scholarly journals Earthquake management: a decision support system based on natural language processing

2016 ◽  
Vol 8 (1) ◽  
pp. 37-45 ◽  
Author(s):  
E. Fersini ◽  
E. Messina ◽  
F. A. Pozzi
Keyword(s):  
Natural Language Processing ◽  
Decision Support ◽  
Decision Support System ◽  
Natural Language ◽  
Language Processing ◽  
Support System

A natural language processing based group decision support system

1994 ◽  
Vol 12 (3) ◽  
pp. 181-188 ◽  
Author(s):  
Sumali P Conlon ◽  
Brian J Reithel ◽  
Milam W Aiken ◽  
Ashraf I Shirani
Keyword(s):  
Natural Language Processing ◽  
Decision Support ◽  
Decision Support System ◽  
Natural Language ◽  
Language Processing ◽  
Support System ◽  
Group Decision ◽  
Group Decision Support ◽  
Group Decision Support System

scholarly journals Information retrieval with text mining for Decision Support System

2012 ◽  
Vol 24 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Mahmuda Rahman
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Natural Language Processing ◽  
Decision Support ◽  
Text Mining ◽  
Decision Support System ◽  
Natural Language ◽  
Key Words ◽  
Language Processing ◽  
Support System

Key words: Natural language processing; C4.5 classification; DSS, machine learning; KNN clustering; SVMDOI: http://dx.doi.org/10.3329/bjsr.v24i2.10768 Bangladesh J. Sci. Res. 24(2):117-126, 2011 (December) 

scholarly journals Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients undergoing endoscopy

2020 ◽  
Author(s):  
Lin Shen ◽  
Adam Wright ◽  
Linda S Lee ◽  
Kunal Jajoo ◽  
Jennifer Nayor ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Decision Support ◽  
Natural Language ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Expert Consensus ◽  
Order Error

Abstract Objective Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decision support system (CDSS) to review orders for patients undergoing outpatient endoscopy. Materials and Methods The CDSS was developed and implemented by an expert panel using an agile approach. The CDSS queried patient-specific historical endoscopy records and applied expert consensus-derived logic and natural language processing to identify possible sedation order errors for human review. A retrospective analysis was conducted to evaluate impact, comparing 4-month pre-pilot and 12-month pilot periods. Results 22 755 endoscopy cases were included (pre-pilot 6434 cases, pilot 16 321 cases). The CDSS decreased the sedation-type order error rate on day of endoscopy (pre-pilot 0.39%, pilot 0.037%, Odds Ratio = 0.094, P-value < 1e-8). There was no difference in background prevalence of erroneous orders (pre-pilot 0.39%, pilot 0.34%, P = .54). Discussion At our institution, low prevalence and high volume of cases prevented routine manual review to verify sedation order appropriateness. Using a cohort-enrichment strategy, a CDSS was able to reduce number of chart reviews needed per sedation-order error from 296.7 to 3.5, allowing for integration into the existing workflow to intercept rare but important ordering errors. Conclusion A workflow-integrated CDSS with expert consensus-derived logic rules and natural language processing significantly reduced endoscopy sedation-type order errors on day of endoscopy at our institution.

scholarly journals A Decision Support System for Predicting Socially Depressed Users Using Bidirectional Encoders Representations from Transformers (BERT)

2021 ◽  
Vol 23 (3) ◽  
Author(s):  
Maharukh Syed ◽  
◽  
Meera Narvekar ◽  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Decision Support ◽  
Decision Support System ◽  
Language Processing ◽  
21St Century ◽  
Support System ◽  
Contextual Data ◽  
Processing Techniques

Depression is one of the leading causes of suicides in society. The youth of the 21st century are inclined towards social media for all their needs and expressions. Close friends can easily predict if someone is happy, sad, or depressed from a user’s daily social media activity like status uploads/shares/reposts/check-ins, etc. This activity can be analyzed in order to understand the pattern of mental health. Such data is easily available and if suspected, it can be reported to a Psychiatrist and Psychologist to prevent socially active depressed patients from taking any wrong decisions regarding their life thus providing a Decision Support System (DSS). Various natural language processing techniques have been used in order to detect depression but there is a need for a unified architecture that is based on contextual data and is bidirectional in nature. This can be achieved by using example be achieved by using the Google research project (BERT) Bidirectional Encoder Representations from Transformers.

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