Interactive Information Retrieval as a Step Towards Effective Knowledge Management in Healthcare

2011 ◽  
pp. 240-256
Author(s):  
Jörg Ontrup
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Medical Knowledge ◽  
Hospital Information Systems ◽  
Data Sets ◽  
Semantic Organization ◽  
Huge Data ◽  
Modern Information ◽  
Novel Algorithms ◽  
Data Collections

The chapter shows how modern information retrieval methodologies can open up new possibilities to support knowledge management in healthcare. Recent advances in hospital information systems lead to the acquisition of huge quantities of data, often characterized by a high proportion of free narrative text embedded in the electronic health record. We point out how text mining techniques augmented by novel algorithms that combine artificial neural networks for the semantic organization of non-crisp data and hyperbolic geometry for an intuitive navigation in huge data sets can offer efficient tools to make medical knowledge in such data collections more accessible to the medical expert by providing context information and links to knowledge buried in medical literature databases.

Interactive Information Retrieval as a Step Towards Effective Knowledge Management in Healthcare

2011 ◽  
pp. 1245-1261
Author(s):  
Jörg Ontrup ◽  
Helge Ritter
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Medical Knowledge ◽  
Hospital Information Systems ◽  
Data Sets ◽  
Semantic Organization ◽  
Huge Data ◽  
Modern Information ◽  
Novel Algorithms ◽  
Data Collections

The chapter shows how modern information retrieval methodologies can open up new possibilities to support knowledge management in healthcare. Recent advances in hospital information systems lead to the acquisition of huge quantities of data, often characterized by a high proportion of free narrative text embedded in the electronic health record. We point out how text mining techniques augmented by novel algorithms that combine artificial neural networks for the semantic organization of non-crisp data and hyperbolic geometry for an intuitive navigation in huge data sets can offer efficient tools to make medical knowledge in such data collections more accessible to the medical expert by providing context information and links to knowledge buried in medical literature databases.

Interactive Information Retrieval as a Step Towards Effective Knowledge Management in Healthcare

2005 ◽  
pp. 52-71
Author(s):  
Jörg Ontrup ◽  
Helge Ritter
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Medical Knowledge ◽  
Hospital Information Systems ◽  
Data Sets ◽  
Semantic Organization ◽  
Huge Data ◽  
Modern Information ◽  
Novel Algorithms ◽  
Data Collections

The chapter shows how modern information retrieval methodologies can open up new possibilities to support knowledge management in healthcare. Recent advances in hospital information systems lead to the acquisition of huge quantities of data, often characterized by a high proportion of free narrative text embedded in the electronic health record. We point out how text mining techniques augmented by novel algorithms that combine artificial neural networks for the semantic organization of non-crisp data and hyperbolic geometry for an intuitive navigation in huge data sets can offer efficient tools to make medical knowledge in such data collections more accessible to the medical expert by providing context information and links to knowledge buried in medical literature databases.

CIKM 2020 conference report

2021 ◽  
pp. 1-4
Author(s):  
Mathieu D'Aquin ◽  
Stefan Dietze
Keyword(s):  
United States ◽  
Machine Learning ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Conference Report ◽  
The United States ◽  
Knowledge Based ◽  
The World ◽  
Science Conference ◽  
Information And Knowledge Management

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.

Search Variability and Interlibrary Services

Infolib ◽  
2020 ◽  
Vol 24 (4) ◽  
pp. 16-21
Author(s):  
Irina Krasilnikova ◽  
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
Electronic Resources ◽  
Scientific Publications ◽  
Retrieval Systems ◽  
Electronic Catalogs ◽  
Information Retrieval Systems ◽  
Modern Information ◽  
Foreign Materials ◽  
Search For Information

The urgency of the problem is associated with an increase in the number of electronic resources in many information and library institutions, the need to search for information from any sources, including external ones, the provision of documents from a group of funds (corporations), the presence of electronic catalogs and search systems. Finding information from catalogs and other search engines has always preceded the execution of orders in the interlibrary service. Borrowing and using documents from different collections (provision of interlibrary services) is possible only if there is up-to-date metadata of modern information retrieval systems (ISS). The purpose of the article is to summarize the results of studying several types of search engines. At the same time, attention was drawn to new scientific publications on the topic under study. An analysis of domestic and foreign materials on the options for searching for information is presented, which is very necessary for users, including those who are remote in the provision of interlibrary services.

