Relationship between mortality and first-day events index from routinely gathered physiological variables in ICU patients

Rivera-Fernández, Ricardo; Castillo-Lorente, E.; Nap, Raoul E.; Vázquez-Mata, Guillermo; Miranda, Dinis Reis

doi:10.1016/j.medin.2012.04.006

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…To extract features which describe the relation between already extracted features, various methods were described: multidimensional correlation analysis [11, 38], association-rule mining [29], sequence patterns of categorized variables [32, 36, 40], convolutional dictionary learning [52], the ratio between means in sequential periods [48], the number and duration of categorical variables under/above a pre-defined threshold [8, 33, 50] and cross-correlation patterns between multiple repeated measurement trends [7, 47].…”

Section: Resultsmentioning

confidence: 99%

“…Lastly, a vast array of two-step modelling methods and three end-to-end modelling methods have been described: the long short-term recurrent neural network [39], the echo-state network [41] and fuzzy rule-based modelling [53]. Of the two-step models, the logistic regression ( n = 8) [8, 28, 33, 35, 36, 43, 44, 50] was most frequently used. No two-step models with time-varying covariates, e.g.…”

Section: Resultsmentioning

confidence: 99%

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Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis

Plate

Leur

Leenen

et al. 2019

BMC Med Res Methodol

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BackgroundThe incorporation of repeated measurements into multivariable prediction research may greatly enhance predictive performance. However, the methodological possibilities vary widely and a structured overview of the possible and utilized approaches lacks. Therefore, we [1] propose a structured framework for these approaches, [2] determine what methods are currently used to incorporate repeated measurements in prediction research in the critical care setting and, where possible, [3] assess the added discriminative value of incorporating repeated measurements.MethodsThe proposed framework consists of three domains: the observation window (static or dynamic), the processing of the raw data (raw data modelling, feature extraction and reduction) and the type of modelling. A systematic review was performed to identify studies which incorporate repeated measurements to predict (e.g. mortality) in the critical care setting. The within-study difference in c-statistics between models with versus without repeated measurements were obtained and pooled in a meta-analysis.ResultsFrom the 2618 studies found, 29 studies incorporated multiple repeated measurements. The annual number of studies with repeated measurements increased from 2.8/year (2000–2005) to 16.0/year (2016–2018). The majority of studies that incorporated repeated measurements for prediction research used a dynamic observation window, and extracted features directly from the data. Differences in c statistics ranged from − 0.048 to 0.217 in favour of models that utilize repeated measurements.ConclusionsRepeated measurements are increasingly common to predict events in the critical care domain, but their incorporation is lagging. A framework of possible approaches could aid researchers to optimize future prediction models.

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