Assessing adverse event reports of hysteroscopic sterilization device removal using natural language processing

2021 ◽  
Author(s):  
Jialin Mao ◽  
Art Sedrakyan ◽  
Tianyi Sun ◽  
Maryam Guiahi ◽  
Scott Chudnoff ◽  
...  
Keyword(s):  
Adverse Event ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Device Removal

scholarly journals Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation

2021 ◽  
Vol 129 ◽  
pp. 104140
Author(s):  
Alireza Borjali ◽  
Martin Magnéli ◽  
David Shin ◽  
Henrik Malchau ◽  
Orhun K. Muratoglu ◽  
...  
Keyword(s):  
Adverse Event ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Event Detection ◽  
Hip Replacement ◽  
Free Text ◽  
Medical Narratives

scholarly journals Application of Natural Language Processing and Network Analysis Techniques to Post-market Reports for the Evaluation of Dose-related Anti-Thymocyte Globulin Safety Patterns

2017 ◽  
Vol 08 (02) ◽  
pp. 396-411
Author(s):  
Taxiarchis Botsis ◽  
Matthew Foster ◽  
Nina Arya ◽  
Kory Kreimeyer ◽  
Abhishek Pandey ◽  
...  
Keyword(s):  
Adverse Event ◽  
Natural Language Processing ◽  
Network Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Adverse Event Reporting System ◽  
Complete Information ◽  
The United States ◽  
Analysis Techniques ◽  
Post Market

Summary Objective: To evaluate the feasibility of automated dose and adverse event information retrieval in supporting the identification of safety patterns. Methods: We extracted all rabbit Anti-Thymocyte Globulin (rATG) reports submitted to the United States Food and Drug Administration Adverse Event Reporting System (FAERS) from the product’s initial licensure in April 16, 1984 through February 8, 2016. We processed the narratives using the Medication Extraction (MedEx) and the Event-based Text-mining of Health Electronic Records (ETHER) systems and retrieved the appropriate medication, clinical, and temporal information. When necessary, the extracted information was manually curated. This process resulted in a high quality dataset that was analyzed with the Pattern-based and Advanced Network Analyzer for Clinical Evaluation and Assessment (PANACEA) to explore the association of rATG dosing with post-transplant lymphoproliferative disorder (PTLD). Results: Although manual curation was necessary to improve the data quality, MedEx and ETHER supported the extraction of the appropriate information. We created a final dataset of 1,380 cases with complete information for rATG dosing and date of administration. Analysis in PANACEA found that PTLD was associated with cumulative doses of rATG >8 mg/kg, even in periods where most of the submissions to FAERS reported low doses of rATG. Conclusion: We demonstrated the feasibility of investigating a dose-related safety pattern for a particular product in FAERS using a set of automated tools. Citation: Botsis T, Foster M, Arya N, Kreimeyer K, Pandey A, Arya D. Application of natural language processing and network analysis techniques to post-market reports for the evaluation of dose-related anti-thymocyte globulin safety patterns. Appl Clin Inform 2017; 8: 396–411 https://doi.org/10.4338/ACI-2016-10-RA-0169

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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