Challenges and opportunities beyond structured data in analysis of electronic health records
M Tayefi, P Ngo, T Chomutare… - Wiley …, 2021 - Wiley Online Library
Electronic health records (EHR) contain a lot of valuable information about individual
patients and the whole population. Besides structured data, unstructured data in EHRs can …
patients and the whole population. Besides structured data, unstructured data in EHRs can …
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 …
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
Early prediction of patient outcomes is important for targeting preventive care. This protocol
describes a practical workflow for developing deep-learning risk models that can predict …
describes a practical workflow for developing deep-learning risk models that can predict …
Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review
Objective To determine the effects of using unstructured clinical text in machine learning
(ML) for prediction, early detection, and identification of sepsis. Materials and methods …
(ML) for prediction, early detection, and identification of sepsis. Materials and methods …
Use of unstructured text in prognostic clinical prediction models: a systematic review
Objective This systematic review aims to assess how information from unstructured text is
used to develop and validate clinical prognostic prediction models. We summarize the …
used to develop and validate clinical prognostic prediction models. We summarize the …
[HTML][HTML] Digital health data quality issues: systematic review
Background The promise of digital health is principally dependent on the ability to
electronically capture data that can be analyzed to improve decision-making. However, the …
electronically capture data that can be analyzed to improve decision-making. However, the …
[HTML][HTML] “Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks
One unintended consequence of the Electronic Health Records (EHR) implementation is the
overuse of content-importing technology, such as copy-and-paste, that creates “bloated” …
overuse of content-importing technology, such as copy-and-paste, that creates “bloated” …
[HTML][HTML] Natural language processing for clinical notes in dentistry: a systematic review
F Pethani, AG Dunn - Journal of Biomedical Informatics, 2023 - Elsevier
Objective To identify and synthesise research on applications of natural language
processing (NLP) for information extraction and retrieval from clinical notes in dentistry …
processing (NLP) for information extraction and retrieval from clinical notes in dentistry …
Online information leaker identification scheme for secure data sharing
This paper proposes a novel scheme for the leaker identification that deals with the dynamic
scenario by handling the requests of the users in an online custom. A distribution strategy is …
scenario by handling the requests of the users in an online custom. A distribution strategy is …
Explainable clinical decision support from text
Clinical prediction models often use structured variables and provide outcomes that are not
readily interpretable by clinicians. Further, free-text medical notes may contain information …
readily interpretable by clinicians. Further, free-text medical notes may contain information …