Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection

• URL: http://arxiv.org/abs/2401.12988v1
• Date: Tue, 16 Jan 2024 13:54:43 GMT
• Title: Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection
• Authors: Haoxin Liu, Wenli Zhang, Jiaheng Xie, Buomsoo Kim, Zhu Zhang, Yidong Chai
• Abstract summary: This study harnesses state-of-the-art AI technology for chronic disease management, specifically in detecting various mental disorders through user-generated textual content. We propose a novel framework that leverages advanced AI techniques, including large language models and multi-prompt engineering. We evaluate our method using four mental disorders, which are prevalent chronic diseases worldwide, as research cases.
• Score: 11.877874209616195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study harnesses state-of-the-art AI technology for chronic disease management, specifically in detecting various mental disorders through user-generated textual content. Existing studies typically rely on fully supervised machine learning, which presents challenges such as the labor-intensive manual process of annotating extensive training data for each disease and the need to design specialized deep learning architectures for each problem. To address such challenges, we propose a novel framework that leverages advanced AI techniques, including large language models and multi-prompt engineering. Specifically, we address two key technical challenges in data-driven chronic disease management: (1) developing personalized prompts to represent each user's uniqueness and (2) incorporating medical knowledge into prompts to provide context for chronic disease detection, instruct learning objectives, and operationalize prediction goals. We evaluate our method using four mental disorders, which are prevalent chronic diseases worldwide, as research cases. On the depression detection task, our method (F1 = 0.975~0.978) significantly outperforms traditional supervised learning paradigms, including feature engineering (F1 = 0.760) and architecture engineering (F1 = 0.756). Meanwhile, our approach demonstrates success in few-shot learning, i.e., requiring only a minimal number of training examples to detect chronic diseases based on user-generated textual content (i.e., only 2, 10, or 100 subjects). Moreover, our method can be generalized to other mental disorder detection tasks, including anorexia, pathological gambling, and self-harm (F1 = 0.919~0.978).

Related papers

• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques [0.5172964916120903]
  This survey reviews the most relevant methodologies that use deep learning techniques to automate the cognitive decline estimation task. We discuss the key features and advantages of each modality and methodology, including state-of-the-art approaches like Transformer architecture and foundation models. In most cases, the textual modality achieves the best results and is the most relevant for detecting cognitive decline.
  arXiv  Detail & Related papers  (2024-10-24T17:59:21Z)
• SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques [9.146311285410631]
  Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies.
  arXiv  Detail & Related papers  (2024-10-17T22:04:32Z)
• Evaluation of OpenAI o1: Opportunities and Challenges of AGI [112.0812059747033]
  o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance. The model excelled in tasks requiring intricate reasoning and knowledge integration across various fields. Overall results indicate significant progress towards artificial general intelligence.
  arXiv  Detail & Related papers  (2024-09-27T06:57:00Z)
• Enhanced Detection of Conversational Mental Manipulation Through Advanced Prompting Techniques [23.710371961866198]
  We implement Chain-of-Thought prompting with Zero-Shot and Few-Shot settings on a binary mental manipulation detection task. Our primary objective is to decipher why certain prompting techniques display superior performance.
  arXiv  Detail & Related papers  (2024-08-14T17:23:12Z)
• 3M-Health: Multimodal Multi-Teacher Knowledge Distillation for Mental Health Detection [9.469887408109251]
  We introduce a Multimodal and Multi-Teacher Knowledge Distillation model for Mental Health Classification. Unlike conventional approaches that often rely on simple concatenation to integrate diverse features, our model addresses the challenge of appropriately representing inputs of varying natures.
  arXiv  Detail & Related papers  (2024-07-12T06:22:45Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• Determining the Difficulties of Students With Dyslexia via Virtual Reality and Artificial Intelligence: An Exploratory Analysis [0.0]
  The VRAIlexia project has been created to tackle this issue by proposing two different tools. The first one has been created and is being distributed among dyslexic students in Higher Education Institutions, for the conduction of specific psychological and psychometric tests. The second tool applies specific artificial intelligence algorithms to the data gathered via the application and other surveys.
  arXiv  Detail & Related papers  (2024-01-15T20:26:09Z)
• Multimodal Stress Detection Using Facial Landmarks and Biometric Signals [1.0124625066746595]
  Multi-modal learning aims to capitalize on the strength of each modality rather than relying on a single signal. This paper proposes a multi-modal learning approach for stress detection that integrates facial landmarks and biometric signals.
  arXiv  Detail & Related papers  (2023-11-06T23:20:30Z)
• Machine Unlearning: A Survey [56.79152190680552]
  A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
  arXiv  Detail & Related papers  (2023-06-06T10:18:36Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining [121.89793208683625]
  Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks. We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
  arXiv  Detail & Related papers  (2023-04-26T01:26:19Z)
• AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning [72.2614468437919]
  We present a machine learning framework, AutoPrognosis 2.0, to develop diagnostic and prognostic models. We provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank. Our risk score has been implemented as a web-based decision support tool and can be publicly accessed by patients and clinicians worldwide.
  arXiv  Detail & Related papers  (2022-10-21T16:31:46Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Time Series Prediction using Deep Learning Methods in Healthcare [0.0]
  Traditional machine learning methods face two main challenges in dealing with healthcare predictive analytics tasks. The high-dimensional nature of healthcare data needs labor-intensive processes to select an appropriate set of features for each new task. Recent deep learning methods have shown promising performance for various healthcare prediction tasks.
  arXiv  Detail & Related papers  (2021-08-30T18:14:27Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare [16.40827965484983]
  We propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. We conduct experiments on two important healthcare problems to show the competitive prediction performance of the proposed method.
  arXiv  Detail & Related papers  (2021-05-16T23:11:11Z)
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [76.20963684020145]
  Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. Bizarre approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning.
  arXiv  Detail & Related papers  (2020-11-12T06:41:05Z)
• A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis [12.02718865835448]
  AdaptiveNet is a novel recurrent neural network architecture, which can deal with multiple lists of different events. We employ the architecture to the problem of disease progression prediction in rheumatoid arthritis using the Swiss Clinical Quality Management registry.
  arXiv  Detail & Related papers  (2020-08-14T11:19:32Z)

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.