OBJECTIVES: This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews.
BACKGROUND: Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techn...
BACKGROUND: Finding specific scientific articles in a large collection is an important natural language processing challenge in the biomedical domain. Systematic reviews and interactive article search are the type of downstream applications that bene...
Machine learning algorithms have recently shown their precision and potential in many different use cases and fields of medicine. Most of the algorithms used are supervised and need a large quantity of labeled data to achieve high accuracy. Also, mos...
Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who needs to understand large quantities of information. In the medical domain, automatic summarization has the potenti...
BMC medical informatics and decision making
Sep 30, 2020
BACKGROUND: While clinical entity recognition mostly aims at electronic health records (EHRs), there are also the demands of dealing with the other type of text data. Automatic medical diagnosis is an example of new applications using a different dat...
BACKGROUND: Biomedical document triage is the foundation of biomedical information extraction, which is important to precision medicine. Recently, some neural networks-based methods have been proposed to classify biomedical documents automatically. I...
OBJECTIVE: In machine learning, it is evident that the classification of the task performance increases if bootstrap aggregation (bagging) is applied. However, the bagging of deep neural networks takes tremendous amounts of computational resources an...
OBJECTIVES: Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field.
OBJECTIVES: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to ...