Development of an Intelligent NLP-Based Audit Plan Knowledge Discovery System

2019 ◽  
Vol 17 (1) ◽  
pp. 89-97
Author(s):  
Qiao Li ◽  
Junming Liu
Keyword(s):  
Natural Language Processing ◽  
Decision Support ◽  
Natural Language ◽  
Knowledge Base ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Discovery System

ABSTRACT Auditors' discussions in audit plan brainstorming sessions provide valuable knowledge on how audit engagement teams evaluate information, identify and assess risks, and make audit decisions. Collected expertise and experience from experienced auditors can be used as decision support for future audit plan engagements. With the help of Natural Language Processing (NLP) techniques, this paper proposes an intelligent NLP-based audit plan knowledge discovery system (APKDS) that can collect and extract important contents from audit brainstorming discussions and transfer the extracted contents into an audit knowledge base for future use.

scholarly journals Looking through glass: Knowledge discovery from materials science literature using natural language processing

Patterns ◽  
2021 ◽  
pp. 100290
Author(s):  
Vineeth Venugopal ◽  
Sourav Sahoo ◽  
Mohd Zaki ◽  
Manish Agarwal ◽  
Nitya Nand Gosvami ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Materials Science ◽  
Science Literature

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.

The Concept of Integrating Artificial Intelligence Technologies Into Human Resources in a Digital Paradigm

2020 ◽  
Vol 9 (2) ◽  
pp. 5-9
Author(s):  
Oksana Chulanova
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Decision Support ◽  
Speech Recognition ◽  
Human Resources ◽  
Natural Language ◽  
Language Processing

The article discusses the capabilities of artificial intelligence technologies - technologies based on the use of artificial intelligence, including natural language processing, intellectual decision support, computer vision, speech recognition and synthesis, and promising methods of artificial intelligence. The results of the author's study and the analysis of artificial intelligence technologies and their capabilities for optimizing work with staff are presented. A study conducted by the author allowed us to develop an author's concept of integrating artificial intelligence technologies into work with personnel in the digital paradigm.

scholarly journals Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods

Cancer ◽  
2016 ◽  
Vol 123 (1) ◽  
pp. 114-121 ◽  
Author(s):  
Tejal A. Patel ◽  
Mamta Puppala ◽  
Richard O. Ogunti ◽  
Joe E. Ensor ◽  
Tiancheng He ◽  
...  
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Decision Support ◽  
Natural Language ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Clinical Decision ◽  
Pathologic Findings ◽  
Mining Methods

scholarly journals System of classification for personnel selection based upon Ukrainian language analyze

2020 ◽  
pp. 034-040
Author(s):  
O.P. Zhezherun ◽  
◽  
M.S. Ryepkin ◽  
◽  
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Classification System ◽  
Personnel Selection ◽  
Classification Systems ◽  
System Expansion ◽  
Recruitment Process

The article describes a classification system with natural language processing. Many systems use neural networks, but it needs massive amounts of data for training, which is not always available. Authors propose to use ontologies in such systems. As example of such approach it is shown the classification system, which helps to form a list of the best candidates during the recruitment process. An overview of the methods for ontologies constructing and language analyzers appropriate for classification systems are presented. The system in the form of a knowledge base is constracted. Described system supports Ukrainian and English languages. The possible ways of system expansion is regarded.

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