Variation Prediction in Clinical Processes

Autor:	Chenxi Gan, Zhengxing Huang, Xudong Lu, Huilong Duan
Rok vydání:	2011
Předmět:	Measure (data warehouse) Operations research Computer science business.industry Variation (game tree) Machine learning computer.software_genre Clinical pathway Patient state Clinical case Artificial intelligence General hospital Set (psychology) Construct (philosophy) business computer
Zdroj:	Artificial Intelligence in Medicine ISBN: 9783642222177 AIME
DOI:	10.1007/978-3-642-22218-4_36
Popis:	For clinical processes, meaningful variations may be related to care performance or even the patient survival. It is imperative that the variations be predicted timely so that the patient care "journey" can be more adaptive and efficient. This study addresses the question of how to predict variations in clinical processes. Given the assumption that a clinical case with low appropriateness between its specific patient state and its' applied medical intervention is more likely to be a variation than other cases, this paper proposes a method to construct an appropriateness measure model based on historical clinical cases so as to predict such variations in future cases of clinical processes. The proposed method is demonstrated on a real life data set from the Chinese Liberation Army General Hospital. The experimental results confirm the given assumption and indicate the feasibility of the proposed method.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9e1c305b69a7a2e7a2afe746bc831969 https://doi.org/10.1007/978-3-642-22218-4_36 Zobrazit plný text záznamu