Randomization tests
Statistical analysis with missing data
Statistical analysis with missing data
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Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
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UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Uniqueness of medical data mining
Artificial Intelligence in Medicine
Exploiting Data Missingness in Bayesian Network Modeling
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Comparing risks of alternative medical diagnosis using Bayesian arguments
Journal of Biomedical Informatics
Diagnose the mild cognitive impairment by constructing Bayesian network with missing data
Expert Systems with Applications: An International Journal
Journal of Biomedical Informatics
Using intelligence techniques to predict postoperative morbidity of endovascular aneurysm repair
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
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ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
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Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
WIMP: Web server tool for missing data imputation
Computer Methods and Programs in Biomedicine
RespiDiag: A Case-Based Reasoning System for the Diagnosis of Chronic Obstructive Pulmonary Disease
Expert Systems with Applications: An International Journal
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When machine learning algorithms are applied to data collected during the course of clinical care, it is generally accepted that the data has not been consistently collected. The absence of expected data elements is common and the mechanism through which a data element is missing often involves the clinical relevance of that data element in a specific patient. Therefore, the absence of data may have information value of its own. In the process of designing an application intended to support a medical problem list, we have studied whether the ''missingness'' of clinical data can provide useful information in building prediction models. In this study, we experimented with four methods of treating missing values in a clinical data set-two of them explicitly model the absence or ''missingness'' of data. Each of these data sets were used to build four different kinds of Bayesian classifiers-a naive Bayes structure, a human-composed network structure, and two networks based on structural learning algorithms. We compared the performance between groups with and without explicit models of missingness using the area under the ROC curve. The results showed that in most cases the classifiers trained using the explicit missing value treatments performed better. The result suggests that information may exist in ''missingness'' itself. Thus, when designing a decision support system, we suggest one consider explicitly representing the presence/absence of data in the underlying logic.