Chinese medical named entity recognition aims to identify named entities from unstructured Chinese medical texts, which has received increasing attention from both academia and industry. The existing efforts could be divided into three categories: rule-based, machine learning-based, and deep learning-based approaches. However, there are still two limitations in Chinese medical named entity recognition: insufficient information fusion and ambiguous word segmentation, leading to incomprehensive representations and suboptimal performance. To tackle these problems, we propose a novel model—Semantic Enhanced relational graph attention Network for Chinese medical named Entity recognition (SENCE), which considers morphology-level, character-level, and word-level semantics simultaneously. Specifically, we build semantic graphs to encode semantic information on different levels and develop semantic enhanced relational graph attention network to capture high-order information and enhance relation-aware representations. Finally, we feed the enhanced representations into bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) for named entity label prediction. We conducted extensive experiments on two benchmark datasets. The results illustrate that the proposed model outperforms state-of-the-art methods, demonstrating the effectiveness of the proposed model. Further ablation studies validate the rationality of the components.

Delirium is an acute and reversible neuropsychiatric syndrome, with its diagnosis relying on clinicians’ dynamic assessments of patients. This process requires the integration of multidimensional clinical information, for which electronic medical records (EMRs) serve as a crucial source. However, Chinese EMRs are often lengthy, unevenly structured, and exhibit considerable variability in symptom descriptions, leading to key diagnostic information being obscured by redundant narratives. These characteristics pose significant challenges for traditional methods to consistently extract relevant diagnostic features. This paper proposes a deep learning framework, guided by medical knowledge, to improve the classification performance and interpretability of delirium in Chinese long-form EMRs. The framework employs a region-guided chunking mechanism to segment records into semantically coherent units and extract key features. It combines differentiated data augmentation and contrastive learning methods to strengthen semantic discriminability, and utilizes adaptive feature fusion to achieve cross-chunk collaborative decision-making. Experimental results demonstrate that this method effectively enhances the accuracy and reliability of delirium diagnosis in Chinese medical texts, showing strong clinical applicability.

Neurological disorders are the leading cause of global disease burden. Early diagnosis and treatment are crucial for enhancing patients’ quality of life and improving their prognosis. With the rapid development of artificial intelligence, especially in speech processing technology, the diagnosis of neurological diseases has made revolutionary breakthroughs. Speech processing technology can detect subtle pronunciation variations in patients’ pronunciation, enabling doctors to identify neurological disorders more objectively and accurately. In this paper, we focus on two specific disorders (Parkinson’s disease, Alzheimer’s disease), conducting an in-depth analysis and comprehensive summary of the existing research on speech-based diagnosis in neurological diseases.This article starts by introducing commonly used public speech datasets and AI processing frameworks. Then, from the perspective of model construction, this article elucidates advanced endeavors in the diagnosis of Parkinson’s disease and Alzheimer’s disease. The related works are divided into three categories: machine learning, deep learning, and large models. Furthermore, this article elaborates on the existing problems in current research and the future development trends, aiming to offer novel insights into the in-depth application of speech processing technology in neurological diseases.

Accurate prediction of intraoperative hazardous events and generation of effective intervention plans are critical to surgical safety, but face multiple challenges of real-time, accuracy, and interpretability. Large-scale language models have potential, but their high cost and potential ‘illusion’ problems limit their application in real-time clinical environments. Traditional multitask learning models are efficient but knowledge-constrained, making it difficult to capture complex reasoning processes. To bridge this gap, this paper proposes a multi-objective distillation knowledge enhancement model-KEMO, which innovatively adopts a multi-objective chain-of-thought distillation framework to not only mimic the prediction results of the instructor’s LLM, but also explicitly migrate its structured reasoning process to the lightweight student model, which improves the answerability of the model by synergistically optimising the three objectives of event prediction, reasoning alignment and scenario generation. Interpretability. Meanwhile, combined with the Knowledge Graph-based Retrieval Augmented Generation mechanism, validated medical knowledge is dynamically injected to enhance the accuracy and reliability of decision-making and reduce model illusion. The experimental results show that the KEMO model significantly outperforms traditional models of the same magnitude in intraoperative hazardous event prediction and prognostic proposal generation, and achieves a performance comparable to that of a large faculty model.The KEMO model effectively bridges the gap between the large language model and the actual clinical application, and facilitates the transformation of the large model knowledge to the actual clinical deployment.