Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea
| Autor: | Probir Banerjee, Doreen Ying Ying Sim, Chee Siong Teh |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Medical diagnostic
Markov chain Mechanism (biology) business.industry Bayesian probability a-priori inferences Expert Reasoning (ER) Context (language use) Cognition medicine.disease Machine learning computer.software_genre Model-based reasoning Obstructive sleep apnea Markov Chain analyses Bayesian Approaches and Cognition-Driven Techniques Cognitive Reasoning (CR) diagnostic support medicine Obstructive Sleep Apnea (OSA) General Materials Science Artificial intelligence Psychology business computer |
| Zdroj: | Procedia - Social and Behavioral Sciences. 97:528-537 |
| ISSN: | 1877-0428 |
| DOI: | 10.1016/j.sbspro.2013.10.269 |
| Popis: | This research proposes a mechanism for cost-effective medical diagnostic support for relatively new physical ailments or diseases where there are incomplete data sets available and hence, common parameters are forced to be used for drawing a- priori inferences. We propose a simple but powerful prediction model that combines the advantages of the Bayesian Approaches and Cognition-Driven Techniques such as Expert Reasoning (ER) and Cognitive Reasoning (CR) using Markov Chain analyses. Then, we demonstrate the effectiveness of our approach in predicting Obstructive Sleep Apnea (OSA). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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