{"title":"icuARM-II:提高儿科重症监护病房个性化风险预测的可靠性。","authors":"Chih-Wen Cheng, Nikhil Chanani, Kevin Maher, Wang","doi":"10.1145/2649387.2649440","DOIUrl":null,"url":null,"abstract":"<p><p>Clinicians in intensive care units (ICUs) rely on standardized scores as risk prediction models to predict a patient's vulnerability to life-threatening events. Conventional Current scales calculate scores from a fixed set of conditions collected within a specific time window. However, modern monitoring technologies generate complex, temporal, and multimodal patient data that conventional prediction models scales cannot fully utilize. Thus, a more sophisticated model is needed to tailor individual characteristics and incorporate multiple temporal modalities for a personalized risk prediction. Furthermore, most scales models focus on adult patients. To address this needdeficiency, we propose a newly designed ICU risk prediction system, called icuARM-II, using a large-scaled pediatric ICU database from Children's Healthcare of Atlanta. This novel database contains clinical data collected in 5,739 ICU visits from 4,975 patients. We propose a temporal association rule mining framework giving clinicians a potential to perform predict risks prediction based on all available patient conditions without being restricted by a fixed observation window. We also develop a new metric that can rigidly assesses the reliability of all all generated association rules. In addition, the icuARM-II features an interactive user interface. Using the icuARM-II, our results demonstrated showed a use case of short-term mortality prediction using lab testing results, which demonstrated a potential new solution for reliable ICU risk prediction using personalized clinical data in a previously neglected population.</p>","PeriodicalId":72044,"journal":{"name":"ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983419/pdf/nihms805837.pdf","citationCount":"0","resultStr":"{\"title\":\"icuARM-II: improving the reliability of personalized risk prediction in pediatric intensive care units.\",\"authors\":\"Chih-Wen Cheng, Nikhil Chanani, Kevin Maher, Wang\",\"doi\":\"10.1145/2649387.2649440\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Clinicians in intensive care units (ICUs) rely on standardized scores as risk prediction models to predict a patient's vulnerability to life-threatening events. Conventional Current scales calculate scores from a fixed set of conditions collected within a specific time window. However, modern monitoring technologies generate complex, temporal, and multimodal patient data that conventional prediction models scales cannot fully utilize. Thus, a more sophisticated model is needed to tailor individual characteristics and incorporate multiple temporal modalities for a personalized risk prediction. Furthermore, most scales models focus on adult patients. To address this needdeficiency, we propose a newly designed ICU risk prediction system, called icuARM-II, using a large-scaled pediatric ICU database from Children's Healthcare of Atlanta. This novel database contains clinical data collected in 5,739 ICU visits from 4,975 patients. We propose a temporal association rule mining framework giving clinicians a potential to perform predict risks prediction based on all available patient conditions without being restricted by a fixed observation window. We also develop a new metric that can rigidly assesses the reliability of all all generated association rules. In addition, the icuARM-II features an interactive user interface. Using the icuARM-II, our results demonstrated showed a use case of short-term mortality prediction using lab testing results, which demonstrated a potential new solution for reliable ICU risk prediction using personalized clinical data in a previously neglected population.</p>\",\"PeriodicalId\":72044,\"journal\":{\"name\":\"ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983419/pdf/nihms805837.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM-BCB ... ... : the ... 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icuARM-II: improving the reliability of personalized risk prediction in pediatric intensive care units.
Clinicians in intensive care units (ICUs) rely on standardized scores as risk prediction models to predict a patient's vulnerability to life-threatening events. Conventional Current scales calculate scores from a fixed set of conditions collected within a specific time window. However, modern monitoring technologies generate complex, temporal, and multimodal patient data that conventional prediction models scales cannot fully utilize. Thus, a more sophisticated model is needed to tailor individual characteristics and incorporate multiple temporal modalities for a personalized risk prediction. Furthermore, most scales models focus on adult patients. To address this needdeficiency, we propose a newly designed ICU risk prediction system, called icuARM-II, using a large-scaled pediatric ICU database from Children's Healthcare of Atlanta. This novel database contains clinical data collected in 5,739 ICU visits from 4,975 patients. We propose a temporal association rule mining framework giving clinicians a potential to perform predict risks prediction based on all available patient conditions without being restricted by a fixed observation window. We also develop a new metric that can rigidly assesses the reliability of all all generated association rules. In addition, the icuARM-II features an interactive user interface. Using the icuARM-II, our results demonstrated showed a use case of short-term mortality prediction using lab testing results, which demonstrated a potential new solution for reliable ICU risk prediction using personalized clinical data in a previously neglected population.