A New Modeling Method Base on Candidate Window for Clinical Concept Extraction

Background Recently, how to structuralize electronic medical records (EMRs) has attracted considerable attention from researchers. Extracting clinical concepts from EMRs is a critical part of EMR structuralization. The performance of clinical concept extraction will directly affect the performance of the downstream tasks related to EMR structuralization. We propose a new modeling method based on candidate window classification, which is different from mainstream sequence labeling models, to improves the performance of clinical concept extraction tasks under strict standards by considering the overall semantics of the token sequence instead of the semantics of each token. We call this model as slide window model. Method In this paper, we comprehensively study the performance of the slide window model in clinical concept extraction tasks. We model the clinical concept extraction task as the task of classifying each candidate window, which was extracted by the slide window. The proposed model mainly consists of four parts. First, the pre-trained language model is used to generate the context-sensitive token representation. Second, a convolutional neural network (CNN) is used to generate all representation vector of the candidate windows in the sentence. Third, every candidate window is classified by a Softmax classifier to obtain concept type probability distribution. Finally, the knapsack algorithm is used as a post-process to maximize the sum of disjoint clinical concepts scores and filter the clinical concepts. Results Experiments show that the slide window model achieves the best micro-average F1 score(81.22%) on the corpora of the 2012 i2b2 NLP challenges and achieves 89.25% F1 score on the 2010 i2b2 NLP challenges under the strict standard. Furthermore, the performance of our approach is always better than the BiLSTM-CRF model and softmax classifier with the same pre-trained language model.Conclusions The slide window model shows a new modeling method for solving clinical concept extraction tasks. It models clinical concept extraction as a problem for classifying candidate windows and extracts clinical concepts by considering the semantics of the entire candidate window. Experiments show that this method of considering the overall semantics of the candidate window can improve the performance of clinical concept extraction tasks under strict standards.

A New Modeling Method Base on Candidate Window for Clinical Concept Extraction

Background Recently, how to structuralize electronic medical records (EMRs) has attracted considerable attention from researchers. Extracting clinical concepts from EMRs is a critical part of EMR structuralization. The performance of clinical concept extraction will directly affect the performance of the downstream tasks related to EMR structuralization. We propose a new modeling method based on candidate window classification, which is different from mainstream sequence labeling models, to improves the performance of clinical concept extraction tasks under strict standards by considering the overall semantics of the token sequence instead of the semantics of each token. We call this model as slide window model. MethodIn this paper, we comprehensively study the performance of the slide window model in clinical concept extraction tasks. We model the clinical concept extraction task as the task of classifying each candidate window, which was extracted by the slide window. The proposed model mainly consists of four parts. First, the pre-trained language model is used to generate the context-sensitive token representation. Second, a convolutional neural network (CNN) is used to generate all representation vector of the candidate windows in the sentence. Third, every candidate window is classified by a Softmax classifier to obtain concept type probability distribution. Finally, the knapsack algorithm is used as a post-process to maximize the sum of disjoint clinical concepts scores and filter the clinical concepts. Results Experiments show that the slide window model achieves the best micro-average F1 score(81.22%) on the corpora of the 2012 i2b2 NLP challenges and achieves 89.25% F1 score on the 2010 i2b2 NLP challenges under the strict standard. Furthermore, the performance of our approach is always better than the BiLSTM-CRF model and softmax classifier with the same pre-trained language model. ConclusionsThe slide window model shows a new modeling method for solving clinical concept extraction tasks. It models clinical concept extraction as a problem for classifying candidate windows and extracts clinical concepts by considering the semantics of the entire candidate window. Experiments show that this method of considering the overall semantics of the candidate window can improve the performance of clinical concept extraction tasks under strict standards.

Automated Recognition of a Wall between Windows from a Single Image

To avoid the time-consuming, costly, and expert-dependent traditional assessment of earthquake damaged structures, image-based automatic methods have been developed recently. Since automated recognition of structure elements is the basis by which these methods achieve automatic detection, this study proposes a method to recognize the wall between windows from a single image automatically. It begins from detection of line segments with further selection and linking to obtain longer line segments. The color features of the two sides of each long line segment are employed to pick out line segments as candidate window edges and then label them. Finally, the images are segmented into several subimages, window regions are located, and then the wall between the windows is located. Real images are tested to verify the method. The results indicate that walls between windows can be successfully recognized.