Herbal prescriptions are a cornerstone of Traditional Chinese Medicine (TCM), bridging diagnostic reasoning and therapeutic interventions. Existing herbal prescription recommendation (HPR) methods often rely on structured inputs or multi-stage pipelines, limiting their applicability to real-world clinical scenarios. To address this, we propose MKE-DAN (Medical Knowledge Enhanced Dual-level Alignment Network), a novel framework that directly generates herbal prescriptions from raw, unstructured medical records. By integrating a domain-specific pre-trained language model and label-wise self-attention, our model leverages intra-modal alignment to capture subtle diagnostic patterns and treatment reasoning from medical records. Additionally, cross-modal alignment with a heterogeneous graph transformer and knowledge-enhanced cross-attention ensures effective integration of structured knowledge with medical records for accurate and interpretable herb recommendations. Using TCM-9K, a large-scale dataset of clinical records paired with expert-verified prescriptions, and the CCL2025-800 dataset for external validation, experimental results demonstrate that MKEDAN achieves state-of-the-art performance, robust generalization, and strong handling of rare prescription patterns, showcasing its potential for real-world deployment in intelligent TCM systems.

Automated International Classification of Diseases (ICD) coding assigns standardized codes to clinical records, playing a critical role in healthcare systems. However, existing methods struggle with semantic gaps between clinical text and ICD codes, poor performance on rare codes, and limited interpretability. We propose TraceCoder, a framework that integrates multi-source external knowledge, including UMLS, Wikipedia, and large language models (LLMs), to enrich ICD code representations and provide traceable, evidence-based predictions. A hybrid attention mechanism is introduced to model interactions among labels, clinical context, and knowledge, improving longtail code recognition and interpretability by grounding predictions in external evidence. Experiments on MIMIC-III-ICD9, MIMIC-IV-ICD9, and MIMIC-IV-ICD10 datasets demonstrate that TraceCoder achieves state-of-the-art performance, with ablation studies validating its components. TraceCoder offers a scalable, interpretable, and reliable solution for automated ICD coding, aligning with clinical needs for accuracy and traceability.

Electronic medical records (EMRs) are crucial for modern healthcare, containing rich information about patient care, diagnoses, and treatments. However, their unstructured nature, domain-specific language, and complexity pose significant challenges for automated understanding. Existing methods often treat all data equally, limiting their ability to handle rare or complex cases effectively. We present TACL (Threshold-Adaptive Curriculum Learning), a novel framework that dynamically adjusts the training process based on sample complexity. Inspired by progressive learning, TACL categorizes data into difficulty levels, focusing on simpler cases early in training and gradually addressing more complex ones. A domain-specific pre-trained language model is used for difficulty assessment, considering semantic, syntactic, and contextual features. Additionally, TACL employs an adaptive training strategy to enhance task-specific performance and ensure generalization across diverse datasets. Experimental results on multilingual datasets, including MIMICIII, MIMIC-IV, and Chinese clinical records, demonstrate TACL's effectiveness in tasks such as ICD coding, readmission prediction, and TCM syndrome differentiation. TACL improves performance on rare and complex cases, providing a scalable and robust solution for medical text understanding.

Intraoperative hypotension (IOH), a critical condition characterized by sustained arterial pressure below 65 mmHg, poses significant risks of organ damage and mortality. Despite advancements in predictive models, current methods often fall short in capturing the dynamic, multi-scale nature of physiological signals due to their reliance on fixed convolutional kernels. To address this challenge, we introduce the Adaptive Time-Shift Convolutional Neural Network (ATS-CNN), a novel approach that combines the flexibility of deformable convolutions with the temporal modeling power of LSTMs. Unlike traditional methods, ATS-CNN leverages an innovative LSTM-based offset generation mechanism to dynamically adjust convolutional sampling points, enabling precise adaptation to the non-stationary characteristics of biosignals. Through a combination of adaptive sampling, deformable 1D convolutions, and robust data augmentation techniques-including dynamic noise injection-the model achieves unparalleled robustness and predictive accuracy. Extensive internal and external validations on two large-scale real-world datasets demonstrate that ATSCNN not only sets a new benchmark in$\mathbf{I O H}$prediction but also offers a clinically interpretable framework, transforming how machine learning addresses high-stakes medical challenges. This fusion of adaptability, precision, and interpretability positions ATS-CNN as a disruptive innovation in the field of perioperative care.

Intraoperative hypotension (IOH) is strongly associated with postoperative complications, including postoperative delirium and increased mortality, making its early prediction crucial in perioperative care. While several artificial intelligence-based models have been developed to provide IOH warnings, existing methods face limitations in incorporating both time and frequency domain information, capturing short- and long-term dependencies, and handling noise sensitivity in biosignal data. To address these challenges, we propose a novel Self-Adaptive Frequency Domain Network (SAFDNet). Specifically, SAFDNet integrates an adaptive spectral block, which leverages Fourier analysis to extract frequency-domain features and employs self-adaptive thresholding to mitigate noise. Additionally, an interactive attention block is introduced to capture both long-term and short-term dependencies in the data. Extensive internal and external validations on two large-scale real-world datasets demonstrate that SAFDNet achieves up to 97.3% AUROC in IOH early warning, outperforming state-of-the-art models. Furthermore, SAFDNet exhibits robust predictive performance and low sensitivity to noise, making it well-suited for practical clinical applications.

