Learning BLSTM-CRF with Multi-channel Attribute Embedding for Medical Information Extraction

AbstractVast amounts of medical information are still recorded as unstructured text. The knowledge contained in this textual data has a great potential to improve clinical routine care, to support clinical research, and to advance personalization of medicine. To access this knowledge, the underlying data has to be semantically integrated – an essential prerequisite to which is information extraction from clinical documents.A body of work, and a good selection of openly available tools for information extraction and semantic integration in the medical domain exist, yet almost exclusively for English language documents. For German texts the situation is rather different: research work is sparse, tools are proprietary or unpublished, and rarely any freely available textual resources exist. In this survey, we (1) describe the challenges of information extraction from German medical documents and the hurdles posed to research in this area, (2) especially address the problems of missing German language resources and privacy implications, and (3) identify the steps necessary to overcome these hurdles and fuel research in semantic integration of textual clinical data.

Download Full-text

Pictorial Visualization of EMR Summary Interface and Medical Information Extraction of Clinical Notes

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) ◽

10.1109/civemsa.2018.8439958 ◽

2018 ◽

Cited By ~ 6

Author(s):

Wei Ruan ◽

Naveenkumar Appasani ◽

Katherine Kim ◽

Joseph Vincelli ◽

Hyun Kim ◽

...

Keyword(s):

Information Extraction ◽

Medical Information ◽

Clinical Notes

Download Full-text

Medical Information Extraction in European Portuguese

Handbook of Research on ICTs for Human-Centered Healthcare and Social Care Services ◽

10.4018/978-1-4666-3986-7.ch032 ◽

2013 ◽

pp. 607-626

Author(s):

Liliana Ferreira ◽

António Teixeira ◽

João Paulo Silva Cunha

Keyword(s):

Information Extraction ◽

Language Processing ◽

Medical Information ◽

Medical Patient ◽

Patient Discharge ◽

Free Text ◽

Practical Applications ◽

External Resources ◽

Electronic Storage ◽

Automatic Code

The electronic storage of medical patient data is becoming a daily experience in most of the practices and hospitals worldwide. However, much of the available data is in free text form, a convenient way of expressing concepts and events but especially challenging if one wants to perform automatic searches, summarization, or statistical analyses. Information Extraction can relieve some of these problems by offering a semantically informed interpretation and abstraction of the texts. MedInX, the Medical Information eXtraction system presented in this chapter is designed to process textual clinical discharge records in order to perform automatic and accurate mapping of free text reports onto a structured representation. MedInX components are based on Natural Language Processing principles and provide several mechanisms to read, process, and utilize external resources, such as terminologies and ontologies. MedInX current practical applications include automatic code assignment and an audit system capable of systematically analyze the content and completeness of the clinical reports. Recent evaluation efforts on a set of authentic patient discharge letters indicate that the system performs with 95% precision and recall.

Download Full-text