Classification of Radiology Reports Using Neural Attention Models

The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure "black-box" models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not only classifies clinically important findings. Specifically, convolutional neural networks (CNN) with attention analysis are used to classify radiology head computed tomography reports based on five categories that radiologists would account for in assessing acute and communicable findings in daily practice. The experiments show that our CNN attention models outperform non-neural models, especially when trained on a larger dataset. Our attention analysis demonstrates the intuition behind the classifier's decision by generating a heatmap that highlights attended terms used by the CNN model; this is valuable when potential downstream medical decisions are to be performed by human experts or the classifier information is to be used in cohort construction such as for epidemiological studies

Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients: A Machine Learning Approach

OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended model (Khorana score). DESIGN: Multiple kernel learning (MKL) based on support vector machines and random optimization (RO) models were used to produce VTE risk predictors (referred to as machine learning [ML]-RO) yielding the best classification performance over a training (3-fold cross-validation) and testing set. RESULTS: Attributes of the patient data set ( n = 1179) were clustered into 9 groups according to clinical significance. Our analysis produced 6 ML-RO models in the training set, which yielded better likelihood ratios (LRs) than baseline models. Of interest, the most significant LRs were observed in 2 ML-RO approaches not including the Khorana score (ML-RO-2: positive likelihood ratio [+LR] = 1.68, negative likelihood ratio [-LR] = 0.24; ML-RO-3: +LR = 1.64, -LR = 0.37). The enhanced performance of ML-RO approaches over the Khorana score was further confirmed by the analysis of the areas under the Precision-Recall curve (AUCPR), and the approaches were superior in the ML-RO approaches (best performances: ML-RO-2: AUCPR = 0.212; ML-RO-3-K: AUCPR = 0.146) compared with the Khorana score (AUCPR = 0.096). Of interest, the best-fitting model was ML-RO-2, in which blood lipids and body mass index/performance status retained the strongest weights, with a weaker association with tumor site/stage and drugs. CONCLUSIONS: Although the monocentric validation of the presented predictors might represent a limitation, these results demonstrate that a model based on MKL and RO may represent a novel methodological approach to derive VTE risk classifiers. Moreover, this study highlights the advantages of optimizing the relative importance of groups of clinical attributes in the selection of VTE risk predictors

Direct Oral Anticoagulants (DOACs) Use in Patients with Renal Insufficiency and Obesity

The popularity of Direct Oral AntiCoagulants (DOACs) for approved indications has risen dramatically following their introduction to the UK’s National Health Service (NHS) due to their convenience of dosing surpassing warfarin. However, prescribing these medications to high-risk patients has been challenging since mainly due to uncertainties around limited clinical trial data. Patients with chronic kidney disease and obesity pose a risk in particular as DOAC dosing was significantly affected by the variables such as, body weight and renal function. Due to the increased prevalence of CKD and obesity among the NHS patient population, the cost savings of preferring DOACs over warfarin was no longerbeneficial due to higher costs of mortalities and consequential morbidities (e.g., strokes and bleeding events). There are very limited interventional studies to rationalise the sample sizes to generalise findings. Therefore, a retrospective real-world data-driven approach was used in this thesis in an attempt to optimise the DOACs dosing regimen for patients with renal impairment and obesity.The main data-driven techniques used in the thesis employed machine learning and multivariate logistic regression (The systematic review in Chapter 6 describes the potential of in-silico modelling). These were applied to a pre-processed dataset, carefully collected from Calderdale and Huddersfield NHS Foundation Trust Hospitals, and profiled accordingly. The methodology was executed in three phases: overall analysis of the full dataset, comprising different BMI categories (Chapter 3), the data analyses comprising patients with morbid obesity only (Chapter 4), and the analyses of the overall dataset comprising patients with different categories of renal impairment (Chapter 5).The factors that influenced the clinical outcomes (such as mortality, ischaemic stroke, clinically relevant non-major bleeding (CRNMB), thromboembolism, length of stay, and emergency visits) in renal impairment and obesity were then determined following data analysis. Some of these factors, which included the individual DOACs administered, exerted a protective effect, while others worsened the safety and, or efficacy indicators. Also, it was found that some of the machine learning models employed in the thesis predicted the target (i.e., DOAC dose regimen) more accurately than others. Chapter 7 provides a discussion of the findings and makes reference and comparison with the existing evidence in the literature. More importantly, the results from patients with renal impairment and obesity were compared. Overall, the aim of generating real-world evidence for optimising DOACs safety and effectiveness in obesity and renal impairment was achieved. Our findings would support clinicians’ decision-making by reducing the uncertainty in DOACs prescribing.There is a need to validate the thesis findings with well-designed prospective studies. There is also a need to explore pharmacometrics analyses and advanced data-driven techniques such as reinforcement learning to arrive at more precise DOAC dosing estimates for patients with renal impairment and obesity

Electronic Health Record-Derived Phenotyping Models to Improve Genomic Research in Stroke

