Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data

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• 作者：Vijetha Vemulapalli^a ; ^{vijetha.vemulapalli@berghealth.com} ; Jiaqi Qu^b ; Jeonifer M. Garren^a ; Leonardo O. Rodrigues^a ; Michael A. Kiebish^a ; Rangaprasad Sarangarajan^a ; Niven R. Narain^a ; Viatcheslav R. Akmaev^a
• 刊名：Artificial Intelligence in Medicine
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：74
• 期：Complete
• 页码：1-8
• 全文大小：1201 K
• 卷排序：74

文摘

Data-driven Bayesian networks based analysis has been performed on health care data. Summarized, healthcare provider level data was used for this analysis. Novel hypothesis linking diagnosis codes was proposed based on findings from Bayesian networks approach. Potential mechanisms were explored to explain novel hypothesis. This paper demonstrates the ability of artificial intelligence methods to advance medical research.