AN ANALYSIS OF SCIENTIFIC PUBLICATIONS ON "DECISION SUPPORT SYSTEMS" AND "BUSINESS INTELLIGENCE" REGARDING RELATED CONCEPTS USING NATURAL LANGUAGE PROCESSING TOOLS

More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of these drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs. In many cases, there is already evidence of efficacy for cancer, but trying to manually extract such evidence from the scientific literature is intractable. In this emerging applications paper, we introduce a system to automate non-cancer generic drug evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language processing techniques and plan to treat them as baseline approaches for future improvement of individual components.

Download Full-text

Business Intelligence and Data Mining

Research and Development in E-Business through Service-Oriented Solutions - Advances in E-Business Research ◽

10.4018/978-1-4666-4181-5.ch003 ◽

2013 ◽

pp. 57-70

Author(s):

Zsolt T. Kardkovács

Keyword(s):

Data Mining ◽

Decision Support ◽

Strategic Planning ◽

Decision Support Systems ◽

Business Intelligence ◽

Business Processes ◽

Support Systems ◽

Decision Makers ◽

Key Factors ◽

Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

Download Full-text

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

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa250 ◽

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.

Download Full-text

A Benchmark to Select Data Mining Based Classification Algorithms for Business Intelligence and Decision Support Systems

International Journal of Data Mining & Knowledge Management Process ◽

10.5121/ijdkp.2012.2503 ◽

2012 ◽

Vol 2 (5) ◽

pp. 25-42 ◽

Cited By ~ 5

Author(s):

Pardeep Kumar

Keyword(s):

Data Mining ◽

Decision Support ◽

Decision Support Systems ◽

Business Intelligence ◽

Support Systems ◽

Classification Algorithms

Download Full-text

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

Management of the personnel and intellectual resources in Russia ◽

10.12737/2305-7807-2020-5-9 ◽

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.

Download Full-text