Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction

• URL: http://arxiv.org/abs/2501.17326v1
• Date: Tue, 28 Jan 2025 22:38:45 GMT
• Title: Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction
• Authors: Mingyu Derek Ma, Xiaoxuan Wang, Yijia Xiao, Anthony Cuturrufo, Vijay S Nori, Eran Halperin, Wei Wang,
• Abstract summary: We introduce MERA, a clinical diagnosis prediction model that bridges pertaining natural language knowledge with medical practice. We apply hierarchical contrastive learning on a disease candidate ranking list to alleviate the large decision space issue.
• Score: 10.403187385041702
• License:
• Abstract: Clinical diagnosis prediction models, when provided with a patient's medical history, aim to detect potential diseases early, facilitating timely intervention and improving prognostic outcomes. However, the inherent scarcity of patient data and large disease candidate space often pose challenges in developing satisfactory models for this intricate task. The exploration of leveraging Large Language Models (LLMs) for encapsulating clinical decision processes has been limited. We introduce MERA, a clinical diagnosis prediction model that bridges pertaining natural language knowledge with medical practice. We apply hierarchical contrastive learning on a disease candidate ranking list to alleviate the large decision space issue. With concept memorization through fine-tuning, we bridge the natural language clinical knowledge with medical codes. Experimental results on MIMIC-III and IV datasets show that MERA achieves the state-of-the-art diagnosis prediction performance and dramatically elevates the diagnosis prediction capabilities of generative LMs.

Related papers

• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning. Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• Conversational Disease Diagnosis via External Planner-Controlled Large Language Models [18.93345199841588]
  This study presents a LLM-based diagnostic system that enhances planning capabilities by emulating doctors. By utilizing real patient electronic medical record data, we constructed simulated dialogues between virtual patients and doctors.
  arXiv  Detail & Related papers  (2024-04-04T06:16:35Z)
• Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data [7.815738943706123]
  Large Language Models (LLMs) are traditionally tailored for natural language processing. This research investigates the adaptability of LLMs, like GPT-4, to EHR data. In response to the longitudinal, sparse, and knowledge-infused nature of EHR data, our prompting approach involves taking into account specific characteristics.
  arXiv  Detail & Related papers  (2024-01-25T20:14:50Z)
• CPLLM: Clinical Prediction with Large Language Models [0.07083082555458872]
  We present a method that involves fine-tuning a pre-trained Large Language Model (LLM) for clinical disease and readmission prediction. For diagnosis prediction, we predict whether patients will be diagnosed with a target disease during their next visit or in the subsequent diagnosis, leveraging their historical diagnosis records. Our experiments have shown that our proposed method, CPLLM, surpasses all the tested models in terms of PR-AUC and ROC-AUC metrics.
  arXiv  Detail & Related papers  (2023-09-20T13:24:12Z)
• DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing [5.022185333260402]
  Diagnostic Reasoning Benchmarks, DR.BENCH, is a new benchmark for developing and evaluating cNLP models with clinical diagnostic reasoning ability. DR.BENCH is the first clinical suite of tasks designed to be a natural language generation framework to evaluate pre-trained language models.
  arXiv  Detail & Related papers  (2022-09-29T16:05:53Z)
• Literature-Augmented Clinical Outcome Prediction [10.46990394710927]
  We introduce techniques to help bridge this gap between EBM and AI-based clinical models. We propose a novel system that automatically retrieves patient-specific literature based on intensive care (ICU) patient information. Our model is able to substantially boost predictive accuracy on three challenging tasks in comparison to strong recent baselines.
  arXiv  Detail & Related papers  (2021-11-16T11:19:02Z)
• VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models [85.4333256782337]
  VBridge is a visual analytics tool that seamlessly incorporates machine learning explanations into clinicians' decision-making workflow. We identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence. We demonstrated the effectiveness of VBridge through two case studies and expert interviews with four clinicians.
  arXiv  Detail & Related papers  (2021-08-04T17:34:13Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

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.