C4.5: programs for machine learning
C4.5: programs for machine learning
Multidimensional curve classification using passing—through regions
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Machine Learning
Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Mining data from intensive care patients
Advanced Engineering Informatics
Prediction of Mechanical Lung Parameters Using Gaussian Process Models
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
A predictive model for cerebrovascular disease using data mining
Expert Systems with Applications: An International Journal
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We present a case study of machine learning and data mining in intensive care medicine. In the study, we compared different methods of measuring pressure-volume curves in artificially ventilated patients suffering from the adult respiratory distress syndrome (ARDS). Our aim was to show that inductive machine learning can be used to gain insights into differences and similarities among these methods. We defined two tasks: the first one was to recognize the measurement method producing a given pressure-volume curve. This was defined as the task of classifying pressure-volume curves (the classes being the measurement methods). The second was to model the curves themselves, that is, to predict the volume given the pressure, the measurement method and the patient data. Clearly, this can be defined as a regression task. For these two tasks, we applied C5.0 and CUBIST, two inductive machine learning tools, respectively. Apart from medical findings regarding the characteristics of the measurement methods, we found some evidence showing the value of an abstract representation for classifying curves: normalization and high-level descriptors from curve fitting played a crucial role in obtaining reasonably accurate models. Another useful feature of algorithms for inductive machine learning is the possibility of incorporating background knowledge. In our study, the incorporation of patient data helped to improve regression results dramatically, which might open the door for the individual respiratory treatment of patients in the future.