4 research outputs found
SemEHR:A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research
OBJECTIVE: Unlocking 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 for secondary research use, generation of actionable medical insights, hospital management, and trial recruitment. To achieve this, we implemented SemEHR, an open source semantic search and analytics tool for EHRs. METHODS: SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualized mentions of a wide range of biomedical concepts within EHRs. Natural language processing annotations are further assembled at the patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data are serviced via ontology-based search and analytics interfaces. RESULTS: SemEHR has been deployed at a number of UK hospitals, including the Clinical Record Interactive Search, an anonymized replica of the EHR of the UK South London and Maudsley National Health Service Foundation Trust, one of Europe's largest providers of mental health services. In 2 Clinical Record Interactive Search-based studies, SemEHR achieved 93% (hepatitis C) and 99% (HIV) F-measure results in identifying true positive patients. At King's College Hospital in London, as part of the CogStack program (github.com/cogstack), SemEHR is being used to recruit patients into the UK Department of Health 100 000 Genomes Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast at searching phenotypes; time for recruitment criteria checking was reduced from days to minutes. Validated on open intensive care EHR data, Medical Information Mart for Intensive Care III, the vital signs extracted by SemEHR can achieve around 97% accuracy. CONCLUSION: Results from the multiple case studies demonstrate SemEHR's efficiency: weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of patients, bringing in more and unexpected insight compared to study-oriented bespoke IE systems. SemEHR is open source, available at https://github.com/CogStack/SemEHR
Two collaborative filtering recommender systems based on sparse dictionary coding
The design and analysis of a novel wideband,
monolithic, bandpass, π-network, voltage controlled
attenuator (VCA) is presented. A 24 to 32 GHz VCA was
developed using 0.15μm GaAs pHEMT technology. This is
the first reported VCA to use a bandpass filter topology to
achieve the required operating frequency band and
eliminate the effects of parasitic capacitances of the
pHEMTs. The bandpass filter absorbs the parasitic
capacitances and thereby eliminates their detrimental
effects.
The measured attenuation dynamic range is 12dB ± 0.5dB
with minimum insertion loss of 2-3dB. The input power
handling capability is up to 0dBm. The VCA is well
matched and may be placed in a 50Ω system [1]