Medical informatics: computer applications in health care and biomedicine (Health informatics)
Medical informatics: computer applications in health care and biomedicine (Health informatics)
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Model Driven Engineering and Ontology Development
Model Driven Engineering and Ontology Development
Java-ML: A Machine Learning Library
The Journal of Machine Learning Research
Guest editorial: Knowledge-based data analysis and interpretation
Artificial Intelligence in Medicine
KNIME - the Konstanz information miner: version 2.0 and beyond
ACM SIGKDD Explorations Newsletter
A four stage approach for ontology-based health information system design
Artificial Intelligence in Medicine
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
| Hi-index | 12.05 |
Implementing a computerized clinical research information system (CRIS) can make clinical research easier and more efficient while improving patient care by providing surgeons with performance feedback. To transform the original manual patient information management system so it delivers patient care, this study developed a CRIS for cardiovascular disease to facilitate surgery treatment tracking. The CRIS tracks hundreds of pieces of data through surgical stages and converts these data into computerized registries, provides surveillance mechanisms, and generates clinical interpretive reports in a timely manner. Surgeons can use the CRIS to identify surgical-related data and interventional cardiovascular procedure risks based on specific patient characteristics, and it has increased the quality and efficiency of patient care. An intelligent data analysis (IDA) tool based on the Weka library that seamlessly integrates with the CRIS has helped provide models for clinical research.