Search Results for author:
Found 16 papers, 4 papers with code
Identifying Distorted Thinking in Patient-Therapist Text Message Exchanges by Leveraging Dynamic Multi-Turn Context
In this work, we automatically identify distorted thinking in text-based patient-therapist exchanges, investigating the role of conversation history (context) in distortion prediction.
Improving Classification of Infrequent Cognitive Distortions: Domain-Specific Model vs. Data Augmentation
Performance improvements with an augmented model, MentalBERT, exceed those obtained with data augmentation.
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.
Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning
Our innovative RLHF reward function surpassed existing RL text simplification reward functions in effectiveness.
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.
MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models
The Reddit Health Online Talk (RedHot) corpus contains posts from medical condition-related subreddits with annotations characterizing the patient experience and medical conditions.
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.
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.
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.
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.
