Mining Clinical Data: Selecting Decision Support Algorithm for the MET-AP System

2005 ◽  
pp. 429-433 ◽  
Author(s):  
Jerzy Blaszczynski ◽  
Ken Farion ◽  
Wojtek Michalowski ◽  
Szymon Wilk ◽  
Steven Rubin ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Data

scholarly journals Dynamic Clinical Data Mining: Search Engine-Based Decision Support

2014 ◽  
Vol 2 (1) ◽  
pp. e13 ◽  
Author(s):  
Leo Anthony Celi ◽  
Andrew J Zimolzak ◽  
David J Stone
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Search Engine ◽  
Clinical Data ◽  
Clinical Data Mining

Integration of Clinical and Genomic Data for Decision Support in Cancer

2008 ◽  
pp. 768-776
Author(s):  
Yorgos Goletsis ◽  
Themis P. Exarchos ◽  
Nikolaos Giannakeas ◽  
Markos G. Tsipouras ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Decision Support ◽  
Molecular Biology ◽  
Early Diagnosis ◽  
Clinical Data ◽  
Support System ◽  
Genomic Data ◽  
Biological Information ◽  
Tumour Suppressor Genes ◽  
Suppressor Genes ◽  
Cancer Diseases

In this article, we address decision support for cancer by exploiting clinical data and identifying mutations on tumour suppressor genes. The goal is to perform data integration between medicine and molecular biology by developing a framework where clinical and genomic features are appropriately combined in order to handle cancer diseases. The constitution of such a decision support system is based on (a) cancer clinical data and (b) biological information that is derived from genomic sources. Through this integration, real time conclusions can be drawn for early diagnosis, staging and more effective cancer treatment.

An Ontology-based Mediator of Clinical Information for Decision Support Systems

2008 ◽  
Vol 47 (06) ◽  
pp. 549-559 ◽  
Author(s):  
K. Ohe ◽  
Y. Kawazoe
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Data ◽  
Support System ◽  
Adverse Drug Events ◽  
Clinical Information ◽  
Temporal Information ◽  
Temporal Abstraction ◽  
Alert System ◽  
Data Repositories

Summary Objective: We have been developing a decision support system that uses electronic clinical data and provides alerts to clinicians. However, the inference rules for such a system are difficult to write in terms of representing domain concepts and temporal reasoning. To address this problem, we have developed an ontologybased mediator of clinical information for the decision support system. Methods: Our approach consists of three steps: 1) development of an ontology-based mediator that represents domain concepts and temporal information; 2) mapping of clinical data to corresponding concepts in the mediator; 3) temporal abstraction that creates high-level, interval-based concepts from time-stamped clinical data. As a result, we can write a concept-based rule expression that is available for use in domain concepts and interval-based temporal information. The proposed approach was applied to a prototype of clinical alert system, and the rules for adverse drug events were executed on data gathered over a 3-month period. Results: The system generated 615 alerts. 346 cases (56%) were considered appropriate and 269 cases (44%) were inappropriate. Of the false alerts, 192 cases were due to data inaccuracy and 77 cases were due to insufficiency of the temporal abstraction. Conclusion: Our approach enabled to represent a concept-based rule expression that was available for the prototype of a clinical alert system. We believe our approach will contribute to narrow the gaps of information model between domain concepts and clinical data repositories.

scholarly journals Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Johanna Schaefer ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Data Integration ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Data ◽  
Rare Diseases ◽  
Scoping Review ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Abstract Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

Integration of Clinical and Genomic Data for Decision Support in Cancer

2011 ◽  
pp. 412-421 ◽  
Author(s):  
Yorgos Goletsis ◽  
Themis P. Exarchos ◽  
Nikolaos Giannakeas ◽  
Markos G. Tsipouras ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Decision Support ◽  
Molecular Biology ◽  
Early Diagnosis ◽  
Clinical Data ◽  
Support System ◽  
Genomic Data ◽  
Biological Information ◽  
Tumour Suppressor Genes ◽  
Suppressor Genes ◽  
Cancer Diseases

In this article, we address decision support for cancer by exploiting clinical data and identifying mutations on tumour suppressor genes. The goal is to perform data integration between medicine and molecular biology by developing a framework where clinical and genomic features are appropriately combined in order to handle cancer diseases. The constitution of such a decision support system is based on (a) cancer clinical data and (b) biological information that is derived from genomic sources. Through this integration, real time conclusions can be drawn for early diagnosis, staging and more effective cancer treatment.

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