Biology subjects

Folarin, A.

Publications and source records attributed to Folarin, A..

3 recordsLinked to original sources

bioRxiv2017

SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research

ObjectiveUnlocking the data contained within both structured and unstructured components of Electronic Health Records (EHRs) has the potential to provide a step change in data available forsecondary research use, generation of actionable medical insights, hospital management and trial recruitment. To achieve this, we implemented SemEHR - a semantic search and analytics, open source tool for EHRs.\n\nMethodsSemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualised mentions of a wide range of biomedical concepts within EHRs. Natural Language Processing (NLP) annotations are further assembled at patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data is serviced via ontology-based search and analytics interfaces.\n\nResultsSemEHR has been deployed to a number of UK hospitals including the Clinical Record Interactive Search (CRIS), an anonymised replica of the EHR of the UK South London and Maudsley (SLaM) NHS Foundation Trust, one of Europes largest providers of mental health services. In two CRIS-based studies, SemEHR achieved 93% (Hepatitis C case) and 99% (HIV case) F-Measure results in identifying true positive patients. At Kings College Hospital in London, as part of the CogStack programme (github.com/cogstack), SemEHR is being used to recruit patients into the UK Dept of Health 100k Genome Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast in searching phenotypes - time for recruitment criteria checking reduced from days to minutes. Validated on an open intensive care EHR data - MIMICIII, the vital signs extracted by SemEHR can achieve around 97% accuracy.\n\nConclusionResults from the multiple case studies demonstrate SemEHRs efficiency - weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of a patient, bringing in more and unexpected insight compared to study-oriented bespoke information extraction systems.\n\nSemEHR is open source available at https://github.com/CogStack/SemEHR.

bioinformatics↗

bioRxiv2017

Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach.

INTRODUCTIONIn this study we investigate the association between A{beta} levels in cerebrospinal fluid (CSF) and genetic risk in a non-demented population. This paper presents the first analysis to use a Bayesian methodology in this area.\n\nMETHODSData from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the EDAR* and DESCRIPA** studies was used in a Bayesian logistic regression analysis. We modeled CSF A{beta} burden using age, diagnosis (healthy control or mild cognitive impairment), APOE and a polygenic risk score (PGRS) associated with Alzheimers Disease (AD). We compared models built using informative priors on age, diagnosis and APOE with non-informative priors on all variables.\n\nRESULTSThe use of informative priors did not improve model performance in the majority of cases. Models using only age, diagnosis and APOE genotype showed the best predictive ability.\n\nDISCUSSIONA previous study indicated that a PGRS of AD case/control status was associated with CSF A{beta} burden in healthy controls. The current study suggests that this association does not lead to models that are more predictive of amyloid positivity than already known factors such as age and APOE.\n\n* Beta amyloid oligomers in the early diagnosis of AD and as marker for treatment response\n\n** Development of screening guidelines and criteria for pre-dementia Alzheimers disease

bioinformatics↗

bioRxiv2017

CogStack - Experiences Of Deploying IntegratedInformation Retrieval And Extraction Services In A Large National Health Service Foundation Trust Hospital

BackgroundTraditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS). Here, we describe our work on creating and deploying a low cost structured and unstructured information retrieval and extraction architecture within Kings College Hospital, the management of governance concerns and the associated use cases and cost saving opportunities that such components present.\n\nResultsTo date, our CogStack architecture has processed over 300 million lines of clinical data, making it available for internal service improvement projects at Kings College London. On generated data designed to simulate real world clinical text, our de-identification algorithm achieved up to 94% precision and up to 96% recall.\n\nConclusionWe describe a toolkit which we feel is of huge value to the UK (and beyond) healthcare community. It is the only open source, easily deployable solution designed for the UK healthcare environment, in a landscape populated by expensive proprietary systems. Solutions such as these provide a crucial foundation for the genomic revolution in medicine.

bioinformatics↗