WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV
- URL: http://arxiv.org/abs/2405.11255v1
- Date: Sat, 18 May 2024 10:56:45 GMT
- Title: WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV
- Authors: Hendrik Damm, Tabea M. G. Pakull, Bahadır Eryılmaz, Helmut Becker, Ahmad Idrissi-Yaghir, Henning Schäfer, Sergej Schultenkämper, Christoph M. Friedrich,
- Abstract summary: This study aims to leverage state of the art language models to automate generating the "Brief Hospital Course" and "Discharge Instructions" sections of Discharge Summaries.
We investigate how automation can improve documentation accuracy, alleviate clinician burnout, and enhance operational efficacy in healthcare facilities.
- Score: 0.38084074204911494
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This study aims to leverage state of the art language models to automate generating the "Brief Hospital Course" and "Discharge Instructions" sections of Discharge Summaries from the MIMIC-IV dataset, reducing clinicians' administrative workload. We investigate how automation can improve documentation accuracy, alleviate clinician burnout, and enhance operational efficacy in healthcare facilities. This research was conducted within our participation in the Shared Task Discharge Me! at BioNLP @ ACL 2024. Various strategies were employed, including few-shot learning, instruction tuning, and Dynamic Expert Selection (DES), to develop models capable of generating the required text sections. Notably, utilizing an additional clinical domain-specific dataset demonstrated substantial potential to enhance clinical language processing. The DES method, which optimizes the selection of text outputs from multiple predictions, proved to be especially effective. It achieved the highest overall score of 0.332 in the competition, surpassing single-model outputs. This finding suggests that advanced deep learning methods in combination with DES can effectively automate parts of electronic health record documentation. These advancements could enhance patient care by freeing clinician time for patient interactions. The integration of text selection strategies represents a promising avenue for further research.
Related papers
- STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical [58.79671189792399]
STLLaVA-Med is designed to train a policy model capable of auto-generating medical visual instruction data.
We validate the efficacy and data efficiency of STLLaVA-Med across three major medical Visual Question Answering (VQA) benchmarks.
arXiv Detail & Related papers (2024-06-28T15:01:23Z) - Automated Information Extraction from Thyroid Operation Narrative: A Comparative Study of GPT-4 and Fine-tuned KoELECTRA [1.137357582959183]
This study focuses on the transformative capabilities of the fine-tuned KoELECTRA model in comparison to the GPT-4 model.
The study leverages advanced natural language processing (NLP) techniques to foster a paradigm shift towards more sophisticated data processing systems.
arXiv Detail & Related papers (2024-06-12T06:44:05Z) - GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models [1.123722364748134]
This paper introduces GAMedX, a Named Entity Recognition (NER) approach utilizing Large Language Models (LLMs)
The methodology integrates open-source LLMs for NER, utilizing chained prompts and Pydantic schemas for structured output to navigate the complexities of specialized medical jargon.
The findings reveal significant ROUGE F1 score on one of the evaluation datasets with an accuracy of 98%.
arXiv Detail & Related papers (2024-05-31T02:53:22Z) - Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology [0.6397820821509177]
We introduce an alternative approach to multimodal medical AI that utilizes the generalist capabilities of a large language model (LLM) as a central reasoning engine.
This engine autonomously coordinates and deploys a set of specialized medical AI tools.
We show that the system has a high capability in employing appropriate tools (97%), drawing correct conclusions (93.6%), and providing complete (94%), and helpful (89.2%) recommendations for individual patient cases.
arXiv Detail & Related papers (2024-04-06T15:50:19Z) - NOTE: Notable generation Of patient Text summaries through Efficient
approach based on direct preference optimization [0.0]
"NOTE" stands for "Notable generation Of patient Text summaries through an Efficient approach based on direct preference optimization"
Patient events are sequentially combined and used to generate a discharge summary for each hospitalization.
Note can be utilized to generate various summaries not only discharge summaries but also throughout a patient's journey.
arXiv Detail & Related papers (2024-02-19T06:43:25Z) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z) - README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP [9.432205523734707]
We introduce a new task of automatically generating lay definitions, aiming to simplify medical terms into patient-friendly lay language.
We first created the dataset, an extensive collection of over 50,000 unique (medical term, lay definition) pairs and 300,000 mentions.
We have also engineered a data-centric Human-AI pipeline that synergizes data filtering, augmentation, and selection to improve data quality.
arXiv Detail & Related papers (2023-12-24T23:01:00Z) - Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning.
They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health.
Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
arXiv Detail & Related papers (2023-05-30T22:05:11Z) - Development and validation of a natural language processing algorithm to
pseudonymize documents in the context of a clinical data warehouse [53.797797404164946]
The study highlights the difficulties faced in sharing tools and resources in this domain.
We annotated a corpus of clinical documents according to 12 types of identifying entities.
We build a hybrid system, merging the results of a deep learning model as well as manual rules.
arXiv Detail & Related papers (2023-03-23T17:17:46Z) - Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine [68.7814360102644]
We propose the Re$3$Writer method with retrieval-augmented generation and knowledge-grounded reasoning.
We demonstrate the effectiveness of our method in generating patient discharge instructions.
arXiv Detail & Related papers (2022-10-23T16:34:39Z) - Benchmarking Automated Clinical Language Simplification: Dataset,
Algorithm, and Evaluation [48.87254340298189]
We construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches.
We propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance.
arXiv Detail & Related papers (2020-12-04T06:09:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.