Document Detail


Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning.
MedLine Citation:
PMID:  12234718     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Steven Ganzert; Josef Guttmann; Kristian Kersting; Ralf Kuhlen; Christian Putensen; Michael Sydow; Stefan Kramer
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  26     ISSN:  0933-3657     ISO Abbreviation:  Artif Intell Med     Publication Date:    2002 Sep-Oct
Date Detail:
Created Date:  2002-09-17     Completed Date:  2002-10-23     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  69-86     Citation Subset:  IM    
Affiliation:
Department of Anesthesiology and Critical Care Medicine, Albert-Ludwigs-University Freiburg, Hugstetter Str. 55, D-79106 Freiburg, Germany. ganzert@informatik.uni-freiburg.de
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MeSH Terms
Descriptor/Qualifier:
Adult
Artificial Intelligence*
Humans
Information Storage and Retrieval*
Intensive Care Units
Lung Volume Measurements
Monitoring, Physiologic
Pressure
Regression Analysis
Respiration, Artificial*
Respiratory Distress Syndrome, Adult*
Respiratory Function Tests

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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