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Found 29 papers, 8 papers with code
Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization
Medical conversations from patient visits are routinely summarized into clinical notes for documentation of clinical care.
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods
In this work, we present a novel annotated corpus, the Pediatric Social History Annotation Corpus (PedSHAC), and evaluate the automatic extraction of detailed SDoH representations using fine-tuned and in-context learning methods with Large Language Models (LLMs).
A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models
Here, we introduce the Corpus of Annotated Medical Imaging Reports (CAMIR), which includes 609 annotated radiology reports from three imaging modality types: Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography-Computed Tomography.
Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication
We achieve F1 scores of 0. 50 for ASA Physical Status Classification, 0. 81 for ICU admission, and 0. 86 for hospital mortality.
Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts
This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data.
Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning
Social determinants of health (SDOH) documented in the electronic health record through unstructured text are increasingly being studied to understand how SDOH impacts patient health outcomes.
ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation
Here we present the Ambient Clinical Intelligence Benchmark (ACI-BENCH) corpus, the largest dataset to date tackling the problem of AI-assisted note generation from visit dialogue.
LeafAI: query generator for clinical cohort discovery rivaling a human programmer
Conclusions: Our work contributes a state-of-the-art data model-agnostic query generation system capable of conditional reasoning using a knowledge base.
The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health
Results: A total of 15 teams participated, and the top teams utilized pretrained deep learning LM.
Leveraging Natural Language Processing to Augment Structured Social Determinants of Health Data in the Electronic Health Record
In an EHR case study, we applied the SDOH extractor to a large clinical data set with 225, 089 patients and 430, 406 notes with social history sections and compared the extracted SDOH information with existing structured data.
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes
To reduce reliance on domain-specific features, we propose a domain generalization method that dynamically masks frequent symptoms words in the source domain.
Extracting Medication Changes in Clinical Narratives using Pre-trained Language Models
An accurate and detailed account of patient medications, including medication changes within the patient timeline, is essential for healthcare providers to provide appropriate patient care.
The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria
In order to identify potential participants at scale, these criteria must first be translated into queries on clinical databases, which can be labor-intensive and error-prone.
Event-based clinical findings extraction from radiology reports with pre-trained language model
In this paper, we present a new corpus of radiology reports annotated with clinical findings.
Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model
Medical imaging reports distill the findings and observations of radiologists, creating an unstructured textual representation of unstructured medical images.
Identifying ARDS using the Hierarchical Attention Network with Sentence Objectives Framework
We explore the automatic identification of ARDS indicators and confounding factors in free-text chest radiograph reports.
Transferability of Neural Network Clinical De-identification Systems
We measured: transferability from external sources; transferability across note types; the contribution of external source data when in-domain training data are available; and transferability across institutions.
Performance of Automatic De-identification Across Different Note Types
Free-text clinical notes detail all aspects of patient care and have great potential to facilitate quality improvement and assurance initiatives as well as advance clinical research.
Jointly Learning Clinical Entities and Relations with Contextual Language Models and Explicit Context
We hypothesize that explicit integration of contextual information into an Multi-task Learning framework would emphasize the significance of context for boosting performance in jointly learning Named Entity Recognition (NER) and Relation Extraction (RE).
Extracting COVID-19 Diagnoses and Symptoms From Clinical Text: A New Annotated Corpus and Neural Event Extraction Framework
In a secondary use application, we explored the prediction of COVID-19 test results using structured patient data (e. g. vital signs and laboratory results) and automatically extracted symptom information.
Automatic Assignment of Radiology Examination Protocols Using Pre-trained Language Models with Knowledge Distillation
Selecting radiology examination protocol is a repetitive, and time-consuming process.
Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation
Despite advances in natural language processing, automating clinical note generation from a clinic visit conversation is a largely unexplored area of research.
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction
The Social History Annotation Corpus (SHAC) includes 4, 480 social history sections with detailed annotation for 12 SDOH characterizing the status, extent, and temporal information of 18K distinct events.
Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset
Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error.
Clinical Event Detection with Hybrid Neural Architecture
Event detection from clinical notes has been traditionally solved with rule based and statistical natural language processing (NLP) approaches that require extensive domain knowledge and feature engineering.
Annotating and Detecting Medical Events in Clinical Notes
Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare.
Annotating Clinical Events in Text Snippets for Phenotype Detection
Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare.
