Adaptive Information Assimilation using Convolutional Neural Network for Forecast of Breast Cancer from Electronic Health Records

Purpose Data acquired from cancer based Electronic Health Records (EHRs) shows key statistics on cancer affected persons. To estimate the impact of the cancer on those persons, we need to extract vital information from those pathology health records. It is an exhaustive procedure to carry out because of large volume of records and data acquired for a continuous period of time.Methods This research portrays, the investigation of convolutional neural network (CNN) and Support Vector Machine (SVM) techniques for extracting topographic codes from the pathology reports of breast cancer. Investigations are carried out using conventional frequency vector space method and the deep learning techniques such as CNN. The learning experience of those algorithms were absorbed on a set of 730 pathology reports.Results We perceived that the CNN technique reliably outperformed the conventional frequency vector methods. It is also observed that it causes the micro and macro average performance to increase up to 0.119, and 0.101, while considering the populated class labels for the CNN model. Unambiguously, the top performing CNN approach attained a micro-F score of 0.821 over the considered topography codes.Conclusion These promising outcomes reveals the prospective of deep learning approaches, particularly CNN for estimating the impact of the cancer from the pathology reports compared to conventional SVM approach. More advanced and accurate approaches to effectively improve the accuracy in information extraction are needed.