Anesthesiology

all 10 cities including the rural areas of the province of Kerman. All data were finally analyzed by SPSS software (version 11.5). Results On the basis of recorded statistical analysis, the mortality cases of human rabies in the province of Kerman during one decade was 10 persons (eight males and two females). One-half of them (50%) were bitten by dogs and the others (50%) by foxes. Among the reported deaths, 40% were from Kahnooj county (Jiroft region). The reported data indicated that 21,546 persons were bitten by animals during 10 years in the province of Kerman. The mean of age of the people who were bitten by dogs was 24.80 years (SD = ±14.6), while the mean age of the people who were bitten by foxes was 57.25 years (SD = ±1.50). There was a significant difference between the mean age of these two groups of the people (P < 0.05). The most frequent rate of injured people was reported in the age group 10-19 years old and the frequency rate of males (76.00%) was more than females (24.00%). Therefore, there was a statistically significant difference between males and females in this study (P < 0.01). About 60% of all persons that were bitten by animals were from rural areas and 40% of them were from urban areas (P < 0.05). Among the people who were bitten and injured by animals during one decade in the province of Kerman, 85.70% of them were not treated by the rabies prophylaxis treatment regimen. Among all of them who were bitten by animals, 50% were injured through hands and feet, 40%

Objective With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution. Materials and Methods Performance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger. Results This rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems. Discussion Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute node...

Statistical modeling of outcomes based on a patient&#39;s presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically found in unstructured notes, and thus not readily available for clinical decision making. In an attempt to fill this gap, this study compared two methods for symptom extraction from Emergency Department (ED) admission notes. Both methods utilized a lexicon derived by expanding The Center for Disease Control and Prevention&#39;s (CDC) Symptoms of Coronavirus list. The first method utilized a word2vec model to expand the lexicon using a dictionary mapping to the Uni ed Medical Language System (UMLS). The second method utilized the expanded lexicon as a rule-based gazetteer and the UMLS. These methods were evaluated against a manually annotated reference (f1-score of 0.87 for UMLS-based ensemble; and 0.85 for rule-based gazetteer with ...

Background: Patient safety and optimal resource allocation are essential during the COVID-19 pandemic. We present a COVID-19 prognostic algorithm implemented in real-time as a clinical decision support structure for symptomatic persons under investigation (PUI) for COVID-19 in the emergency department (ED).Methods: The training data included 1,469 patients who tested positive for SARS-CoV-2 within 14 days of acute care. The validation was done using a retrospective set of 414 SARS-CoV-2 positive patients and a PUI set of 13,271 patients who had symptomatic SARS-CoV-2 test during acute care visit. We performed a real-time model assessment on 2,174 patients with an ED visit and symptomatic test or COVID-19 positive result. The logistic regression prognostic model used demographics, comorbidities, home medications, and vital signs factors. The COVID-19 severity outcome comprised a composite of intensive care unit (ICU) admission, invasive mechanical ventilation (ventilator) use, and mo...