MedDM:LLM-executable clinical guidance tree for clinical decision-making

It is becoming increasingly emphasis on the importance of LLM participating in clinical diagnosis decision-making. However, the low specialization refers to that current medical LLMs can not provide specific medical advice, which are more like a medical Q\&A. And there is no suitable clinical guidance tree data set that can be used directly with LLM. To address this issue, we first propose LLM-executavle clinical guidance tree(CGT), which can be directly used by large language models, and construct medical diagnostic decision-making dataset (MedDM), from flowcharts in clinical practice guidelines. We propose an approach to screen flowcharts from medical literature, followed by their identification and conversion into standardized diagnostic decision trees. Constructed a knowledge base with 1202 decision trees, which came from 5000 medical literature and covered 12 hospital departments, including internal medicine, surgery, psychiatry, and over 500 diseases.Moreover, we propose a method for reasoning on LLM-executable CGT and a Patient-LLM multi-turn dialogue framework

ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning

While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust representation training methods that are designed for traditional RL become less effective when applied to GCRL. To address this challenge, we first propose the Semi-Contrastive Representation attack, a novel approach inspired by the adversarial contrastive attack. Unlike existing attacks in RL, it only necessitates information from the policy function and can be seamlessly implemented during deployment. Then, to mitigate the vulnerability of existing GCRL algorithms, we introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer to improve the adversarial robustness of the underlying RL agent against various types of perturbations. Extensive experiments validate the superior performance of our attack and defence methods across multiple state-of-the-art GCRL algorithms. Our tool ReRoGCRL is available at https://github.com/TrustAI/ReRoGCRL.Comment: This paper has been accepted in AAAI24 (https://aaai.org/aaai-conference/

ESimCSE Unsupervised Contrastive Learning Jointly with UDA Semi-Supervised Learning for Large Label System Text Classification Mode

The challenges faced by text classification with large tag systems in natural language processing tasks include multiple tag systems, uneven data distribution, and high noise. To address these problems, the ESimCSE unsupervised comparative learning and UDA semi-supervised comparative learning models are combined through the use of joint training techniques in the models.The ESimCSE model efficiently learns text vector representations using unlabeled data to achieve better classification results, while UDA is trained using unlabeled data through semi-supervised learning methods to improve the prediction performance of the models and stability, and further improve the generalization ability of the model. In addition, adversarial training techniques FGM and PGD are used in the model training process to improve the robustness and reliability of the model. The experimental results show that there is an 8% and 10% accuracy improvement relative to Baseline on the public dataset Ruesters as well as on the operational dataset, respectively, and a 15% improvement in manual validation accuracy can be achieved on the operational dataset, indicating that the method is effective.Comment: This paper contains 14 pages,4 figures,4 table

A prospective approach for enhancing the performance of carbon-mixed concrete via retarder incorporation

The concrete that absorbs carbon dioxide during the mixing process, termed as carbon-mixed concrete, has become a hot research topic under the background of dual carbon goals. However, the workability of concrete significantly decreases even with the absorption of a minimal amount of carbon dioxide, indicating a potential challenge in its practical application. Therefore, this study investigated the effects of incorporating 0.5%, 1.0%, and 2.0% carbon dioxide (relative to the mass of cementitious materials) and retarder on the mechanical properties and microstructure of concrete through mercury intrusion, X-ray diffraction, and scanning electron microscopy, revealing the mechanism of retarder improving carbon-mixed concrete. The results indicated that the flowability and 28-day compressive strength of concrete mixed with 0.5% carbon dioxide decreased by 68.75% and 10.77%, respectively. However, after adding 0.25% retarder, these values for the carbon-mixed concrete only decreased by 18.75% and 3.52%. Meanwhile, the introduction of carbon dioxide can form carbonates and carboaluminates and refine the internal pores of the concrete matrix. This study proposes an effective method to improve the performance of carbon-mixed concrete, which can promote the efficient absorption of carbon dioxide in the concrete industry

Low expression of Notch1 may be associated with acute myocardial infarction

BackgroundThe transmembrane protein Notch1 is associated with cell growth, development, differentiation, proliferation, apoptosis, adhesion, and the epithelial mesenchymal transition. Proteomics, as a research method, uses a series of sequencing techniques to study the composition, expression levels, and modifications of proteins. Here, the association between Notch1 and acute myocardial infarction (AMI) was investigated using proteomics, to assess the possibility of using Notch1 as a biomarker for the disease.MethodsFifty-five eligible patients with AMI and 74 with chronic coronary syndrome (CCS) were enrolled, representing the experimental and control groups, respectively. The mRNA levels were assessed using RT-qPCR and proteins were measured using ELISA, and the results were compared and analyzed.ResultsNotch1 mRNA levels were 0.52 times higher in the peripheral blood mononuclear cells of the AMI group relative to the CCS group (p < 0.05) while Notch1 protein levels were 0.63 times higher in peripheral blood plasma in AMI patients (p < 0.05). Notch1 levels were not associated with older age, hypertension, smoking, high abdominal-blood glucose, high total cholesterol, and high LDL in AMI. Logistic regression indicated associations between AMI and reduced Notch1 expression, hypertension, smoking, and high fasting glucose.ConclusionsNotch1 expression was reduced in the peripheral blood of patients with AMI relative to those with CCS. The low expression of Notch1 was found to be an independent risk factor for AMI and may thus be an indicator of the disease

6,6′-Oxydichroman

The title compound, C18H18O3, was synthesized from dichroman in concentrated sulfuric acid. The mol­ecule has a twofold axis passing through the central O atom. The dihedral angle between the two symmetry-related benzene rings is 63.6 (3)°. Weak C—H⋯π inter­actions are present in the structure