Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus

The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning

An automated technique for identifying associations between medications, laboratory results and problems

AbstractBackgroundThe patient problem list is an important component of clinical medicine. The problem list enables decision support and quality measurement, and evidence suggests that patients with accurate and complete problem lists may have better outcomes. However, the problem list is often incomplete.ObjectiveTo determine whether association rule mining, a data mining technique, has utility for identifying associations between medications, laboratory results and problems. Such associations may be useful for identifying probable gaps in the problem list.DesignAssociation rule mining was performed on structured electronic health record data for a sample of 100,000 patients receiving care at the Brigham and Women’s Hospital, Boston, MA. The dataset included 272,749 coded problems, 442,658 medications and 11,801,068 laboratory results.MeasurementsCandidate medication-problem and laboratory-problem associations were generated using support, confidence, chi square, interest, and conviction statistics. High-scoring candidate pairs were compared to a gold standard: the Lexi-Comp drug reference database for medications and Mosby’s Diagnostic and Laboratory Test Reference for laboratory results.ResultsWe were able to successfully identify a large number of clinically accurate associations. A high proportion of high-scoring associations were adjudged clinically accurate when evaluated against the gold standard (89.2% for medications with the best-performing statistic, chi square, and 55.6% for laboratory results using interest).ConclusionAssociation rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches

Drug-gene interactions of antihypertensive medications and risk of incident cardiovascular disease: a pharmacogenomics study from the CHARGE consortium

Background Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals. Methods Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk of major cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regression models to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases). Results Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0×10−8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genome-wide association studies (Pinteraction ≥ 0.01). Our results suggest that there are no major pharmacogenetic influences of common SNPs on the relationship between blood pressure medications and the risk of incident CVD

Risk Factors for BK Virus Infection after Kidney Transplantation, London, Ontario 2016

Our objective was to determine the risk factors for BK virus infection in renal allograft recipients in the first year after transplantation. In this cohort, we included all patients who received renal allograft at London Health Sciences Centre (LHSC) between 2012 and 2014. We continued post-transplantation follow-up for one year. Of 175 patients (37% female) with median age (range) of 53 (14-82) years, 40 (22.9%) developed BK viremia (median interval:100 days, range: 35-264). Recipient age, recipient gender, hemodialysis (HD) vs peritoneal dialysis (PD), Human Leukocyte Antigens A1, B35 and Cw4 increased the risk of post-transplant BKV infection. However, donor gender, donor age, deceased vs living donor, delayed graft function, ABO incompatibility and retransplantation did not increase the risk. PD and HD patients do not appear to have equal risks at the time of transplantation. Further studies are required to determine the immunologic reasons for this difference

The prediction of HLA genotypes from next generation sequencing and genome scan data

Genome-wide association studies have very successfully found highly significant disease associations with single nucleotide polymorphisms (SNP) in the Major Histocompatibility Complex for adverse drug reactions, autoimmune diseases and infectious diseases. However, the extensive linkage disequilibrium in the region has made it difficult to unravel the HLA alleles underlying these diseases. Here I present two methods to comprehensively predict 4-digit HLA types from the two types of experimental genome data widely available. The Virtual SNP Imputation approach was developed for genome scan data and demonstrated a high precision and recall (96% and 97% respectively) for the prediction of HLA genotypes. A reanalysis of 6 genome-wide association studies using the HLA imputation method identified 18 significant HLA allele associations for 6 autoimmune diseases: 2 in ankylosing spondylitis, 2 in autoimmune thyroid disease, 2 in Crohn's disease, 3 in multiple sclerosis, 2 in psoriasis and 7 in rheumatoid arthritis. The EPIGEN consortium also used the Virtual SNP Imputation approach to detect a novel association of HLA-A*31:01 with adverse reactions to carbamazepine. For the prediction of HLA genotypes from next generation sequencing data, I developed a novel approach using a naïve Bayes algorithm called HLA-Genotyper. The validation results covered whole genome, whole exome and RNA-Seq experimental designs in the European and Yoruba population samples available from the 1000 Genomes Project. The RNA-Seq data gave the best results with an overall precision and recall near 0.99 for Europeans and 0.98 for the Yoruba population. I then successfully used the method on targeted sequencing data to detect significant associations of idiopathic membranous nephropathy with HLA-DRB1*03:01 and HLA-DQA1*05:01 using the 1000 Genomes European subjects as controls. Using the results reported here, researchers may now readily unravel the association of HLA alleles with many diseases from genome scans and next generation sequencing experiments without the expensive and laborious HLA typing of thousands of subjects. Both algorithms enable the analysis of diverse populations to help researchers pinpoint HLA loci with biological roles in infection, inflammation, autoimmunity, aging, mental illness and adverse drug reactions

Computational methods for the discovery and analysis of genes and other functional DNA sequences

The need for automating genome analysis is a result of the tremendous amount of genomic data. As of today, a high-throughput DNA sequencing machine can run millions of sequencing reactions in parallel, and it is becoming faster and cheaper to sequence the entire genome of an organism. Public databases containing genomic data are growing exponentially, and hence the rise in demand for intuitive automated methods of DNA analysis and subsequent gene identification. However, the complexity of gene organization makes automation a challenging task, and smart algorithm design and parallelization are necessary to perform accurate analyses in reasonable amounts of time. This work describes two such automated methods for the identification of novel genes within given DNA sequences. The first method utilizes negative selection patterns as an evolutionary rationale for the identification of additional members of a gene family. As input it requires a known protein coding gene in that family. The second method is a massively parallel data mining algorithm that searches a whole genome for inverted repeats (palindromic sequences) and identifies potential precursors of non-coding RNA genes. Both methods were validated successfully on the fully sequenced and well studied plant species, Arabidopsis thaliana --Abstract, page iv

