Obtaining medical pathways from a large number of medical logs has become a current research hotspot. In this article, we proposed a method that combines trace clustering, process discovery and neural network to discover medical pathway models from complex medical logs. The source medical logs were structured as XES event logs first. Cases with similar medical behavior were aggregated by trace clustering. Use process mining to generate process models. Extract reasonable medical pathways from the process models. Neural network was used to determine the proportional characteristics of medical pathways. Combine the above to form a usable medical pathway model. The results of the experiments show that the average simplicity of the generated process model is 0.695, the average accuracy of the neural network models is 93.44%, and the medical pathway model score is about 0.879.
Depression is a kind of mood disorder disease characterized by significant and lasting depression, which seriously affects people's physical and mental health. In recent years, the number of people suffering from depression has gradually increased. In order to improve the recognition rate of depression and reduce the workload of doctors, this paper proposes to apply the deep learning algorithm BiLSTM (Bi-directional Long Short-Term Memory) and Attention to recognize depression. Among them, BiLSTM is used to extract contextual temporal information of text features and facial features. Attention is used to learn the correlation between vision and text modalities. This paper undertakes extensive experiments to demonstrate the network's effect. The experimental results show that this method has certain practical application value for depression recognition.
Deep neural networks are frequently used to automate the examination of radiographic images in medical. These approaches may be used to train on huge datasets or extract features from small datasets using pre-trained networks. Due to the lack of large pulmonary tuberculosis datasets, it is possible to diagnose tuberculosis using pre-trained deep convolutional neural networks. Thus, this article aims to detect and diagnose tuberculosis in chest X-rays by combining a pre-trained deep convolutional neural network with a machine learning model. Combined the deep pre-trained DenseNet201 network with the machine learning XGBoost classifier to create a hybrid model for classifying patients as tuberculosis infected or not. The proposed model extracts feature using the pre-trained DenseNet201 neural networks and classify them employing the XGBoost classifier. We performed extensive experiments to assess the performance of the proposed DenseNet201-XGBoost model using tuberculosis chest x-ray images. Comparative study shows that the proposed DenseNet201-XGBoost-based tuberculosis classification model outperforms other competing approaches.
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