A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

Abstract

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field.

Keywords: bNER; biomedical; data-mining; electronic health records; healthcare.

Figure 1

PRISMA flow diagram illustrating the steps involved in conducting a systematic literature review.

Figure 2

A general pipeline for the treatment of cancer patients along their journey through the healthcare system.

Figure 3

A visual representation of the decrease in positive sentiment between Day 19 and Day 21 of treatment.

Figure 4

Pre-trained language models (PLMs) with typical examples.