Stroke is a highly heterogeneous and complex disease that is a leading cause of death in the United States. The landscape of risk factors for stroke is vast, and its large genetic burden has yet to be fully discovered. We hypothesize that the small number of stroke variants recovered so far is due to 1) the vast phenotypic heterogeneity of stroke and 2) binary labeling of stroke genome-wide association study (GWAS) participants as cases or controls. Specifically, genome-wide association studies accumulate hundreds of thousands to millions of participants to acquire adequate signal for variant discovery. This requires time-consuming manual curation of cases and controls often involving large-scale collaborations. Genetic biobanks connected to electronic health records (EHR) can facilitate these studies by using data routinely captured during clinical care like billing diagnosis codes. These data, however, do not define adjudicated cases and controls, with many patients falling somewhere in between. There is an opportunity to use machine learning to add nuance to these definitions. We hypothesize that an expanded definition of disease by incorporating correlated diseases and risk factors from EHR data will improve GWAS power. We also hypothesize that granularly subtyping stroke using unsupervised learning methods can provide insight into stroke etiology and heterogeneity. In Chapter 1, we described the motivation for building upon current phenotyping methods for subtyping and genome-wide association studies to improve GWAS power. In Chapter 2, using patients from Columbia-New York Presbyterian (NYP) Hospital, we built and evaluated machine learning models to identify patients with acute ischemic stroke based on 75 different case-control and classifier combinations. In chapter 3, we compared two data-driven and unsupervised methods, non-negative matrix factorization (NMF) and Hierarchical Poisson Factorization, to subtype stroke patients and determined whether any of the subtypes correlate to stroke severity. In chapter 4, we estimated the heritability of acute ischemic stroke by treating the patient probabilities assigned by the machine learning phenotyping models for acute ischemic stroke in chapter 2 as a quantitative trait and mapping the probabilities to Columbia-NYP EHR-generated pedigrees. We also applied our machine learning phenotyping algorithm method, which we call QTPhenProxy, to venous thromboembolism on Columbia eMERGE Consortium patients and ran a genome-wide association study using the model probabilities as a quantitative trait. Finally, we applied QTPhenProxy to subjects in the UK Biobank for stroke and 14 other diseases and ran genome-wide association studies for each disease. We found that our machine-learned models performed well in identifying acute ischemic stroke patients in the Columbia-NYP EHR and in the UK Biobank. We also found some NMF-derived subtypes that were significantly correlated with stroke severity. We were underpowered in the eMERGE venous thromboembolism cohort GWAS and did not recover any known or new variants. Finally, we found that QTPhenProxy improved the power of GWAS of stroke and several subtypes in the UK Biobank, recovered known variants, and discovered a new variant that replicates in a previous stroke GWAS. Our results for QTPhenProxy demonstrate the promise of incorporating large but messy sets of data, such as the electronic health record, to improve signal in genome-wide association studies

Examining Venous Thromboembolism Post-Operative Orthopedic Care Using Electronic Order Sets

Venous thromboembolism (VTE) is a serious health concern of patients undergoing orthopedic surgery. Analysis of the study site semiannual reports from January 2014 through March 2015 indicated 10 VTE events in 546 orthopedic cases. The community hospital was classed as an outlier performing in the bottom 10th percentile when compared to other hospitals. To standardize the ordering of VTE prophylaxis, the hospital developed a postoperative electronic VTE order set. The purpose of this project was to assess the difference in orthopedic VTE occurrences in the postoperative total hip arthroplasty (THA) patients before and after the implementation of the electronic VTE order set. The goal of the project was to use an electronic retrospective chart review to evaluate if the order set implementation influenced the adherence to ordering mechanical and pharmacological prophylaxis in the THA patient. Differences in the ordering of VTE prophylaxis and VTE outcomes were evaluated using a retrospective review of 325 preimplementation order set cases and 406 postimplementation order set cases. This evaluation demonstrated that appropriate pharmacological prophylaxis ordering increased and orthopedic VTE occurrences decreased after the standardized electronic order set was implemented. Social change occurred through the empowerment of clinicians when empirical evidence was provided for use at the point of care, which positively impacted patient outcomes undergoing a common surgical procedure. VTE is no longer considered a routine postoperative orthopedic complication as technology-enabled solutions have proven to be appropriate tools to combat and prevent postoperative VTE complications

Development and implementation of a mobile device-based pediatric electronic decision support tool as part of a national practice standardization project

