Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms

Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.Comment: 30th IEEE International Symposium on Computer-Based Medical Systems - IEEE CBMS 201

RELIGION AND CONTEMPORARY MUSIC CULTURE THE EFFECT OF RAP MUSIC ON YOUTH BRIEF SOCIOLOGICAL REFLECTIONS

With this article, I will argue the impact of the musical culture of the musical subgenre of rap on youth today. To achieve this, I set specific questions within the context of Sociology of Religion and further, trying to seek the conditions of sociological methodology and the very reason that explains the big, and for some people, annoying impact of rap and its music subgenres on young people. The postmodern subproducts of the contemporary globalized music industry; are they really entertaining or not? And on what scale? What are the values, the principles and the social reflections that rap music really promotes? Are the rap artists and those who manage and promote them, very willing to contribute within the context of an authentic artistic expression or do they lean to the tendency to reproduce anti-social stereotypes?With this article, I will argue the impact of the musical culture of the musical subgenre of rap on youth today. To achieve this, I set specific questions within the context of Sociology of Religion and further, trying to seek the conditions of sociological methodology and the very reason that explains the big, and for some people, annoying impact of rap and its music subgenres on young people. The postmodern subproducts of the contemporary globalized music industry; are they really entertaining or not? And on what scale? What are the values, the principles and the social reflections that rap music really promotes? Are the rap artists and those who manage and promote them, very willing to contribute within the context of an authentic artistic expression or do they lean to the tendency to reproduce anti-social stereotypes

A novel framework for assessing metadata quality in epidemiological and public health research settings

Metadata are critical in epidemiological and public health research. However, a lack of biomedical metadata quality frameworks and limited awareness of the implications of poor quality metadata renders data analyses problematic. In this study, we created and evaluated a novel framework to assess metadata quality of epidemiological and public health research datasets. We performed a literature review and surveyed stakeholders to enhance our understanding of biomedical metadata quality assessment. The review identified 11 studies and nine quality dimensions; none of which were specifically aimed at biomedical metadata. 96 individuals completed the survey; of those who submitted data, most only assessed metadata quality sometimes, and eight did not at all. Our framework has four sections: a) general information; b) tools and technologies; c) usability; and d) management and curation. We evaluated the framework using three test cases and sought expert feedback. The framework can assess biomedical metadata quality systematically and robustly

Association between clinical presentations before myocardial infarction and coronary mortality: a prospective population-based study using linked electronic records.

BACKGROUND: Ischaemia in different arterial territories before acute myocardial infarction (AMI) may influence post-AMI outcomes. No studies have evaluated prospectively collected information on ischaemia and its effect on short- and long-term coronary mortality. The objective of this study was to compare patients with and without prospectively measured ischaemic presentations before AMI in terms of infarct characteristics and coronary mortality. METHODS AND RESULTS: As part of the CALIBER programme, we linked data from primary care, hospital admissions, the national acute coronary syndrome registry and cause-specific mortality to identify patients with first AMI (n = 16,439). We analysed time from AMI to coronary mortality (n = 5283 deaths) using Cox regression (median 2.6 years follow-up), comparing patients with and without recent ischaemic presentations. Patients with ischaemic presentations in the 90 days before AMI experienced lower coronary mortality in the first 7 days after AMI compared with those with no prior ischaemic presentations, after adjusting for age, sex, smoking, diabetes, blood pressure and cardiovascular medications [HR: 0.64 (95% CI: 0.57-0.73) P < 0.001], but subsequent mortality was higher [HR: 1.42 (1.13-1.77) P = 0.001]. Patients with ischaemic presentations closer in time to AMI had the lowest seven day mortality (P-trend = 0.001). CONCLUSION: In the first large prospective study of ischaemic presentations prior to AMI, we have shown that those occurring closest to AMI are associated with lower short-term coronary mortality following AMI, which could represent a natural ischaemic preconditioning effect, observed in a clinical setting. CLINICAL TRIALS REGISTRATION: Clinicaltrials.gov identifier NCT01604486

Can primary care electronic health records facilitate the prediction of early cognitive decline associated with dementia: a systematic literature review

Introduction Identifying the early stages of dementia is key in care management, clinical trial recruitment and mitigating the impact of cognitive impairment. At present, cognitive tests are most commonly used to investigate early stages of dementia and are often only conducted after initial symptoms of cognitive decline have been identified. There is potential to harness routinely collected data from electronic health records (EHR) to discover markers of early-stage dementia, both in its cognitive and non-cognitive manifestations. However, the extent to which primary care EHR can facilitate earlier diagnosis of dementia has not systematically been examined. We aim to determine the extent to which EHR can be utilized to identify prodromal dementia in primary care settings through a systematic review of the literature. Method We searched electronic medical databases (including Scopus, Web of Science, OvidSP, MEDLINE and PsychINFO) for potentially relevant studies up to and including September 2016 and written in English. We used the following MeSH search terms: “dementia” (including its subtypes), “electronic health records” (variations thereof) and “primary care”. Additionally, grey literature was searched including reports released by the government, councils and relevant major UK charities. Results We identified and reviewed 31 studies. In total 35 risk factors and 147 potential markers of early cognitive decline were identified. There was considerable variability across studies as to whether markers were classed as confounders, risk factors, early markers or co-morbidities. Markers predominantly fell within cognitive, affective, motor and autonomic symptoms, prescription patterns of both dementia and non-dementia medication and health system utilization, including type of consultation, frequency of contact and duration. Three studies investigated variation in the markers’ predictive strengths at different time points during the prodromal period of dementia. In the 24 months prior to diagnosis of dementia, gait disturbances, changes in weight, number of consultations, specialty referrals and hospital admissions showed the strongest strength of association with dementia diagnosis. Number of consultations, unpredictability in consulting patterns, such as “Did not attend”, carer and social care involvement showed the strongest strength of association with dementia diagnosis during a longer prodromal period (up to 54 months). Discussion Tests which specifically investigate cognitive health, such as the Mini Mental State Exam (MMSE) exam, are often only conducted in the period of Mild Cognitive Impairment (MCI) preceding dementia diagnosis, once irremediable damage has occurred. In many cases, these symptoms are conflated with normal ageing, affective disorders, or attenuated by multimorbidities, and are therefore not directly linked to dementia. These results show that there is a broad range of potential markers which could be used to better define prodromal dementia, however very little literature has been published in this area. Conclusion There is significant potential to use routinely collected data from EHR to investigate and define prodromal dementia. The use of EHR allows us to obtain a more complete understanding of early-stage dementia according to its more commonly investigated cognitive signs, as well as non-cognitive presentations. Understanding the breadth and trajectories in prodromal dementia period will be key in facilitating earlier diagnosis