Extraction and Classification of Drug-Drug Interaction from Biomedical Text Using a Two-Stage Classifier

One of the critical causes of medical errors is Drug-Drug interaction (DDI), which occurs when one drug increases or decreases the effect of another drug. We propose a machine learning system to extract and classify drug-drug interactions from the biomedical literature, using the annotated corpus from the DDIExtraction-2013 shared task challenge. Our approach applies a two-stage classifier to handle the highly unbalanced class distribution in the corpus. The first stage is designed for binary classification of drug pairs as interacting or non-interacting, and the second stage for further classification of interacting pairs into one of four interacting types: advise, effect, mechanism, and int. To find the set of best features for classification, we explored many features, including stemmed words, bigrams, part of speech tags, verb lists, parse tree information, mutual information, and similarity measures, among others. As the system faced two different classification tasks, binary and multi-class, we also explored various classifiers in each stage. Our results show that the best performing classifier in both stages was Support Vector Machines, and the best performing features were 1000 top informative words and part of speech tags between two main drugs. We obtained an F-Measure of 0.64, showing a 12% improvement over our submitted system to the DDIExtraction 2013 competition

Interdisciplinary systematic review: does alignment between system and design shape adoption and use of barcode medication administration technology?

BACKGROUND: In order to reduce safety risks associated with medication administrations, technologies such as barcode medication administration (BCMA) are increasingly used. Examining how human factors influence adoption and usability of this technology can potentially highlight areas for improvement in design and implementation. OBJECTIVE: To describe how human factors related determinants for BCMA have been researched and reported by healthcare and human-computer interaction disciplines. DATA SOURCES: The Cumulative Index of Nursing, and Allied Health Literature, PubMed, OVID MEDLINE and Google Scholar. STUDY ELIGIBILITY CRITERIA: Primary research published from April 2000 to April 2020, search terms developed to identity different disciplinary research perspectives that examined BCMA use, used a human factors lens and were published in English. SYNTHESIS METHODS: Computerised systematic searches were conducted in four databases. Eligible papers were systematically analysed for themes. Themes were discussed with a second reviewer and supervisors to ensure they were representative of content. RESULTS: Of 3707 papers screened, 11 were included. Studies did not fit neatly into a clinical or human-computer interaction perspective but instead uncovered a range of overlapping narratives, demonstrating consensus on the key themes despite differing research approaches. Prevalent themes were misaligned design and workflow, adaptation and workarounds, mediating factors, safety, users' perceptions and design and usability. Inadequate design frequently led to workarounds, which jeopardised safety. Reported mediating factors included clarity of user needs, pre/post implementation evaluations, analysis of existing workarounds and appropriate technology, infrastructure and staffing. LIMITATIONS: Most studies were relatively small and qualitative, making it difficult to generalise findings. CONCLUSION: Evaluating interdisciplinary perspectives including human factors approaches identified similar and complementary enablers and barriers to successful technology use. Often, mediating factors were developed to compensate for unsuitable design; a collaborative approach between system designer and end users is necessary for BCMA to achieve its true safety potential

Standardising clinical outcomes measures for adult clinical trials in Fabry Disease: A global Delphi Consensus

Background: Recent years have witnessed a considerable increase in clinical trials of new investigational agents for Fabry disease (FD). Several trials investigating different agents are currently in progress; however, lack of standardisation results in challenges to interpretation and comparison. To facilitate the standardisation of investigational programs, we have developed a common framework for future clinical trials in FD. Methods and findings: A broad consensus regarding clinical outcomes and ways to measure them was obtained via the Delphi methodology. 35 FD clinical experts from 4 continents, representing 3389 FD patients, participated in 3 rounds of Delphi procedure. The aim was to reach a consensus regarding clinical trial design, best treatment comparator, clinical outcomes, measurement of those clinical outcomes and inclusion and exclusion criteria. Consensus results of this initiative included: the selection of the adaptative clinical trial as the ideal study design and agalsidase beta as ideal comparator treatment due to its longstanding use in FD. Renal and cardiac outcomes, such as glomerular filtration rate, proteinuria and left ventricular mass index, were prioritised, whereas neurological outcomes including cerebrovascular and white matter lesions were dismissed as a primary or secondary outcome measure. Besides, there was a consensus regarding the importance of patient-related outcomes such as general quality of life, pain, and gastrointestinal symptoms. Also, unity about lysoGb3 and Gb3 tissue deposits as useful surrogate markers of the disease was obtained. The group recognised that cardiac T1 mapping still has potential but requires further development before its widespread introduction in clinical trials. Finally, patients with end-stage renal disease or renal transplant should be excluded unless a particular group for them is created inside the clinical trial. Conclusion: This consensus will help to shape the future of clinical trials in FD. We note that the FDA has, coincidentally, recently published draft guidelines on clinical trials in FD and welcome this contribution