Search Results for author:
Found 45 papers, 4 papers with code
A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports
This is one of the first attempts focusing on CDCR in the clinical domain and holds potential in benefiting physicians and clinical research through long-term tracking of radiology findings.
coreference-resolution
Cross Document Coreference Resolution
A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports
Radiology reports contain important clinical information about patients which are often tied through spatial expressions.
RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports
We present a radiology question answering dataset, RadQA, with 3074 questions posed against radiology reports and annotated with their corresponding answer spans (resulting in a total of 6148 question-answer evidence pairs) by physicians.
Ranked #1 on
Reading Comprehension
on RadQA
Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases
Clinical trials are pivotal in medical research, and NLP can enhance their success, with application in recruitment.
Question Answering for Electronic Health Records: A Scoping Review of datasets and models
Because of the differences in data format and modality, this differs greatly from other medical QA tasks that employ medical websites or scientific papers to retrieve answers, making it critical to research EHR question answering.
Text Classification of Cancer Clinical Trial Eligibility Criteria
Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language.
Evaluation of AI Chatbots for Patient-Specific EHR Questions
This paper investigates the use of artificial intelligence chatbots for patient-specific question answering (QA) from clinical notes using several large language model (LLM) based systems: ChatGPT (versions 3. 5 and 4), Google Bard, and Claude.
Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes
This is the first work to represent and extract a wide variety of clinical information in ophthalmology.
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
Results: Using baseline prompts, GPT-3. 5 and GPT-4 achieved relaxed F1 scores of 0. 634, 0. 804 for MTSamples, and 0. 301, 0. 593 for VAERS.
Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research
Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc.
Toward a Neural Semantic Parsing System for EHR Question Answering
Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR question answering (QA).
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries
Our goal is to provide a benchmark dataset for multi-modal QA systems, and to open up new avenues of research in improving question answering over EHR structured data by using context from unstructured clinical data.
Deep learning-based NLP Data Pipeline for EHR Scanned Document Information Extraction
We also evaluated the combinations of image preprocessing methods (gray-scaling, dilate & erode, increased contrast by 20%, increased contrast by 60%), and two deep learning architectures (with and without structured input that provides document layout information).
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID
We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19.
Three-level Hierarchical Transformer Networks for Long-sequence and Multiple Clinical Documents Classification
We present a Three-level Hierarchical Transformer Network (3-level-HTN) for modeling long-term dependencies across clinical notes for the purpose of patient-level prediction.
Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data
The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing.
Extracting Adherence Information from Electronic Health Records
Patient adherence is a critical factor in health outcomes.
Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping
Classifying fine-grained ischemic stroke phenotypes relies on identifying important clinical information.
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
We show the importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review.
Extracting Concepts for Precision Oncology from the Biomedical Literature
Finally, we propose additional directions for research for improving extraction performance and utilizing the NLP system in downstream precision oncology applications.
RadLex Normalization in Radiology Reports
Further, no study to date has attempted to leverage RadLex for standardization.
Patient Cohort Retrieval using Transformer Language Models
Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks.
An Evaluation of Two Commercial Deep Learning-Based Information Retrieval Systems for COVID-19 Literature
This has led to both corpora for biomedical articles related to COVID-19 (such as the CORD-19 corpus (Wang et al., 2020)) as well as search engines to query such data.
Towards an Ontology-based Medication Conversational Agent for PrEP and PEP
ABSTRACT: HIV (human immunodeficiency virus) can damage a human{'}s immune system and cause Acquired Immunodeficiency Syndrome (AIDS) which could lead to severe outcomes, including death.
TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic.
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering
We evaluate the performance of various Transformer language models, when pre-trained and fine-tuned on different combinations of open-domain, biomedical, and clinical corpora on two clinical question answering (QA) datasets (CliCR and emrQA).
Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports
The framework is adopted from the existing SpatialNet representation in the general domain with the aim to generate more accurate representations of spatial language used by radiologists.
Understanding Spatial Language in Radiology: Representation Framework, Annotation, and Spatial Relation Extraction from Chest X-ray Reports using Deep Learning
We define a representation framework for extracting spatial information from radiology reports (Rad-SpRL).
Extraction of Lactation Frames from Drug Labels and LactMed
This paper describes a natural language processing (NLP) approach to extracting lactation-specific drug information from two sources: FDA-mandated drug labels and the NLM Drugs and Lactation Database (LactMed).
A Paraphrase Generation System for EHR Question Answering
This paper proposes a dataset and method for automatically generating paraphrases for clinical questions relating to patient-specific information in electronic health records (EHRs).
A frame semantic overview of NLP-based information extraction for cancer-related EHR notes
Conclusion: The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes.
Enhancing Clinical Concept Extraction with Contextual Embeddings
We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings, and compare these on four concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015.
Ranked #1 on
Clinical Concept Extraction
on 2010 i2b2/VA
Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP
The proliferation of deep learning methods in natural language processing (NLP) and the large amounts of data they often require stands in stark contrast to the relatively data-poor clinical NLP domain.
Annotating Named Entities in Consumer Health Questions
We describe a corpus of consumer health questions annotated with named entities.
Annotating Logical Forms for EHR Questions
From these, 468 specific questions are found containing 259 unique medical concepts and requiring 53 unique predicates to represent the logical forms.
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.
Annotating Question Decomposition on Complex Medical Questions
This paper presents a method for annotating question decomposition on complex medical questions.
EmpaTweet: Annotating and Detecting Emotions on Twitter
The rise of micro-blogging in recent years has resulted in significant access to emotion-laden text.
Annotating Spatial Containment Relations Between Events
Events form complex predicate-argument structures that model the participants in the event, their roles, as well as the temporal and spatial grounding.
