AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
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AMIA Annu Symp Proc · Jan 2007
Semantic query generation from eligibility criteria in clinical trials.
Towards the goal of automated eligibility determination for clinical trials from electronic health records, we propose a method to formulate Semantic Web based queries using the free-text eligibility criteria on clinicaltrials.gov.
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AMIA Annu Symp Proc · Jan 2007
Identification of misspelled words without a comprehensive dictionary using prevalence analysis.
Misspellings are common in medical documents and can be an obstacle to information retrieval. We evaluated an algorithm to identify misspelled words through analysis of their prevalence in a representative body of text. We evaluated the algorithm's accuracy of identifying misspellings of 200 anti-hypertensive medication names on 2,000 potentially misspelled words randomly selected from narrative medical documents. ⋯ Area under the ROC curve for identification of misspelled words was 0.96. Sensitivity, specificity, and positive predictive value were 99.25%, 89.72% and 82.9% for the prevalence ratio threshold (0.32768) with the highest F-measure (0.903). Prevalence analysis can be used to identify and correct misspellings with high accuracy.
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This research proposes a comprehensive Information Conceptual Model for a Mass Casualty Continuum of Care. The conceptual model lays out the key relationships among entities/factors in mass casualty events needed to provide real-time visibility of data that track patients, personnel, resources and potential hazards to improve responders situational awareness. Validation of the model is being done using Delphi techniques that establish consensus among a panel of experts.
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AMIA Annu Symp Proc · Jan 2007
Utility of commonly captured data from an EHR to identify hospitalized patients at risk for clinical deterioration.
Rapid Response Teams (RRTs) respond to critically ill patients in the hospital. Activation of RRTs is highly subjective and misses a proportion of at-risk patients. We created an automated scoring system for non-ICU inpatients based on readily available electronic vital signs data, age, and body mass index. ⋯ Using a cutoff score of 4 or greater would result in identification of an additional 20 patients over the 7 patients identified by the current method of RRT activation. The area under the Receiver Operating Curve for the prediction model was 0.72 which compared favorably to other scoring systems. An electronic scoring system using readily captured EMR data may improve identification of patients at risk for clinical deterioration.
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Predicting hospital admission for Emergency Department (ED) patients at the time of triage may improve throughput. To predict admission we created and validated a Bayesian Network from 47,993 encounters (training: n=23,996, validation: n=9,599, test: n=14,398). The area under the receiver operator characteristic curve was 0.833 (0.8260.840) for the network and 0.790 (0.7810.799) for the control variable (acuity only). Predicting hospital admission early during an encounter may help anticipate ED workload and potential overcrowding.