Name
Title | ||
|---|---|---|
Comparison of Multi-class Machine Learning Methods for the Identification of Factors Most Predictive of Prognosis in Neurobehavioral assessment of Pediatric Severe Disorder of Consciousness through LOCFAS scale |
Abstract | ||
|---|---|---|
Severe Disorders of Consciousness (DoC) are generally caused by brain trauma, anoxia or stroke, and result in conditions ranging from coma to the confused-agitated state. Prognostic decision is difficult to achieve during the first year after injury, especially in the pediatric cases. Nevertheless, prognosis crucially informs rehabilitation decision and family expectations. We compared four multi-class machine learning classification approaches for the prognostic decision in pediatric DoC. We identified domains of a neurobehavioral assessment tool, Level of Cognitive Functioning Assessment Scale, mostly contributing to decision in a cohort of 124 cases. We showed the possibility to generalize to new admitted pediatric cases, thus paving the way for real employment of machine learning classifiers as an assistive tool to prognostic decision in clinics. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8856880 | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Child,Coma,Consciousness,Consciousness Disorders,Humans,Machine Learning,Prognosis | Rehabilitation,Computer science,Coma,Stroke,Consciousness,Cognitive skill,Severe disorder,Artificial intelligence,Cohort,Machine learning | Conference |
Volume | ISSN | ISBN |
2019 | 1557-170X | 978-1-5386-1312-2 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erika Molteni | 1 | 12 | 2.05 |
| Katia Colombo | 2 | 0 | 0.34 |
| Elena Beretta | 3 | 15 | 6.06 |
| Susanna Galbiati | 4 | 0 | 0.34 |
| Liane Dos Santos Canas | 5 | 0 | 0.34 |
| Marc Modat | 6 | 898 | 72.33 |
| Sandra Strazzer | 7 | 2 | 1.54 |