Neural natural language processing for unstructured data in electronic health records: a review
Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
Natural language processing for smart healthcare
Smart healthcare has achieved significant progress in recent years. Emerging artificial
intelligence (AI) technologies enable various smart applications across various healthcare …
intelligence (AI) technologies enable various smart applications across various healthcare …
Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction
Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver
impressive performance in many clinical tasks. Large training cohorts, however, are often …
impressive performance in many clinical tasks. Large training cohorts, however, are often …
Graph embedding on biomedical networks: methods, applications and evaluations
Motivation Graph embedding learning that aims to automatically learn low-dimensional
node representations, has drawn increasing attention in recent years. To date, most recent …
node representations, has drawn increasing attention in recent years. To date, most recent …
[HTML][HTML] A survey of word embeddings for clinical text
Representing words as numerical vectors based on the contexts in which they appear has
become the de facto method of analyzing text with machine learning. In this paper, we …
become the de facto method of analyzing text with machine learning. In this paper, we …
Multi-domain clinical natural language processing with MedCAT: the medical concept annotation toolkit
Electronic health records (EHR) contain large volumes of unstructured text, requiring the
application of information extraction (IE) technologies to enable clinical analysis. We present …
application of information extraction (IE) technologies to enable clinical analysis. We present …
The secondary use of electronic health records for data mining: Data characteristics and challenges
The primary objective of implementing Electronic Health Records (EHRs) is to improve the
management of patients' health-related information. However, these records have also been …
management of patients' health-related information. However, these records have also been …
[HTML][HTML] Readmission prediction using deep learning on electronic health records
Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF)
patients that pose significant health risks and escalate care cost. In order to reduce …
patients that pose significant health risks and escalate care cost. In order to reduce …
Large language models are poor medical coders—benchmarking of medical code querying
Abstract Background Large language models (LLMs) have attracted significant interest for
automated clinical coding. However, early data show that LLMs are highly error-prone when …
automated clinical coding. However, early data show that LLMs are highly error-prone when …
[HTML][HTML] CODER: Knowledge-infused cross-lingual medical term embedding for term normalization
Objective This paper aims to propose knowledge-aware embedding, a critical tool for
medical term normalization. Methods We develop CODER (Cross-lingual knowledge …
medical term normalization. Methods We develop CODER (Cross-lingual knowledge …