Institutions and social structures

This paper clarifies the terms "institutions" and "social structures" and related terms "rules", "conventions", "norms", "values" and "customs". Part one explores the similarities between institutions and social structures whilst the second and third parts explore differences. Part two considers institutions, rules, habits or habitus and habituation, whilst part three critically reflects on three common conceptions of social structures. The conclusion comments upon reflexive deliberation via the internal conversation. © The Executive Management Committee/Blackwell Publishing Ltd. 2008

Problem-based Learning at Georgia Tech

This podcast introduces listeners to the concept of Problem-Based Learning (PBL) and how this pedagogical technique has been applied at Georgia Tech. The primary focus is the use of PBL in an undergraduate neuroethics course taught during fall 2013

Evaluation of multiple open-source deep learning models for detecting and grading COVID-19 on chest radiographs

Purpose: Chest x-rays are complex to report accurately. Viral pneumonia is often subtle in its radiological appearance. In the context of the COVID-19 pandemic, rapid triage of cases and exclusion of other pathologies with artificial intelligence (AI) can assist over-stretched radiology departments. We aim to validate three open-source AI models on an external test set. Approach: We tested three open-source deep learning models, COVID-Net, COVIDNet-S-GEO, and CheXNet for their ability to detect COVID-19 pneumonia and to determine its severity using 129 chest x-rays from two different vendors Phillips and Agfa. Results: All three models detected COVID-19 pneumonia (AUCs from 0.666 to 0.778). Only the COVID Net-S-GEO and CheXNet models performed well on severity scoring (Pearson's r 0.927 and 0.833, respectively); COVID-Net only performed well at either task on images taken with a Philips machine (AUC 0.735) and not an Agfa machine (AUC 0.598). Conclusions: Chest x-ray triage using existing machine learning models for COVID-19 pneumonia can be successfully implemented using open-source AI models. Evaluation of the model using local x-ray machines and protocols is highly recommended before implementation to avoid vendor or protocol dependent bias.</p

Large-scale identification of undiagnosed hepatic steatosis using natural language processingResearch in context

Summary: Background: Nonalcoholic fatty liver disease (NAFLD) is a major cause of liver-related morbidity in people with and without diabetes, but it is underdiagnosed, posing challenges for research and clinical management. Here, we determine if natural language processing (NLP) of data in the electronic health record (EHR) could identify undiagnosed patients with hepatic steatosis based on pathology and radiology reports. Methods: A rule-based NLP algorithm was built using a Linguamatics literature text mining tool to search 2.15 million pathology report and 2.7 million imaging reports in the Penn Medicine EHR from November 2014, through December 2020, for evidence of hepatic steatosis. For quality control, two independent physicians manually reviewed randomly chosen biopsy and imaging reports (n = 353, PPV 99.7%). Findings: After exclusion of individuals with other causes of hepatic steatosis, 3007 patients with biopsy-proven NAFLD and 42,083 patients with imaging-proven NAFLD were identified. Interestingly, elevated ALT was not a sensitive predictor of the presence of steatosis, and only half of the biopsied patients with steatosis ever received an ICD diagnosis code for the presence of NAFLD/NASH. There was a robust association for PNPLA3 and TM6SF2 risk alleles and steatosis identified by NLP. We identified 234 disorders that were significantly over- or underrepresented in all subjects with steatosis and identified changes in serum markers (e.g., GGT) associated with presence of steatosis. Interpretation: This study demonstrates clear feasibility of NLP-based approaches to identify patients whose steatosis was indicated in imaging and pathology reports within a large healthcare system and uncovers undercoding of NAFLD in the general population. Identification of patients at risk could link them to improved care and outcomes. Funding: The study was funded by US and German funding sources that did provide financial support only and had no influence or control over the research process