Neural-signature methods for structured EHR prediction

Models that can effectively represent structured Electronic Healthcare Records (EHR) are central to an increasing range of applications in healthcare. Due to the sequential nature of health data, Recurrent Neural Networks have emerged as the dominant component within state-of-the-art architectures. The signature transform represents an alternative modelling paradigm for sequential data. This transform provides a non-learnt approach to creating a fixed vector representation of temporal features and has shown strong performances across an increasing number of domains, including medical data. However, the signature method has not yet been applied to structured EHR data. To this end, we follow recent work that enables the signature to be used as a differentiable layer within a neural architecture enabling application in high dimensional domains where calculation would have previously been intractable. Using a heart failure prediction task as an exemplar, we provide an empirical evaluation of different variations of the signature method and compare against state-of-the-art baselines. This first application of neural-signature methods in real-world healthcare data shows a competitive performance when compared to strong baselines and thus warrants further investigation within the health domain

Classification of atherothrombotic events in myocardial infarctions survivors with supervised machine learning using data from an electronic health record system

The aim was to build a prediction model for subsequent atherothrombotic events for patients who survived a myocardial infarction. The dataset contained 7,582 patients from a national Electronic Health Record. The prediction is a binary outcome (event and no event) in a period of five years after a myocardial infarction. Different classifiers were tested and XGBoost achieved the best F1-score=0.76. Top features are: imd_score, age_at_entry, egfr_ckdepi_base, height, and SBP_base

Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms

Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main EHR use-case is defining phenotyping algorithms that identify disease status, onset and severity. Phenotyping algorithms utilize diagnoses, prescriptions, laboratory tests, symptoms and other elements in order to identify patients with or without a specific trait. No common standardized, structured, computable format exists for storing phenotyping algorithms. The majority of algorithms are stored as human-readable descriptive text documents making their translation to code challenging due to their inherent complexity and hinders their sharing and re-use across the community. In this paper, we evaluate the two key Semantic Web Technologies, the Web Ontology Language and the Resource Description Framework, for enabling computable representations of EHR-driven phenotyping algorithms.Comment: Accepted American Medical Informatics Association Annual Symposium 201

Diagnostic windows in non-neoplastic diseases: a systematic review

BACKGROUND: Investigating changes in prediagnostic healthcare utilisation can help identify how much earlier conditions could be diagnosed. Such 'diagnostic windows' are established for cancer but remain relatively unexplored for non-neoplastic conditions. AIM: To extract evidence on the presence and length of diagnostic windows for non-neoplastic conditions. DESIGN AND SETTING: A systematic review of studies of prediagnostic healthcare utilisation was carried out. METHOD: A search strategy was developed to identify relevant studies from PubMed and Connected Papers. Data were extracted on prediagnostic healthcare use, and evidence of diagnostic window presence and length was assessed. RESULTS: Of 4340 studies screened, 27 were included, covering 17 non-neoplastic conditions, including both chronic (for example, Parkinson's disease) and acute conditions (for example, stroke). Prediagnostic healthcare events included primary care encounters and presentations with relevant symptoms. For 10 conditions, sufficient evidence to determine diagnostic window presence and length was available, ranging from 28 days (herpes simplex encephalitis) to 9 years (ulcerative colitis). For the remaining conditions, diagnostic windows were likely to be present, but insufficient study duration was often a barrier to robustly determining their length, meaning that diagnostic window length may exceed 10 years for coeliac disease, for example. CONCLUSION: Evidence of changing healthcare use before diagnosis exists for many non-neoplastic conditions, establishing that early diagnosis is possible, in principle. In particular, some conditions may be detectable many years earlier than they are currently diagnosed. Further research is required to accurately estimate diagnostic windows and to determine how much earlier diagnosis may be possible, and how this might be achieved

Deriving research-quality phenotypes from national electronic health records to advance precision medicine: a UK Biobank case-study

High-throughput genotyping and increased availability of electronic health records (EHR) are giving scientists the unprecedented opportunity to exploit routinely generated clinical data to advance precision medicine. The extent to which national structured EHR in the United Kingdom can be utilized in genome-wide association studies (GWAS) has not been systematically examined. In this study, we evaluate the performance of an EHR-derived acute myocardial infarction phenotype (AMI) for performing GWAS in the UK Biobank