MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases

Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks like medical question answering (QA). Moreover, they tend to function as "black-boxes," making it challenging to modify their behavior. Addressing this, our study delves into model editing utilizing in-context learning, aiming to improve LLM responses without the need for fine-tuning or retraining. Specifically, we propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then we incorporate them into the query prompt for the LLM. Focusing on medical QA using the MedQA-SMILE dataset, we evaluate the impact of different retrieval models and the number of facts provided to the LLM. Notably, our edited Vicuna model exhibited an accuracy improvement from 44.46% to 48.54%. This work underscores the potential of model editing to enhance LLM performance, offering a practical approach to mitigate the challenges of black-box LLMs.Comment: 6 page

CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study

Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research. Recently, Large Language Models (LLMs) such as ChatGPT have achieved tremendous success in various downstream tasks thanks to their promising performance in language understanding, inference, and generation. It is then natural to test their feasibility in solving the cohort recruitment task, which involves the classification of a given paragraph of medical text into disease label(s). However, when applied to knowledge-intensive problem settings such as medical text classification, where the LLMs are expected to understand the decision made by human experts and accurately identify the implied disease labels, the LLMs show a mediocre performance. A possible explanation is that, by only using the medical text, the LLMs neglect to use the rich context of additional information that languages afford. To this end, we propose to use a knowledge graph as auxiliary information to guide the LLMs in making predictions. Moreover, to further boost the LLMs adapt to the problem setting, we apply a chain-of-thought (CoT) sample selection strategy enhanced by reinforcement learning, which selects a set of CoT samples given each individual medical report. Experimental results and various ablation studies show that our few-shot learning method achieves satisfactory performance compared with fine-tuning strategies and gains superb advantages when the available data is limited. The code and sample dataset of the proposed CohortGPT model is available at: https://anonymous.4open.science/r/CohortGPT-4872/Comment: 16 pages, 10 figure

Reliability analysis of all components in structural systems based on adaptive point estimate method and the principle of maximum entropy

Date: May 14 (Mon), 2018Place: ROHM Plaza Meeting Room, Kyoto University Katsura Campus, Kyoto, JAPANSupported by JSPS-NSFC Japan-China Scientific Cooperation ProjectOrganized by Structural Engineering of Buildings Laboratory, Department of Architecture and Architectural Engineering, Kyoto Universit

Toll-like receptor 9 interaction with CpG ODN – An in silico analysis approach

BACKGROUND: Toll-like receptor 9 (TLR9) recognises unmethylated CpG DNA and activates a signalling cascade, leading to the production of inflammatory cytokines such as TNF-α, IL-1, IL-6 and IL-12 via the adaptor protein MyD88. However, the specific sequence and structural requirements of the CpG DNA for the recognition of and binding to TLR9 are unknown. Moreover, the 3D structures of TLR9 and the TLR9-ODN complex have not been determined. In this study, we propose a reliable model of the interaction of the TLR9 ECD with CpG ODN using bioinformatics tools. RESULTS: The three-dimensional structures of two TLR9 ECD-CpG ODN complexes were constructed using a homology modelling and docking strategy. Based on the models of these complexes, the TLR9 ECD-CpG ODN interaction patterns were calculated. The results showed that the interface between the human TLR9 and the CpG ODN molecule is geometrically complementary. The computed molecular interactions indicated that LRR11 is the main region of TLR9 that binds to CpG ODN and that five positively charged residues within LRR11 are involved in the binding of the TLR9 ECD to the CpG ODN. Observations in the close-up view of these interactions indicated that these five positively charged residues contribute differently to the binding region within the TLR9 ECD-CpG ODN complex. 337Arg and 338Lys reside in the binding sites of ODN, forming hydrogen bonds and direct contacts with the CpG ODN, whereas 347Lys, 348Arg, and 353His do not directly contact the CpG ODN. These results are in agreement with previously reported experimental data. CONCLUSION: In this study, we present two structural models for the human and mouse TLR9 ECD in a complex with CpG ODN. Some features predicted by this model are consistent with previously reported experimental data. This complex model may lead to a better understanding of the function of TLR9 and its interaction with CpG ODN and will improve our understanding of TLR9-ligand interaction in general

Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training

Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a domain-specific knowledge graph from large collections of textual data without manual annotations is still an urgent problem to be solved. In response, we propose an integrated framework for adapting and re-learning knowledge graphs from one coarse domain (biomedical) to a finer-define domain (oncology). In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation. Consequently, no manual data annotation is required to train the model. We introduce a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples. Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graph efficiently

Reliability assessment of deteriorating structures using Bayesian updated probability density evolution method (PDEM)

Date: May 14 (Mon), 2018Place: ROHM Plaza Meeting Room, Kyoto University Katsura Campus, Kyoto, JAPANSupported by JSPS-NSFC Japan-China Scientific Cooperation ProjectOrganized by Structural Engineering of Buildings Laboratory, Department of Architecture and Architectural Engineering, Kyoto Universit

MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation

In recent years, the Segmentation Anything Model (SAM) has attracted considerable attention as a foundational model well-known for its robust generalization capabilities across various downstream tasks. However, SAM does not exhibit satisfactory performance in the realm of medical image analysis. In this study, we introduce the first study on adapting SAM on video segmentation, called MediViSTA-SAM, a novel approach designed for medical video segmentation. Given video data, MediViSTA, spatio-temporal adapter captures long and short range temporal attention with cross-frame attention mechanism effectively constraining it to consider the immediately preceding video frame as a reference, while also considering spatial information effectively. Additionally, it incorporates multi-scale fusion by employing a U-shaped encoder and a modified mask decoder to handle objects of varying sizes. To evaluate our approach, extensive experiments were conducted using state-of-the-art (SOTA) methods, assessing its generalization abilities on multi-vendor in-house echocardiography datasets. The results highlight the accuracy and effectiveness of our network in medical video segmentation

SAMAug: Point Prompt Augmentation for Segment Anything Model

This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information about the user's intention to SAM. Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu

Artificial General Intelligence for Medical Imaging

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models. We emphasize the importance of integrating clinical expertise, domain knowledge, and multimodal capabilities into AGI models. In addition, we lay out key roadmaps that guide the development and deployment of healthcare AGI models. Throughout the review, we provide critical perspectives on the potential challenges and pitfalls associated with deploying large-scale AGI models in the medical field. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare and beyond