Accurate, automated extraction of clinical stroke information from unstructured text has several important applications. ICD-9/10 codes can misclassify ischemic stroke events and do not distinguish acuity or location. Expeditious, accurate data extraction could provide considerable improvement in identifying stroke in large datasets, triaging critical clinical reports, and quality improvement efforts. In this study, we developed and report a comprehensive framework studying the performance of simple and complex stroke-specific Natural Language Processing (NLP) and Machine Learning (ML) methods to determine presence, location, and acuity of ischemic stroke from radiographic text. We collected 60,564 Computed Tomography and Magnetic Resonance Imaging Radiology reports from 17,864 patients from two large academic medical centers. We used standard techniques to featurize unstructured text and developed neurovascular specific word GloVe embeddings. We trained various binary classification algorithms to identify stroke presence, location, and acuity using 75% of 1,359 expert-labeled reports. We validated our methods internally on the remaining 25% of reports and externally on 500 radiology reports from an entirely separate academic institution. In our internal population, GloVe word embeddings paired with deep learning (Recurrent Neural Networks) had the best discrimination of all methods for our three tasks (AUCs of 0.96, 0.98, 0.93 respectively). Simpler NLP approaches (Bag of Words) performed best with interpretable algorithms (Logistic Regression) for identifying ischemic stroke (AUC of 0.95), MCA location (AUC 0.96), and acuity (AUC of 0.90). Similarly, GloVe and Recurrent Neural Networks (AUC 0.92, 0.89, 0.93) generalized better in our external test set than BOW and Logistic Regression for stroke presence, location and acuity, respectively (AUC 0.89, 0.86, 0.80). Our study demonstrates a comprehensive assessment of NLP techniques for unstructured radiographic text. Our findings are

Nonalcoholic fatty liver disease (NAFLD) is independently associated with obesity and cardiovascular disease (CVD).CVD is the primary cause of mortality in the predominantly obese population of adults with NAFLD. NAFLD is increasingly seen in individuals who are lean and overweight (i.e., nonobese), but it is unclear whether their risk of CVD is comparable to those with NAFLD and obesity. Using a prospective cohort of patients with NAFLD, we compared the prevalence and incidence of CVD in individuals with and without obesity. NAFLD was diagnosed by biopsy or imaging after excluding other chronic liver disease etiologies. Logistic regression was used to compare the odds of baseline CVD by obesity status. Cox proportional hazards regression was used to evaluate obesity as a predictor of incident CVD and to identify predictors of CVD in subjects with and without obesity. At baseline, adults with obesity had a higher prevalence of CVD compared to those without obesity (12.0% vs. 5.0%, P = 0.02). During follow-up, however, obesity did not predict incident CVD (hazard ratio [HR], 1.24; 95% confidence interval [CI], 0.69-2.22) or other metabolic diseases. Findings were consistent when considering body mass index as a continuous variable and after excluding subjects who were overweight. Age (adjusted HR [aHR], 1.05; 95% CI, 1.03-1.08), smoking (aHR, 4.61; 95% CI, 1.89-11.22), and decreased low-density lipoprotein levels (aHR, 0.98; 95% CI, 0.96-1.00) independently predicted incident CVD in the entire cohort, in subjects with obesity, and in those without obesity, respectively. Conclusion: Individuals with overweight or lean NAFLD are not protected from incident CVD compared to those with NAFLD and obesity, although CVD predictors appear to vary between these groups. Patients without obesity also should undergo rigorous risk stratification and treatment. (Hepatology Communications 2021;0:1-11). N onalcoholic fatty liver disease (NAFLD)is the most prevalent chronic liver disease worldwide, impacting an estimated 25% of the global population and incurring a cost of more than $100 billion per annum in the United States alone. (1,2) NAFLD is projected to continue increasing in prevalence during the next decade. (3) NAFLD is an independent risk factor for cardiovascular disease (CVD) and major adverse CVD events. (4) CVD is a leading cause of mortality in this population, (5)

Lassa fever remains endemic in parts of West Africa and continues to pose as a quiescent threat globally. We described the background on Lassa fever, factors contributing to its emergence and spread, preventive measures, and potential solutions. This review provides a holistic and comprehensive source for academicians, clinicians, researchers, policymakers, infectious disease epidemiologists, virologists, and other stakeholders.