Timely prediction and intervention for Intraoperative Hypotension (IOH), a prevalent complication associated with general anesthesia, is crucial to prevent severe postoperative outcomes. While existing machine learning methods for IOH prediction have shown promise, they face limitations such as reliance on single data sources and disregard for crucial features like the trend in Arterial Blood Pressure (ABP) waveforms. To address these challenges, this paper proposes a novel multichannel deep learning framework that combines Convolutional Neural Networks (CNN) and Transformers for automatic IOH prediction. The model leverages four types of physiological waveforms, including ABP, ElectroCardioGram (ECG), photoplethysmography (PLE), and Carbon Dioxide (CO2), to capture both local and global information, enhancing information representation. Experimental results on retrospective data of 14,140 adult patients undergoing non-cardiac surgery from VitalDB, a public data repository of vital signs taken during surgeries in 10 operating rooms at Seoul National University Hospital (from January 6, 2005 to March 1, 2014), demonstrate that the proposed model consistently outperforms other methods, achieving superior AUROC values of 0.943, 0.928, and 0.923 at 5, 10, and 15 minutes before the event, respectively. These results demonstrate the powerfulness of multi-modal data source as well as the importance of ABP waveform trends. Additionally, the incorporation of multi-task learning and the attention mechanism validates the effectiveness and superiority of our model, highlighting its potential for proactive clinical interventions and improved postoperative patient outcomes.

OBJECTIVE: Acute kidney injury (AKI) is a common complication after pediatric cardiac surgery, and the early detection of AKI may allow for timely preventive or therapeutic measures. However, current AKI prediction researches pay less attention to time information among time-series clinical data and model building strategies that meet complex clinical application scenario. This study aims to develop and validate a model for predicting postoperative AKI that operates sequentially over individual time-series clinical data. MATERIALS AND METHODS: A retrospective cohort of 3386 pediatric patients extracted from PIC database was used for training, calibrating, and testing purposes. A time-aware deep learning model was developed and evaluated from 3 clinical perspectives that use different data collection windows and prediction windows to answer different AKI prediction questions encountered in clinical practice. We compared our model with existing state-of-the-art models from 3 clinical perspectives using the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision-recall curve (PR AUC). RESULTS: Our proposed model significantly outperformed the existing state-of-the-art models with an improved average performance for any AKI prediction from the 3 evaluation perspectives. This model predicted 91% of all AKI episodes using data collected at 24 h after surgery, resulting in a ROC AUC of 0.908 and a PR AUC of 0.898. On average, our model predicted 83% of all AKI episodes that occurred within the different time windows in the 3 evaluation perspectives. The calibration performance of the proposed model was substantially higher than the existing state-of-the-art models. CONCLUSIONS: This study showed that a deep learning model can accurately predict postoperative AKI using perioperative time-series data. It has the potential to be integrated into real-time clinical decision support systems to support postoperative care planning.

The genetic, proteomic, disease and pharmacological studies have generated rich data in protein interaction, disease regulation and drug activities useful for systems-level study of the biological, disease and drug therapeutic processes. These studies are facilitated by the established and the emerging computational methods. More recently, the network descriptors developed in other disciplines have become more increasingly used for studying the protein-protein, gene regulation, metabolic, disease networks. There is an inadequate coverage of these useful network features in the public web servers. We therefore introduced upto 313 literature-reported network descriptors in PROFEAT web server, for describing the topological, connectivity and complexity characteristics of undirected unweighted (uniform binding constants and molecular levels), undirected edge-weighted (varying binding constants), undirected node-weighted (varying molecular levels), undirected edge-node-weighted (varying binding constants and molecular levels) and directed unweighted (oriented process) networks. The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein-protein, gene regulatory, gene co-expression, protein-drug and metabolic networks. PROFEAT is accessible free of charge at http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi.

MOTIVATION: Genetic and gene expression variations within and between populations and across geographical regions have substantial effects on the biological phenotypes, diseases, and therapeutic response. The development of precision medicines can be facilitated by the OMICS studies of the patients of specific ethnicity and geographic region. However, there is an inadequate facility for broadly and conveniently accessing the ethnic and regional specific OMICS data. RESULTS: Here, we introduced a new free database, HEROD, a human ethnic and regional specific OMICS database. Its first version contains the gene expression data of 53 070 patients of 169 diseases in seven ethnic populations from 193 cities/regions in 49 nations curated from the Gene Expression Omnibus (GEO), the ArrayExpress Archive of Functional Genomics Data (ArrayExpress), the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Geographic region information of curated patients was mainly manually extracted from referenced publications of each original study. These data can be accessed and downloaded via keyword search, World map search, and menu-bar search of disease name, the international classification of disease code, geographical region, location of sample collection, ethnic population, gender, age, sample source organ, patient type (patient or healthy), sample type (disease or normal tissue) and assay type on the web interface. AVAILABILITY AND IMPLEMENTATION: The HEROD database is freely accessible at http://bidd2.nus.edu.sg/herod/index.php. The database and web interface are implemented in MySQL, PHP and HTML with all major browsers supported. CONTACT: [email protected].

This article proposes an auction mechanism based scheme for joint radio resource management (JRRM) in a reconfigurable system in a multi-operator scenario. Through the periodical auction and transaction for the radio resource between different radio access technologies (RATs) of multiple operators, the spare radio resource can be fully utilized to meet the demand of the RATs being short of radio resource. In order to rationally handle the profit assignment between multiple operators, a specific pricing strategy is put forward. In addition, a novel architecture is also presented to support this JRRM scheme. Simulation results reveal that the proposed scheme not only effectively reduces the total session blocking probability, but also greatly improves the total radio resource utilization ratio and the profits of operators.