Evolving classification of intensive care patients from event data

Predictive model of hospitalization for children and adolescents with chronic disease

ABSTRACT Objectives: Describe a predictive model of hospitalization frequency for children and adolescents with chronic disease. Methods: A decision tree-based model was built using a database of 141 children and adolescents with chronic disease admitted to a federal public hospital; 18 variables were included and the frequency of hospitalization was defined as the outcome. Results: The decision tree obtained in this study could properly classify 80.85% of the participants. Model reading provided an understanding that situations of greater vulnerability such as unemployment, low income, and limited or lack of family involvement in care were predictors of a higher frequency of hospitalization. Conclusions: The model suggests that nursing professionals should adopt prevention actions for modifiable factors and authorities should make investments in health promotion for non-modifiable factors. It also enhances the debate about differentiated care to these patients.

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

BACKGROUND

The 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.

METHODS

The 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.

RESULTS

From 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.

CONCLUSIONS

Repeated 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.

Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection

Accurate prognosis of outcome events, such as clinical procedures or disease diagnosis, is central in medicine. The emergence of longitudinal clinical data, like the Electronic Health Records (EHR), represents an opportunity to develop automated methods for predicting patient outcomes. However, these data are highly dimensional and very sparse, complicating the application of predictive modeling techniques. Further, their temporal nature is not fully exploited by current methods, and temporal abstraction was recently used which results in symbolic time intervals representation. We present Maitreya, a framework for the prediction of outcome events that leverages these symbolic time intervals. Using Maitreya, learn predictive models based on the temporal patterns in the clinical records that are prognostic markers and use these markers to train predictive models for eight clinical procedures. In order to decrease the number of patterns that are used as features, we propose the use of three one class feature selection methods. We evaluate the performance of Maitreya under several parameter settings, including the one-class feature selection, and compare our results to that of atemporal approaches. In general, we found that the use of temporal patterns outperformed the atemporal methods, when representing the number of pattern occurrences.