Research On Clinical Trial Criteria Classification Based On Electronic Medical Records

In recent years,the technology of deep learning and natural language processing has been developing rapidly,and the connection between medical field and computer field is getting closer and closer.Computer is not only used for electronics,computing and communication,but also for auxiliary clinical research,and its powerful computing and processing ability can help produce medical decisions.The clinical trial cohort selection task in this thesis is the application of natural language processing in medical research.With the increasing accuracy of computer decision making in clinical research,the application of natural language processing technology in medical field has attracted more researchers'attention.This thesis introduces a cohort selection of clinical trials task and related data set,which aims to answer the question:can NLP models use narrative medical records to identify which patients meet selection criteria for clinical trials?Instead of using the traditional methods of grammar,semantic analysis and rule construction,this thesis adopted the method of deep learning to build the NLP classification model based on the word embedding and sentence embedding respecti'vely.In the method based on word embedding,this thesis use pre-training word embedding models and the word embedding models which is obtained by word embedding expansion methods to construct word vector for clinical record text and use word vector representing the text information.Then,a series of clinical trial criteria classification models were constructed using deep learning models such as Convolutional Neural Network,Bidirectional Long-Short Time Memory model,Attention Mechanism and RNNCNN model.And based on these models,a two-channel classification model based on word embedding was designed;In the method based on sentence embedding,this thesis used the pre-training InferSent model to encode text sentences into sentence vectors.Then the model which was proposed in this thesis was improved to obtain a two-channel classification model based on sentence vectors.Finally,the classification performance of our model is verified by different comparison experiments.The two-channel model based on the word vector realized micro-fl of 0.7810 on the test set.After the word vector expansion method was used,the micro-fl value was increased to 0.7905.The two-channel model based on sentence vector achieved 0.7961 micro-fl on the test set,achieving the best effect of the model,and proving the effectiveness of the improved model on clinical trial criteria classification task.