scholarly journals Automated Detection of Adverse Events Using Natural Language Processing of Discharge Summaries

2005 ◽  
Vol 12 (4) ◽  
pp. 448-457 ◽  
Author(s):  
Genevieve B. Melton ◽  
George Hripcsak
Keyword(s):  
Natural Language Processing ◽  
Adverse Events ◽  
Natural Language ◽  
Language Processing ◽  
Automated Detection ◽  
Discharge Summaries

scholarly journals Developing A Deep Learning Natural Language Processing Algorithm For Automated Reporting Of Adverse Drug Reactions

2021 ◽  
Author(s):  
Christopher McMaster ◽  
Julia Chan ◽  
David FL Liew ◽  
Elizabeth Su ◽  
Albert G Frauman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Adverse Events ◽  
Natural Language ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
Processing Algorithm ◽  
Drug Reactions ◽  
Discharge Summaries ◽  
Natural Language Processing Algorithm

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 150,000 unlabelled discharge summaries; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.934 (95% CI 0.931 - 0.955) for the task of identifying discharge summaries containing ADR mentions.

Health Natural Language Processing: Methodology Development and Applications (Preprint)

2020 ◽  
Author(s):  
Tianyong Hao ◽  
Zhengxing Huang ◽  
Likeng Liang ◽  
Heng Weng ◽  
Buzhou Tang
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Technologies ◽  
Medical Decision ◽  
Eligibility Criteria ◽  
Text Data ◽  
Methodology Development ◽  
Domain Experts ◽  
Discharge Summaries

UNSTRUCTURED With the rapid growth of information technology, the necessity for processing massive amounts of health and medical data utilizing advanced information technologies has also grown. A large amount of valuable data exists in natural text such as free diagnosis text, discharge summaries, online health discussions, eligibility criteria of clinical trials, and so on. Health natural language processing automatically analyzes the commonalities and differences of large amounts of text data and recommend appropriate actions on behalf of domain experts to assist medical decision making. This editorial shares the methodology innovation of health natural language processing and its applications in medial domain.

Natural language processing for automated detection of incidental durotomy

2020 ◽  
Vol 20 (5) ◽  
pp. 695-700 ◽  
Author(s):  
Aditya V. Karhade ◽  
Michiel E.R. Bongers ◽  
Olivier Q. Groot ◽  
Erick R. Kazarian ◽  
Thomas D. Cha ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automated Detection ◽  
Incidental Durotomy

scholarly journals Cross-linguistic automated detection of metaphors for poverty and cancer

2018 ◽  
Vol 10 (3) ◽  
pp. 467-493 ◽  
Author(s):  
OANA DAVID ◽  
TEENIE MATLOCK
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Social Problems ◽  
Conceptual Metaphor ◽  
Automated Detection ◽  
Analytic Tool ◽  
Corpus Linguistic ◽  
Recent Developments ◽  
Corpus Data

abstractConceptual metaphor research has benefited from advances in discourse analytic and corpus linguistic methodologies over the years, especially given recent developments with Natural Language Processing (NLP) technologies. Such technologies are now capable of identifying metaphoric expressions across large bodies of text. Here we focus on how one particular analytic tool, MetaNet, can be used to study everyday discourse about personal and social problems, in particular, poverty and cancer, by leveraging reusable networks of primary metaphors enhanced with specific metaphor subcases. We discuss the advantages of this approach in allowing us to gain valuable insights into cross-linguistic metaphor commonalities and variation. To demonstrate its utility, we analyze corpus data from English and Spanish.

scholarly journals Predicting early psychiatric readmission with natural language processing of narrative discharge summaries

2016 ◽  
Vol 6 (10) ◽  
pp. e921-e921 ◽  
Author(s):  
A Rumshisky ◽  
M Ghassemi ◽  
T Naumann ◽  
P Szolovits ◽  
V M Castro ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Psychiatric Readmission ◽  
Discharge Summaries
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