There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model—GatorTron—using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og.

Objective Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Background The purpose of this study was to compare the effects of scalp nerve block (SNB) and local anesthetic infiltration (LA) with 0.75% ropivacaine on postoperative inflammatory response, intraoperative hemodynamic response, and postoperative pain control in patients undergoing craniotomy. Methods Fifty-seven patients were admitted for elective craniotomy for surgical clipping of a cerebral aneurysm. They were randomly divided into three groups: Group S (SNB with 15 mL of 0.75% ropivacaine), group I (LA with 15 mL of 0.75% ropivacaine) and group C (that only received routine intravenous analgesia). Pro-inflammatory cytokine levels in plasma for 72 h postoperatively, hemodynamic response to skin incision, and postoperative pain intensity were measured. Results The SNB with 0.75% ropivacaine not only decreased IL-6 levels in plasma 6 h after craniotomy but also decreased plasma CRP levels and increased plasma IL-10 levels 12 and 24 h after surgery compared to LA and routine analgesia. There were significant increases in mean arterial pressure 2 and 5 mins after the incision and during dura opening in Groups I and C compared with Group S. Group S had lower postoperative pain intensity, longer duration before the first dose of oxycodone, less consumption of oxycodone and lower incidence of PONV through 48 h postoperatively than Groups I and C. Conclusion Preoperative SNB attenuated inflammatory response to craniotomy for cerebral aneurysms, blunted the hemodynamic response to scalp incision, and controlled postoperative pain better than LA or routine analgesia. Trial registration Clinicaltrials.gov NCT03073889 (PI:Xi Yang; date of registration:08/03/2017).

Misinformation of COVID-19 is prevalent on social media as the pandemic unfolds, and the associated risks are extremely high. Thus, it is critical to detect and combat such misinformation. Recently, deep learning models using natural language processing techniques, such as BERT (Bidirectional Encoder Representations from Transformers), have achieved great successes in detecting misinformation. In this paper, we proposed an explainable natural language processing model based on DistilBERT and SHAP (Shapley Additive exPlanations) to combat misinformation about COVID-19 due to their efficiency and effectiveness. First, we collected a dataset of 984 claims about COVID-19 with fact checking. By augmenting the data using back-translation, we doubled the sample size of the dataset and the DistilBERT model was able to obtain good performance