Linking Electronic Medical Records To Large-Scale Simulation Models: Can We Put Rapid Learning On Turbo?

2007 ◽  
Vol 26 (Suppl1) ◽  
pp. w125-w136 ◽  
Author(s):  
David M. Eddy
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Large Scale ◽  
Simulation Models ◽  
Rapid Learning ◽  
Large Scale Simulation

Large-Scale Simulation Models in Population and Development: A Reply

1977 ◽  
Vol 3 (1/2) ◽  
pp. 126
Author(s):  
W. Brian Arthur ◽  
Geoffrey McNicoll
Keyword(s):  
Large Scale ◽  
Simulation Models ◽  
Large Scale Simulation

scholarly journals Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding

2019 ◽  
Vol 9 (18) ◽  
pp. 3658 ◽  
Author(s):  
Jianliang Yang ◽  
Yuenan Liu ◽  
Minghui Qian ◽  
Chenghua Guan ◽  
Xiangfei Yuan
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Large Scale ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Language Models ◽  
Named Entity

Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based solutions need laborious feature engineering. For the moment, deep learning solutions like Long Short-term Memory with Conditional Random Field (LSTM–CRF) achieved considerable performance in many datasets. In this paper, we developed a multitask attention-based bidirectional LSTM–CRF (Att-biLSTM–CRF) model with pretrained Embeddings from Language Models (ELMo) in order to achieve better performance. In the multitask system, an additional task named entity discovery was designed to enhance the model’s perception of unknown entities. Experiments were conducted on the 2010 Informatics for Integrating Biology & the Bedside/Veterans Affairs (I2B2/VA) dataset. Experimental results show that our model outperforms the state-of-the-art solution both on the single model and ensemble model. Our work proposes an approach to improve the recall in the clinical named entity recognition task based on the multitask mechanism.

scholarly journals Auditing service delivery in glaucoma clinics using visual field records: a feasibility study

2019 ◽  
Vol 4 (1) ◽  
pp. e000352
Author(s):  
Stephen R Kelly ◽  
Susan R Bryan ◽  
John M Sparrow ◽  
David P Crabb
Keyword(s):  
Visual Field ◽  
Service Delivery ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Large Scale ◽  
Health Service Delivery ◽  
Mean Deviation ◽  
Normal Limits ◽  
Field Analyser

ObjectiveThis study aimed to demonstrate that large-scale visual field (VF) data can be extracted from electronic medical records (EMRs) and to assess the feasibility of calculating metrics from these data that could be used to audit aspects of service delivery of glaucoma care.Method and analysisHumphrey visual field analyser (HFA) data were extracted from Medisoft EMRs from five regionally different clinics in England in November 2015, resulting in 602 439 records from 73 994 people. Target patients were defined as people in glaucoma clinics with measurable and sustained VF loss in at least one eye (HFA mean deviation (MD) outside normal limits ≥2 VFs). Metrics for VF reliability, stage of VF loss at presentation, speed of MD loss, predicted loss of sight years (bilateral VF impairment) and frequency of VFs were calculated.ResultsOne-third of people (34.8%) in the EMRs had measurable and repeatable VF loss and were subject to analyses (n=25 760 patients). Median (IQR) age and presenting MD in these patients were 71 (61, 78) years and −6 (–10, –4) dB, respectively. In 19 264 patients with >4 years follow-up, median (IQR) MD loss was −0.2 (−0.8, 0.3) dB/year and median (IQR) intervals between VF examinations was 11 (8, 16) months. Metrics predicting loss of sight years and reliability of examinations varied between centres (p<0.001).ConclusionThis study illustrates the feasibility of assessing aspects of health service delivery in glaucoma clinics through analysis of VF databases. Proposed metrics could be useful for blindness prevention from glaucoma in secondary care centres.

scholarly journals Multiple rotations of Gaussian quadratures: An efficient method for uncertainty analyses in large-scale simulation models

2021 ◽  
Vol 136 ◽  
pp. 104929
Author(s):  
Davit Stepanyan ◽  
Harald Grethe ◽  
Georg Zimmermann ◽  
Khalid Siddig ◽  
Andre Deppermann ◽  
...  
Keyword(s):  
Efficient Method ◽  
Large Scale ◽  
Simulation Models ◽  
Large Scale Simulation ◽  
Gaussian Quadratures ◽  
Uncertainty Analyses

scholarly journals Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model

2011 ◽  
Vol 42 (1) ◽  
pp. 41-50 ◽  
Author(s):  
R. H. Perlis ◽  
D. V. Iosifescu ◽  
V. M. Castro ◽  
S. N. Murphy ◽  
V. S. Gainer ◽  
...  
Keyword(s):  
Electronic Medical Records ◽  
Gold Standard ◽  
Medical Records ◽  
Large Scale ◽  
Clinical Investigation ◽  
Antidepressant Treatment ◽  
Billing Data ◽  
Cross Sectional ◽  
Longitudinal Outcomes ◽  
Co Morbidity

BackgroundElectronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome.MethodNatural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard.ResultsModels incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85–0.88 v. 0.54–0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001).ConclusionsThe application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.

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