A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

2017 ◽  
Vol 71 (4) ◽  
pp. 231-237 ◽  
Author(s):  
Kwang Hyeon Kim ◽  
Suk Lee ◽  
Jang Bo Shim ◽  
Kyung Hwan Chang ◽  
Dae Sik Yang ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Radiation Oncology ◽  
Clinical Decision ◽  
Toxicity Prediction ◽  
Prediction Modeling ◽  
Preliminary Study

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 PO-0701: Automated application of radiation oncology prediction models for clinical decision support

2015 ◽  
Vol 115 ◽  
pp. S344-S345
Author(s):  
J. Van Soest ◽  
M.S. Marshall ◽  
R. Van Stiphout ◽  
R. Gatta ◽  
A. Damiani ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Radiation Oncology ◽  
Prediction Models ◽  
Clinical Decision

A Framework for Data and Mined Knowledge Interoperability in Clinical Decision Support Systems

2012 ◽  
pp. 84-110 ◽  
Author(s):  
Reza S. Kazemzadeh ◽  
Kamran Sartipi ◽  
Priya Jayaratna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Guideline ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Personnel

Due to reliance on human knowledge, the practice of medicine is subject to errors that endanger patients’ health and cause substantial financial loss to healthcare institutions. Computer-based decision support systems assist healthcare personnel to improve quality of clinical practice. Currently, most clinical guideline modeling languages represent decision-making knowledge in terms of basic logical expressions. In this paper, we focus on encoding, sharing, and using results of data mining analyses to influence decision making within Clinical Decision Support Systems. A knowledge management framework is proposed that addresses the issues of data and knowledge interoperability by adopting healthcare and data mining modeling standards. In a further step, data mining results are incorporated into a guideline-based decision support system. A prototype tool has been developed to provide an environment for clinical guideline authoring and execution. Also, three real world case studies have been presented, one of which is used as a running example throughout the paper.

scholarly journals An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support

2019 ◽  
Vol 6 (5) ◽  
pp. e9766
Author(s):  
Sofian Berrouiguet ◽  
Romain Billot ◽  
Mark Erik Larsen ◽  
Jorge Lopez-Castroman ◽  
Isabelle Jaussent ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Suicide Attempts ◽  
Regression Trees ◽  
Clinical Decision ◽  
Clustering Methods ◽  
Web Based ◽  
Suicide Attempters ◽  
Electronic Health

Background In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health–based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. Objective The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. Methods We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. Results We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. Conclusions Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians.

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