequence as many rare mendelian diseases as possible, and this work in EDS serves as a successful proof of concept to that effect

BMC Nephrol

BackgroundThere is a growing awareness in primary care of the importance of identifying patients with chronic kidney disease (CKD) so that they can receive appropriate clinical care; one method that has been widely embraced is the use of automated reporting of estimated glomerular filtration rate (eGFR) by clinical laboratories. We undertook a qualitative study to examine how clinicians use eGFR in clinical decision making, patient communication issues, barriers to use of eGFR, and suggestions to improve the clinical usefulness of eGFR reports.MethodsOur study used qualitative methods with structured interviews among primary care clinicians including both physicians and allied health providers, recruited from Kaiser Permanente Northwest, a non-profit health maintenance organization.ResultsWe found that clinicians generally held favorable views toward eGFR reporting but did not use eGFR to replace serum creatinine in their clinical decision-making. Clinicians used eGFR as a tool to help identify CKD, educate patients about their kidney function and make treatment decisions. Barriers noted by several clinicians included a desire for greater education regarding care for patients with CKD and tools to facilitate discussion of eGFR findings with patients.ConclusionsThe manner in which clinicians use eGFRs appears to be more complex than previously understood, and our study illustrates some of the efforts that might be usefully undertaken (e.g. specific clinician education) when encouraging further promulgation of eGFR reporting and usage