This review focuses on machine learning and on how methods and models combining data
analytics and artificial intelligence have been applied to laboratory medicine so far. Although …

Background: The prediction of neurologic outcome is a fundamental concern in the
resuscitation of patients with severe brain injury. Objective: To provide an evidence-based …

Objectives As laboratory medicine continues to undergo digitalization and automation,
clinical laboratorians will likely be confronted with the challenges associated with artificial …

Background Identifying patients who may progress to a poor clinical outcome will encourage
earlier appropriate therapeutic interventions. Brain edema may contribute to secondary …

Background Serum albumin and prealbumin are both negative acute-phase reactants, and
usually at low levels in stress. We aim to determine their predictive values for poor outcome …

Purpose With the in-depth application of machine learning (ML) in clinical practice, it has
been used to predict the mortality risk in patients with traumatic brain injuries (TBI). However …

Objectives Although laboratory parameters have long been recognized as indicators of
outcome of traumatic brain injury (TBI), it remains a challenge to predict the recovery of …

Previous studies have identified some clinical parameters for predicting long-term functional
recovery and mortality after traumatic brain injury (TBI). Here, data mining methods were …

Comparison of admission serum albumin and hemoglobin as pred... : Medicine Comparison of
admission serum albumin and hemoglobin as predictors of outcome in children with moderate …

As the digital health care transformation unfolds, artificial intelligence (AI), algorithms, and
clinical decision support are becoming a more accepted and integrated part of the health …