Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records

BMC Med Inform Decis Mak

Yang

et al. 2020

Background Electronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disease (CVD), and are able to use them to predict CVD, we have the ability to automatically process clinical texts, resulting in an improved accuracy of supporting doctors for the clinical diagnosis of CVD. In the case where CVD is becoming more worldwide, predictive CVD based on EMRs has been studied by many researchers to address this important aspect of improving diagnostic efficiency. Methods This paper proposes an Enhanced Character-level Deep Convolutional Neural Networks (EnDCNN) model for cardiovascular disease prediction. Results On the manually annotated Chinese EMRs corpus, our risk factor identification extraction model achieved 0.9073 of F-score, our prediction model achieved 0.9516 of F-score, and the prediction result is better than the most previous methods. Conclusions The character-level model based on text region embedding can well map risk factors and their labels as a unit into a vector, and downsampling plays a crucial role in improving the training efficiency of deep CNN. What’s more, the shortcut connections with pre-activation used in our model architecture implements dimension-matching free in training.

Section: Resultsmentioning

confidence: 99%

Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction

Zhang

BMC Med Inform Decis Mak

Yang

et al. 2020

“…In Figure 6, we have a visual example, which consists of the following three parts: (a) A sample of the case characteristics in EMR is that the risk factors have been marked according to the labeling rules [18]. (b) We translated the case characteristics into English.…”

Section: Resultsmentioning

confidence: 99%

“…Then we will use the EMRs that need to be used for prediction to identify the risk factors through the model. This corpus intends to be used to develop a risk factor information extraction system that, in turn, can be applied as a foundation for the further study of the progress of risk factors and CVD [18]. For the corpus, we divided it into CVD and no CVD according to the clinically diagnosed disease in the electronic medical record.…”

Section: Dataset and Evaluation Metricsmentioning

confidence: 99%

Deep Cardiovascular Disease Prediction with Risk Factors Powered Bi-attention

Neural Information Processing

Zhang

Qin

et al. 2020

Background Cardiovascular disease (CVD), as a chronic disease, has been perplexing human beings and is one of the serious diseases endangering life and health. Therefore, using the electronic medical record information of patients to automatically predict CVD has important application value in intelligent auxiliary diagnosis and treatment, and is a hot issue in intelligent medical research. In recent years, attention mechanism has been successfully extended to various tasks of natural language processing. Typically, these methods use attention to focus on a small part of the context and summarize it with a fixed-size vector, coupling attention in time, and/or often forming a uni-directional attention. Methods In this paper, we propose a CVD risk factors powered bi-directional attention (RFPBiA) network, which is a multi-stage hierarchical process, representing information fusion at different granularity levels, and uses the bi-directional attention to obtain the text representation of risk factors without early aggregation. Results The experimental results show that the proposed method can obviously improve the performance of CVD prediction, and the F-score reaches 0.9424, which is better than the existing related methods. Conclusions We propose to extract the risk factors leading to CVD by using the existing mature entity recognition technology, which provides a new idea for disease prediction tasks. Moreover, the memory-less attention mechanism in both directions in our proposed prediction model of RFPBiA can fuse the character sequence and the risk factors contained in the electronic medical record text to predict CVD.

“…Experimental results show that the F-score reaches 0.9586, which fully demonstrates the effectiveness of our proposed method and network architecture. In summary, our contribution is four-fold, leading to the following conclusions: We no longer simply utilize the entire EMR as in previous related works, but use the 12 risk factors proposed by Su et al [ 8 ] instead. This can well avoid the interference of a large amount of redundant information in EMRs on CVD prediction.…”

Section: Introductionmentioning

confidence: 99%

“…We no longer simply utilize the entire EMR as in previous related works, but use the 12 risk factors proposed by Su et al [ 8 ] instead. This can well avoid the interference of a large amount of redundant information in EMRs on CVD prediction.…”

Section: Introductionmentioning

confidence: 99%

A risk factor attention-based model for cardiovascular disease prediction

Wang

et al. 2022

BMC Bioinformatics

Background Cardiovascular disease (CVD) is a serious disease that endangers human health and is one of the main causes of death. Therefore, using the patient’s electronic medical record (EMR) to predict CVD automatically has important application value in intelligent assisted diagnosis and treatment, and is a hot issue in intelligent medical research. However, existing methods based on natural language processing can only predict CVD according to the whole or part of the context information of EMR. Results Given the deficiencies of the existing research on CVD prediction based on EMRs, this paper proposes a risk factor attention-based model (RFAB) to predict CVD by utilizing CVD risk factors and general EMRs text, which adopts the attention mechanism of a deep neural network to fuse the character sequence and CVD risk factors contained in EMRs text. The experimental results show that the proposed method can significantly improve the prediction performance of CVD, and the F-score reaches 0.9586, which outperforms the existing related methods. Conclusions RFAB focuses on the key information in EMR that leads to CVD, that is, 12 risk factors. In the stage of risk factor identification and extraction, risk factors are labeled with category information and time attribute information by BiLSTM-CRF model. In the stage of CVD prediction, the information contained in risk factors and their labels is fused with the information of character sequence in EMR to predict CVD. RFAB makes well use of the fine-grained information contained in EMR, and also provides a reliable idea for predicting CVD.