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Percutaneous coronary intervention (PCI) has become a vital treatment approach for coronary artery disease, but the clinical data of PCI cannot be directly utilized due to its unstructured characteristics. The existing clinical named entity recognition (CNER) has been used to identify specific entities such as body parts, drugs, and diseases, but its specific potential in PCI clinical texts remains largely unexplored. How to effectively use CNER to deeply mine the information in the existing PCI clinical records is worth studying. In this paper, a total of 24 267 corpora are collected from the Cardiovascular Disease Treatment Center of the People's Hospital of Liaoning Province in China. We select three types of clinical record texts of fine-grained PCI surgical information, from which 5.8% of representative surgical records of PCI patients are selected as datasets for labeling. To fully utilize global information and multi-level semantic features, we design a novel character-level vector embedding method and further propose a new hybrid model based on it. Based on the classic Bidirectional Long Short-Term Memory Network (BiLSTM), the model further integrates Convolutional Neural Networks (CNNs) and Bidirectional Encoder Representations from Transformers (BERTs) for feature extraction and representation, and finally uses Conditional Random Field (CRF) for decoding and predicting label sequences. This hybrid model is referred to as BCC-BiLSTM in this paper. In order to verify the performance of the proposed hybrid model for extracting PCI surgical information, we simultaneously compare both representative traditional and intelligent methods. Under the same circumstances, compared with other intelligent methods, the BCC-BiLSTM proposed in this paper reduces the word vector dimension by 15%, and the F1 score reaches 86.2% in named entity recognition of PCI clinical texts, which is 26.4% higher than that of HMM. The improvement is 1.2% higher than BiLSTM + CRF and 0.7% higher than the most popular BERT + BiLSTM + CRF. Compared with the representative models, the hybrid model has better performance and can achieve optimal results faster in the model training process, so it has good clinical application prospects. [ABSTRACT FROM AUTHOR]

Machine learning provides increasingly reliable assistance for medical experts in diagnosing coronary heart disease. This study proposes a deep learning hybrid model based coronary heart disease (CAD) prediction method, which can significantly improve the prediction accuracy compared to traditional solutions. This research scheme is based on the data of 7291 patients and proposes a hybrid model, which uses two different deep neural network models and a recurrent neural network model as the main model for training. The prediction results based on the main model training use a k-nearest neighbor model for secondary training so as to improve the accuracy of coronary heart disease prediction. The comparison between the model prediction results and the clinical diagnostic results shows that the prediction model has a prediction accuracy rate of 82.8%, a prediction precision rate of 87.08%, a prediction recall rate of 88.57%, a prediction F1-score of 87.82%, and an area under the curve value of 0.8 in the test set. Compared to single model machine learning predictions, the hybrid model has a significantly improved accuracy and has effectively solved the problem of overfitting. A deep learning based CAD prediction hybrid model that combines multiple weak models into a strong model can fully explore the complex inter-relationships between various features under limited feature values and sample size, improve the evaluation indicators of the prediction model, and provide effective auxiliary support for CAD diagnosis. [ABSTRACT FROM AUTHOR]