Creation of a simple natural language processing tool to support an imaging utilization quality dashboard

2017 ◽  
Vol 101 ◽  
pp. 93-99 ◽  
Author(s):  
Jordan Swartz ◽  
Christian Koziatek ◽  
Jason Theobald ◽  
Silas Smith ◽  
Eduardo Iturrate
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Processing Tool ◽  
Imaging Utilization

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

2021 ◽  
Author(s):  
Xinxu Shen ◽  
Troy Houser ◽  
David Victor Smith ◽  
Vishnu P. Murty
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Individual Difference ◽  
Language Processing ◽  
Large Scale ◽  
High Reliability ◽  
Difference Analysis ◽  
Universal Sentence ◽  
Natural Language Processing Tool

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

A Natural Language Processing Tool to Support the Electronic Invoicing Process in Italy

2021 ◽  
Author(s):  
Luigi Di Puglia Pugliese ◽  
Francesca Guerriero ◽  
Giusy Macrina ◽  
Enza Messina
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Processing Tool

scholarly journals A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153749 ◽  
Author(s):  
Chinmoy Nath ◽  
Mazen S. Albaghdadi ◽  
Siddhartha R. Jonnalagadda
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Data Extraction ◽  
Large Scale Data ◽  
Natural Language Processing Tool ◽  
Scale Data

scholarly journals A Natural Language Processing Tool to Extract Quantitative Smoking Status from Clinical Narratives

2020 ◽  
Author(s):  
Xi Yang ◽  
Hanyuan Yang ◽  
Tianchen Lyu ◽  
Shuang Yang ◽  
Yi Guo ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Smoking Status ◽  
Lung Cancer Screening ◽  
Rule Engine ◽  
Training Set ◽  
Test Set ◽  
Clinical Notes ◽  
Natural Language Processing Tool

AbstractThis study presents a natural language processing (NLP) tool to extract quantitative smoking information (e.g., Pack-Year, Quit Year, Smoking Year, and Pack per Day) from clinical notes and standardized them into Pack-Year unit. We annotated a corpus of 200 clinical notes from patients who had low-dose CT imaging procedures for lung cancer screening and developed an NLP system using a two-layer rule-engine structure. We divided the 200 notes into a training set and a test set and developed the NLP system only using the training set. The experimental results on the test set showed that our NLP system achieved the best F1 scores of 0.963 and 0.946 for lenient and strict evaluation, respectively.NoteAccepted as a presentation at the 2020 IEEE International Conference on Healthcare Informatics (ICHI) Workshop on Health Natural Language Processing (HealthNLP 2020).https://ohnlp.github.io/HealthNLP2020/healthnlp2020#.

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