The clinical informatization of traditional Chinese medicine (TCM) focuses on serving users and assisting in diagnosis. The rules of clinical knowledge play an important role in improving the TCM informatization service. However, many rules are difficult to find because of the complexity of the data in the current TCM syndrome prediction. Therefore, we proposed an end-to-end model, called Decision-making System for the Diagnosis of Syndrome (DSDS), which is based on the knowledge graph (KG) of TCM. This paper introduces the link prediction for the diagnosis of syndrome by dismantling medical records into multiple symptoms. In addition, based on the symptoms and predicted syndromes, the most relevant syndrome could be determined by the scoring and voting method in this paper. The results show that the accuracy of DSDS is 80.6.

Liver cancer is a malignant tumor with high incidence and mortality rates. Computed tomography is a key imaging modality for clinical diagnosis but faces challenges such as subtle intra-class variations and the inefficiency of manual interpretation. Traditional diagnostic approaches often lack generalizability and effective feature extraction, increasing the risk of misdiagnosis. While deep learning models like ResUNet have improved diagnostic performance, issues such as information loss and inaccurate tumor boundary segmentation persist. To address these challenges, this study proposes NMD-FusionNet, a deep learning-based framework for liver cancer image segmentation and diagnosis. The framework includes a three-stage pipeline: first, a refined non-local means filtering algorithm is employed for pre-screening, discarding over 80% of non-diagnostic images using adaptive thresholding; second, a multimodal image fusion method integrates multi-phase, multi-source liver cancer image data through multi-scale decomposition and precise fusion rules to reduce noise and motion artifacts; third, a dual-path DconnNet segmentation network is constructed, incorporating a directional excitation module in the encoder and a spatial awareness unit in the decoder, guided by a boundary-constrained loss function to enhance segmentation accuracy. Evaluated on over 2,000 liver cancer images from the Second People's Hospital of Huaihua, NMD-FusionNet achieves a tumor segmentation Intersection over Union (IoU) of 83.9%, representing an 8.7% improvement over ResUNet and demonstrating superior sensitivity. This work provides a reliable computer-aided diagnostic tool for radiologists and shows strong potential for clinical application, particularly in resource-limited healthcare settings.

The electronic medical records (EMRs) of traditional Chinese medicine (TCM) include a wealth of TCM knowledge and syndrome diagnosis information, which is crucial for improving the quality of TCM auxiliary decision‐making. In practical diagnosis, one disease corresponds to one syndrome, posing considerable hurdles for the informatization of TCM. The purpose of this work was to create an end‐to‐end TCM diagnostic model, and the knowledge graph (KG) created in this article is used to improve the model’s information and realize auxiliary decision‐making for TCM disorders. We approached auxiliary decision‐making for syndrome differentiation in this article as a multilabel classification task and presented a knowledge‐based decision support model for syndrome differentiation (KDSD). Specifically, we created a KG based on TCM features (TCMKG), supplementing the textual representation of medical data with embedded information. Finally, we proposed fusing medical text with KG entity representation (F‐MT‐KER) to get prediction results using a linear output layer. After obtaining the vector representation of the medical record text using the BERT model, the vector representation of various KG embedded models can provide additional hidden information to a certain extent. Experimental results show that our method improves by 1% (P@1) on the syndrome differentiation auxiliary decision task compared to the baseline model BERT. The usage of EMRs can aid TCM development more efficiently. With the help of entity level representation, character level representation, and model fusion, the multilabel classification method based on the pretraining model and KG can better simulate the TCM syndrome differentiation of the complex cases.