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Found 30 papers, 2 papers with code
EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain
We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representation of biomedical terminology (PubMedBERT) on a clinical corpus along with a novel entity-centric masking strategy to infuse domain knowledge in the learning process.
Defining and Learning Refined Temporal Relations in the Clinical Narrative
We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative.
Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models
In this work, we propose a new NLP task that aims to generate a list of problems in a patient’s daily care plan using input from the provider’s progress notes during hospitalization.
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering.
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
Generative models have been showing potential for producing data in mass.
Leveraging A Medical Knowledge Graph into Large Language Models for Diagnosis Prediction
In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr. Knows, inspired by the clinical diagnostic reasoning process.
Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023.
Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning
Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors.
Progress Note Understanding -- Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 Shared Task
The goal of the task was to identify and prioritize diagnoses as the first steps in diagnostic decision support to find the most relevant information in long documents like daily progress notes.
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing
The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce the cognitive burden and medical error has not been investigated.
Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models
In this work, we propose a new NLP task that aims to generate a list of problems in a patient's daily care plan using input from the provider's progress notes during hospitalization.
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding
This work introduces a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning, and summarization.
A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing
Conclusions: The existing clinical NLP tasks cover a wide range of topics and the field will continue to grow and attract more attention from both general domain NLP and clinical informatics community.
Classifying Long Clinical Documents with Pre-trained Transformers
Automatic phenotyping is a task of identifying cohorts of patients that match a predefined set of criteria.
A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction
Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text.
Two-stage Federated Phenotyping and Patient Representation Learning
A large percentage of medical information is in unstructured text format in electronic medical record systems.
Extracting Adverse Drug Event Information with Minimal Engineering
In this paper we describe an evaluation of the potential of classical information extraction methods to extract drug-related attributes, including adverse drug events, and compare to more recently developed neural methods.
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance.
Learning Patient Representations from Text
Mining electronic health records for patients who satisfy a set of predefined criteria is known in medical informatics as phenotyping.
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing.
