MIEdeep - Medical Information Extraction for German Medical Texts using Deep Learning Methods

点击翻译按钮获取中文摘要

A steadily growing pool of German unstructured medical texts created a need for a robust, data-driven and modular software framework for medical information extraction, which benefits from most recent achievements in deep learning in the domain of natural language processing (NLP). This need has not been sufficiently addressed by current research especially in the context of German medical textual data. We propose MIEdeep (Medical Information Extraction using deep learning) to (i) integrate state-of-the-art deep learning approaches for data preparation, computer-assisted rapid training data creation and medical information extraction and (ii) to make these technologies available for clinical researchers by plugging them into a modern, easy-to-access graphical user interface.In particular, we use data programming, which enables users to rapidly create large amounts of labeled training data without the need for time consuming and costly manual annotation. We build on these labeled training data to integrate state-of-the-art supervised deep learning models for medical information extraction. MIEdeep wraps these steps up into one intuitive and easy-to-use graphical user interface to make deep learning approachable for clinical domain experts.Our proposal aims to leverage the vast pool of unstructured medical data from clinical routine for secondary use cases like clinical research. The use of data programming to create training data could finally bring together healthcare data and deep learning models. In a following project these research outcomes can be used as decision support of the medical staff, thus to improve patient treatment. This project has the potential to create impact on research in clinical NLP, by providing a robust software framework using a state-of-the-art method for rapid training data creation to address current obstacles in medical information extraction. The extracted information can then be fed back into clinical routines and procedures. As the MIEdeep framework can be applied in different clinical settings, we aim to provide the framework under a creative commons license and to publish it on a public repository. This enables us, to disseminate the ongoing work and to interact with potential stakeholders and end-users.