Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding.

Applying methods in natural language processing on electronic health records (EHR) data is a growing field. Existing corpus and annotation focus on modeling textual features and relation prediction. However, there is a paucity of annotated corpus built to model clinical diagnostic thinking, a process involving text understanding, domain knowledge abstraction and reasoning. This work introduces a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning, and summarization. We created an annotated corpus based on an extensive collection of publicly available daily progress notes, a type of EHR documentation that is collected in time series in a problem-oriented format. The conventional format for a progress note follows a Subjective, Objective, Assessment and Plan heading (SOAP). We also define a new suite of tasks, Progress Note Understanding, with three tasks utilizing the three annotation stages. The novel suite of tasks was designed to train and evaluate future NLP models for clinical text understanding, clinical knowledge representation, inference, and summarization.

A scoping review of publicly available language tasks in clinical natural language processing.

OBJECTIVE: To provide a scoping review of papers on clinical natural language processing (NLP) shared tasks that use publicly available electronic health record data from a cohort of patients. MATERIALS AND METHODS: We searched 6 databases, including biomedical research and computer science literature databases. A round of title/abstract screening and full-text screening were conducted by 2 reviewers. Our method followed the PRISMA-ScR guidelines. RESULTS: A total of 35 papers with 48 clinical NLP tasks met inclusion criteria between 2007 and 2021. We categorized the tasks by the type of NLP problems, including named entity recognition, summarization, and other NLP tasks. Some tasks were introduced as potential clinical decision support applications, such as substance abuse detection, and phenotyping. We summarized the tasks by publication venue and dataset type. DISCUSSION: The breadth of clinical NLP tasks continues to grow as the field of NLP evolves with advancements in language systems. However, gaps exist with divergent interests between the general domain NLP community and the clinical informatics community for task motivation and design, and in generalizability of the data sources. We also identified issues in data preparation. CONCLUSION: The existing clinical NLP tasks cover a wide range of topics and the field is expected to grow and attract more attention from both general domain NLP and clinical informatics community. We encourage future work to incorporate multidisciplinary collaboration, reporting transparency, and standardization in data preparation. We provide a listing of all the shared task papers and datasets from this review in a GitLab repository.

Use of Iatrogenic Lipid Emulsion and Subsequent Plasmapheresis for the Treatment of Amitriptyline Overdose.

Plasmapheresis for the treatment of hypertriglyceridemia is relatively uncommon and mostly reported either in patients experiencing hypertriglyceridemia-induced acute pancreatitis or patients with therapy-resistant familial hypercholesterolemia. Standard therapies for hypertriglyceridemia include dietary modification and lipid-lowering medication. For severe hypertriglyceridemia, the risk of pancreatitis increases significantly as triglyceride levels increase above 1000 mg/dL, and current therapies are unable to reduce triglyceride levels rapidly enough. Here, we report a case of a 48-year-old male patient who presented to the emergency department due to an amitriptyline overdose. In addition to being started on IV sodium bicarbonate therapy, an intravenous 20% fat emulsion bolus at 1.5 mL/kg was administered followed by 0.25 mL/kg/min infusion for 4 hours as a strategy to absorb lipophilic amitriptyline. Two days posttreatment, he was noted to have substantial hypertriglyceridemia (serum triglycerides: 6,475 mg/dL). His amylase was within the normal range at 37 U/L (reference range: 20-100 U/L), his lipase was low at 40 U/L (reference range: 75-390 U/L), and he was without evidence of any clinical sequelae secondary to hypertriglyceridemia (e.g., pancreatitis). Due to the severity of his hypertriglyceridemia, plasmapheresis was initiated urgently for rapid reduction in serum triglyceride levels to prevent pancreatitis and end-organ damage. He underwent a 1-plasma volume exchange with 5% albumin as the replacement fluid. This reduced his triglyceride levels to 185 mg/dL (reference range: 3-149 mg/dL). His symptoms secondary to his amitriptyline overdose were also resolved. Here, we report a 2-step process of intravenous lipid emulsion followed by plasmapheresis for amitriptyline overdose.