scholarly journals Future of Medical Research in Rare Diseases and Cancers: Shift from Pharma to Biotech and the Golden Age of Medical Advancement

2017 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Abhith Pallegar
Keyword(s):  
Big Data ◽  
Medical Research ◽  
Rare Diseases ◽  
Network Effects ◽  
Medical Knowledge ◽  
Economic Cost ◽  
Medical Data ◽  
Data Sets ◽  
Leading Role ◽  
Diverse Data

The objective of the paper is to elucidate how interconnected biological systems can be better mapped and understood using the rapidly growing area of Big Data. We can harness network efficiencies by analyzing diverse medical data and probe how we can effectively lower the economic cost of finding cures for rare diseases. Most rare diseases are due to genetic abnormalities, many forms of cancers develop due to genetic mutations. Finding cures for rare diseases requires us to understand the biology and biological processes of the human body. In this paper, we explore what the historical shift of focus from pharmacology to biotechnology means for accelerating biomedical solutions. With biotechnology playing a leading role in the field of medical research, we explore how network efficiencies can be harnessed by strengthening the existing knowledge base. Studying rare or orphan diseases provides rich observable statistical data that can be leveraged for finding solutions. Network effects can be squeezed from working with diverse data sets that enables us to generate the highest quality medical knowledge with the fewest resources. This paper examines gene manipulation technologies like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) that can prevent diseases of genetic variety. We further explore the role of the emerging field of Big Data in analyzing large quantities of medical data with the rapid growth of computing power and some of the network efficiencies gained from this endeavor. 

scholarly journals A Dynamic Methodology for Improving the Search Experience

2006 ◽  
Vol 25 (2) ◽  
pp. 78 ◽  
Author(s):  
Marcia D. Kerchner
Keyword(s):  
Information Retrieval ◽  
Systems Engineering ◽  
Information Seeking ◽  
Business Owners ◽  
Early Years ◽  
Search Performance ◽  
Working Relationship ◽  
Help Desk ◽  
Modern Information

In the early years of modern information retrieval, the fundamental way in which we understood and evaluated search performance was by measuring precision and recall. In recent decades, however, models of evaluation have expanded to incorporate the information-seeking task and the quality of its outcome, as well as the value of the information to the user. We have developed a systems engineering-based methodology for improving the whole search experience. The approach focuses on understanding users’ information-seeking problems, understanding who has the problems, and applying solutions that address these problems. This information is gathered through ongoing analysis of site-usage reports, satisfaction surveys, Help Desk reports, and a working relationship with the business owners.

A Comprehensive Assessment of Modern Information Retrieval Tools

2007 ◽  
pp. 263-275 ◽  
Author(s):  
Nishant Kumar ◽  
Jan De Beer ◽  
Jan Vanthienen ◽  
Marie-Francine Moens
Keyword(s):  
Information Retrieval ◽  
Comprehensive Assessment ◽  
Modern Information

scholarly journals Informative Frailty Indices from Binarized Biomarkers

2020 ◽  
Author(s):  
Garrett Stubbings ◽  
Spencer Farrell ◽  
Arnold Mitnitski ◽  
Kenneth Rockwood ◽  
Andrew Rutenberg
Keyword(s):  
Medical Knowledge ◽  
Data Sets ◽  
Canadian Study ◽  
Adverse Health Outcomes ◽  
Health And Nutrition ◽  
Health Measure ◽  
Upper Limits ◽  
Insight Into ◽  
Better Than ◽  
Biomarker Data

AbstractFrailty indices (FI) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FI from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a “quantile” methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey (NHANES) and the Canadian Study of Health and Aging (CSHA) data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, the upper limits of the FI observed in various data sets, and highlights general difficulties in obtaining absolute scales for comparing FI between studies.

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