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Big data and predictive analytics in neurocritical care

PURPOSE OF REVIEW

To describe predictive data and workflow in the intensive care unit when managing neurologically ill patients.

RECENT FINDINGS

In the era of Big Data in medicine, intensive critical care units are data-rich environments. Neurocritical care adds another layer of data with advanced multimodal monitoring to prevent secondary brain injury from ischemia, tissue hypoxia, and a cascade of ongoing metabolic events. A step closer toward personalized medicine is the application of multimodal monitoring of cerebral hemodynamics, bran oxygenation, brain metabolism, and electrophysiologic indices, all of which have complex and dynamic interactions. These data are acquired and visualized using different tools and monitors facing multiple challenges toward the goal of the optimal decision support system. In this review, we highlight some of the predictive data used to diagnose, treat, and prognosticate the neurologically ill patients. We describe information management in neurocritical care units including data acquisition, wrangling, analysis, and visualization.

Applying Machine Learning to Pediatric Critical Care Data

OBJECTIVES

To explore whether machine learning applied to pediatric critical care data could discover medically pertinent information, we analyzed clinically collected electronic medical record data, after data extraction and preparation, using k-means clustering.

DESIGN

Retrospective analysis of electronic medical record ICU data.

SETTING

Tertiary Children's Hospital PICU.

PATIENTS

Anonymized electronic medical record data from PICU admissions over 10 years.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Data from 11,384 PICU episodes were cleaned, and specific features were generated. A k-means clustering algorithm was applied, and the stability and medical validity of the resulting 10 clusters were determined. The distribution of mortality, length of stay, use of ventilation and pressors, and diagnostic categories among resulting clusters was analyzed. Clusters had significant prognostic information (p < 0.0001). Cluster membership predicted mortality (area under the curve of the receiver operating characteristic = 0.77). Length of stay, the use of inotropes and intubation, and diagnostic categories were nonrandomly distributed among the clusters (p < 0.0001).

CONCLUSIONS

A standard machine learning methodology was able to determine significant medically relevant information from PICU electronic medical record data which included prognosis, diagnosis, and therapy in an unsupervised approach. Further development and application of machine learning to critical care data may provide insights into how critical illness happens to children.

Innovative designs for the smart ICU: Part 3: Advanced ICU informatics

This third and final installment of this series on innovative designs for the smart ICU addresses the steps involved in conceptualizing, actualizing, using, and maintaining the advanced ICU informatics infrastructure and systems. The smart ICU comprehensively and electronically integrates the patient in the ICU with all aspects of care, displays data in a variety of formats, converts data to actionable information, uses data proactively to enhance patient safety, and monitors the ICU environment to facilitate patient care and ICU management. The keys to success in this complex informatics design process include an understanding of advanced informatics concepts, sophisticated planning, installation of a robust infrastructure capable of both connectivity and interoperability, and implementation of middleware solutions that provide value. Although new technologies commonly appear compelling, they are also complicated and challenging to incorporate within existing or evolving hospital informatics systems. Therefore, careful analysis, deliberate testing, and a phased approach to the implementation of innovative technologies are necessary to achieve the multilevel solutions of the smart ICU.