OBJECTIVE: Implementing evidence-based practices requires a multi-faceted approach. Electronic clinical decision support (ECDS) tools may encourage evidence-based practice adoption. However, data regarding the role of mobile ECDS tools in pediatrics is scant. Our objective is to describe the development, distribution, and usage patterns of a smartphone-based ECDS tool within a national practice standardization project. MATERIALS AND METHODS: We developed a smartphone-based ECDS tool for use in the American Academy of Pediatrics, Value in Inpatient Pediatrics Network project entitled Reducing Excessive Variation in the Infant Sepsis Evaluation (REVISE). The mobile application (app), PedsGuide, was developed using evidence-based recommendations created by an interdisciplinary panel. App workflow and content were aligned with clinical benchmarks; app interface was adjusted after usability heuristic review. Usage patterns were measured using Google Analytics. RESULTS: Overall, 3805 users across the United States downloaded PedsGuide from December 1, 2016, to July 31, 2017, leading to 14 256 use sessions (average 3.75 sessions per user). Users engaged in 60 442 screen views, including 37 424 (61.8%) screen views that displayed content related to the REVISE clinical practice benchmarks, including hospital admission appropriateness (26.8%), length of hospitalization (14.6%), and diagnostic testing recommendations (17.0%). Median user touch depth was 5 [IQR 5]. DISCUSSION: We observed rapid dissemination and in-depth engagement with PedsGuide, demonstrating feasibility for using smartphone-based ECDS tools within national practice improvement projects. CONCLUSIONS: ECDS tools may prove valuable in future national practice standardization initiatives. Work should next focus on developing robust analytics to determine ECDS tools\u27 impact on medical decision making, clinical practice, and health outcomes

Prediction Score for Anticoagulation Control Quality Among Older Adults

Background: Time in the therapeutic range (TTR) is associated with the effectiveness and safety of vitamin K antagonist (VKA) therapy. To optimize prescribing of VKA, we aimed to develop and validate a prediction model for TTR in older adults taking VKA for nonvalvular atrial fibrillation and venous thromboembolism. Methods and Results: The study cohort comprised patients aged ≥65 years who were taking VKA for atrial fibrillation or venous thromboembolism and who were identified in the 2 US electronic health record databases linked with Medicare claims data from 2007 through 2014. With the predictors identified from a systematic review and clinical knowledge, we built a prediction model for TTR, using one electronic health record system as the training set and the other as the validation set. We compared the performance of the new models to that of a published prediction score for TTR, SAMe‐TT 2R2. Based on 1663 patients in the training set and 1181 in the validation set, our optimized score included 42 variables and the simplified model included 7 variables, abbreviated as PROSPER (Pneumonia, Renal dysfunction, Oozing blood [prior bleeding], Staying in hospital ≥7 days, Pain medication use, no Enhanced [structured] anticoagulation services, Rx for antibiotics). The PROSPER score outperformed SAMe‐TT 2R2 when predicting both TTR ≥70% (area under the receiver operating characteristic curve 0.67 versus 0.55) and the thromboembolic and bleeding outcomes (area under the receiver operating characteristic curve 0.62 versus 0.52). Conclusions: Our geriatric TTR score can be used as a clinical decision aid to select appropriate candidates to receive VKA therapy and as a research tool to address confounding and treatment effect heterogeneity by anticoagulation quality

Incidence of Venous Thromboembolism Following New Use of Non-steroidal Anti-inflammatory Drugs in U.S. Women

At least six pharmacoepidemiologic studies have suggested that non-steroidal anti-inflammatory drug (NSAID) use increases the risk of venous thromboembolism (VTE), however, biologic mechanisms for an association are unclear. Most previous studies employed prevalent user case control designs; none employed a new user active comparator design. Body mass index (BMI), often an important confounder in pharmacoepidemiologic studies, is unavailable in insurance claims data and its prediction is difficult. There is increasing interest in internal validation via linkage to electronic health records (EHR) systems to augment claims data. The aims of this dissertation were: 1) to evaluate incidence of VTE following new use of NSAIDs in a long-term cohort of U.S. women, and 2) to explore approaches to control for unmeasured confounding of the association of NSAID initiation and VTE by BMI with self-reported BMI values available for comparison. We identified new use of non-aspirin NSAIDs and incident VTE among 39,876 participants of the Women's Health Study followed from 1993-2012 with annual questionnaires. We designed as-treated analyses comparing NSAIDs initiation with non-initiation and acetaminophen initiation comparators to estimate the relative and absolute effect of NSAID initiation on incidence of VTE. Propensity scores incorporating age, BMI, calendar time, and relevant medical, behavioral, and socioeconomic variables comorbidities updated over time were implemented via weighting to control for confounding. We created subsamples to mimic a claims data analysis (without BMI data) augmented by EHR linkage and compared the performance of multiple imputation (MI) of BMI under completely at random, at random, and not at random internal validation scenarios. Initiation of NSAIDs was associated with increased VTE risk compared to non-initiation, but the association was null or diminished when compared with initiation of acetaminophen, an active comparator with similar indications but no known thrombotic effects. With internal validation for unmeasured BMI, MI approaches showed potential for confounding control in most situations despite poor prediction (R2=0.16) but with reduced effectiveness in analyses with small validation samples and/or few VTE events. The new user active comparator design reduced unmeasured confounding by BMI without the requirement of additional data or statistical procedures.Doctor of Philosoph

A study assessing the characteristics of big data environments that predict high research impact: application of qualitative and quantitative methods

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible