scholarly journals Feasibility of Natural Language Processing in Surgery: Sensitivity and Specificity Compared to Manual Extraction

2021 ◽  
Vol 233 (5) ◽  
pp. S93
Author(s):  
Martin P. Morris ◽  
Adrienne N. Christopher ◽  
Viren Patel ◽  
Joseph A. Mellia ◽  
Tong Liu ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sensitivity And Specificity ◽  
Language Processing ◽  
Manual Extraction

scholarly journals Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Benjamin L. Cook ◽  
Ana M. Progovac ◽  
Pei Chen ◽  
Brian Mullin ◽  
Sherry Hou ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Suicidal Ideation ◽  
Natural Language ◽  
Sensitivity And Specificity ◽  
Language Processing ◽  
Prediction Models ◽  
Psychiatric Symptoms ◽  
Well Being ◽  
Structured Data ◽  
Predictive Values

Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12). Predictor variables included structured items (e.g., relating to sleep and well-being) and responses to one unstructured question, “how do you feel today?” We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP-based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data-based models. The PPV, sensitivity, and specificity for NLP-based models of heightened psychiatric symptoms (GHQ-12 ≥ 4) were 0.56, 0.59, and 0.60, respectively, compared to 0.79, 0.79, and 0.85 in structured models. NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question. These models have promise for rapidly identifying persons at risk of suicide or psychological distress and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible.

scholarly journals Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review

2021 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
Mahdieh Montazeri ◽  
Ali Afraz ◽  
Raheleh Mahboob Farimani ◽  
Fahimeh Ghasemian
Keyword(s):  
Lung Cancer ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Lung Cancer Patients ◽  
The Difference ◽  
Combination Of Methods ◽  
Manual Extraction

Introduction: Lung cancer is the second most common cancer for men and women. Using natural language processing to automatically extract information from text, lead to decrease labor of manual extraction from large volume of text material and save time. The aim of this study is to systematically review of studies which reviewed NLP methods in diagnosing and staging lung cancer.Material and Methods:  PubMed, Scopus, Web of science, Embase was searched for English language articles that reported diagnosing and staging methods in lung cancer Using NLP until DEC 2019. Two reviewers independently assessed original papers to determine eligibility for inclusion in the review.Results: Of 119 studies, 7 studies were included. Three studies developed a NLP algorithm to scan radiology notes and determine the presence or absence of nodules to identify patients with incident lung nodules for treatment or follow-up. Two studies used NLP to transform the report text, including identification of UMLS terms and detection of negated findings to classifying reports, also one of them used an SVM-based text classification system for staging lung cancer patients. All studies reported various performance measures based on the difference between combination of methods. Most of studies have reported sensitivity and specificity of the NLP algorithm for identifying the presence of lung nodules.Conclusion: Evaluation of studies in diagnosing and staging methods in lung cancer using NLP shows there is a number of studies on diagnosing lung cancer but there are a few works on staging that. In some studies, combination of methods was considered and NLP isolated was not sufficient for capturing satisfying results. There are potentials to improve studies by adding other data sources, further refinement and subsequent validation.

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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