Search results for "Injury data"
showing 2 items of 2 documents
A PCA Interpretation of the Glasgow Coma Scale in the Trauma Brain Injury PECARN Dataset
2018
CT scan is strongly recommended for a patient affected by head trauma, but he/she must absorb a certain amount of radiations. For this reason, the physician tries to avoid such a practice for pediatric patients. The symptoms analysis, visual/tactile inspection, and reactions to appropriate stimuli from the physician could induce him/her to put the patient in a period of observation instead of performing an immediate CT scan. As a consequence, the correct evaluation of those symptoms is a crucial task. For this reason, the Pediatric Glasgow Coma Scale (PGCS) plays a fundamental role, because it is a numeric scale regarding the patient’s mental status. It is computed as the sum of the score f…
Comparison of feature importance measures as explanations for classification models
2021
AbstractExplainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature importance. However, there are several different approaches how feature importances are being measured, most notably global and local. In this study we compare different feature importance measures using both linear (logistic regression with L1 penalization) and non-linear (random forest) methods and local interpretable model-agnostic explanations on top of them. These methods are applied to two datasets from the medical domain, the openly available breast cancer …