A causal modeling framework for generating clinical practice guidelines from data

Subramani Mani, Constantin Aliferis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The practice of medicine is becoming increasingly evidence-based and clinical practice guidelines (CPGs) are necessary for advancing evidence-based medicine (EBM). We hypothesize that machine learning methods can play an important role in learning CPGs automatically from data . Automatically induced CPGs can then be used for further manual refinement and deployment, for automated guideline compliance checking, for better understanding of disease processes, and for improved physician education. We discuss why learning CPGs is a special form of computational causal discovery and why simply predictive (i.e., non-causal) methods may not be appropriate for this task.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings
Pages446-450
Number of pages5
StatePublished - Oct 15 2007
Event11th Conference on Artificial Intelligence in Medicine, AIME 2007 - Amsterdam, Netherlands
Duration: Jul 7 2007Jul 11 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4594 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th Conference on Artificial Intelligence in Medicine, AIME 2007
CountryNetherlands
CityAmsterdam
Period7/7/077/11/07

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Mani, S., & Aliferis, C. (2007). A causal modeling framework for generating clinical practice guidelines from data. In Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings (pp. 446-450). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4594 LNAI).