Mining Cancer-related Information in Electronic Healthcare Records with Natural Language Processing

Abstract

Cancer is in the midst of leading causes of death. In 2018, around 1,735,350 new cases of cancer were estimated and 609,640 people will die from cancer in the United States. A wealth of cancer-relevant information is conserved in a variety of types of healthcare records, for example, the electronic health records (EHRs). However, part of the critical information is organized in the free narrative text which hampers machine to interpret the information underlying the text. The development of artificial intelligence provides a variety of solutions to this plight. For example, the technology of natural language processing (NLP) has emerged bridging the gap between free text and structured representation of cancer information. Recently, several researchers have published their work on unearthing cancer-related information in EHRs based on the NLP technology. Apart from the traditional NLP methods, the development of deep learning helps EHRs mining go further.