Using Published Medical Results and Non-homogenous Data in Rule Learning

Many factors limit researchers from accessing studies' original data sets. As a result, much medical and healthcare research is based off of systematic reviews and meta-analysis of published results. However, when research involves the use of aggregated data from multiple studies, traditional machine learning-based means of analysis cannot be used. This paper describes diversity of data and results available in published man-uscripts, and relates them to a rule learning method that can be applied to build classification and predictive models from such input. The method can be used to support meta-analysis and systematic reviews. Two ap-plication areas are used to illustrate the discussed issues: diagnosis of liver diseases in patients with metabolic syndrome, and detection of polycystic ovary syndrome.

[1]  Kenneth A. Kaufman,et al.  The AQ21 Natural Induction Program for Pattern Discovery: Initial Version and its Novel Features , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).

[2]  Weiguo Fan,et al.  Literature-based discovery on the World Wide Web , 2002, TOIT.

[3]  Mark W. Lipsey,et al.  Practical Meta-Analysis , 2000 .

[4]  Massimo Volpe,et al.  Antihypertensive treatment and development of heart failure in hypertension: a Bayesian network meta-analysis of studies in patients with hypertension and high cardiovascular risk. , 2011, Archives of internal medicine.

[5]  Janusz Wojtusiak,et al.  Model Learning from Published Aggregated Data , 2011, Learning Structure and Schemas from Documents.

[6]  R. Michalski.  Attributional Calculus: A Logic and Representation Language for Natural Induction , 2004 .

[7]  Ryszard S. Michalski,et al.  Reasoning with unknown, not-applicable and irrelevant meta-values in concept learning and pattern discovery , 2011, Journal of Intelligent Information Systems.

[8]  K R Abrams,et al.  Bayesian methods in meta-analysis and evidence synthesis. , 2001, Statistical methods in medical research.

[9]  Fatos Xhafa,et al.  Learning Structure and Schemas from Documents , 2011, Studies in Computational Intelligence.

[10]  Luc De Raedt,et al.  Logical and relational learning , 2008, Cognitive Technologies.

[11]  Alex J. Sutton,et al.  Bayesian methods in meta-analysis and evidence synthesis , 2001 .

[12]  Ryszard S. Michalski,et al.  Machine Learning and Inference Laboratory The Distribution Approximation Approach to Learning from Aggregated Data , 2008 .

[13]  D. Dewailly,et al.  Polycystic ovary syndrome (PCOS). , 2010, Annales d'endocrinologie.

[14]  Saso Dzeroski,et al.  Inductive Logic Programming: Techniques and Applications , 1993 .

[15]  Janusz Wojtusiak,et al.  Rule-Based Prediction of Medical Claims' Payments: A Method and Initial Application to Medicaid Data , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[16]  Aaron M. Cohen,et al.  Research Paper: Cross-Topic Learning for Work Prioritization in Systematic Review Creation and Update , 2009, J. Am. Medical Informatics Assoc..

[17]  Marc Weeber,et al.  Literature-based Discovery , 2008 .

[18]  Janusz Wojtusiak,et al.  AQ21 USER 'S GUIDE , 2004 .

[19]  Ryszard S. Michalski,et al.  Towards application of rule learning to the meta-analysis of clinical data: An example of the metabolic syndrome , 2009, Int. J. Medical Informatics.