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Found 14 papers, 3 papers with code
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.
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
Generative models have been showing potential for producing data in mass.
The impact of responding to patient messages with large language model assistance
Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously.
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.
Considerations for health care institutions training large language models on electronic health records
Large language models (LLMs) like ChatGPT have excited scientists across fields; in medicine, one source of excitement is the potential applications of LLMs trained on electronic health record (EHR) data.
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.
High-Dimensional Feature Selection for Genomic Datasets
We cluster features first based on $\Delta\mathbf{x}$ and then using the entropy of features.
