Interrelationships Between Inflammatory Score, Delayed Cerebral Ischemia and Unfavorable Outcome in Patients with aSAH: A Four-Way Decomposition.

Background: To identify biomarkers and develop an inflammatory score based on proper integration to improve risk prediction of delayed cerebral ischemia (DCI) and poor outcome in patients with aneurysmal subarachnoid hemorrhage (aSAH). We also further explore the mediation and interaction of DCI within the chain of events using the four-way effect decomposition.

Methods: Machine learning algorithms are used for biomarker selection and constructed the inflammatory score.

Synthetic minority over-sampling technique-enhanced machine learning models for predicting recurrence of postoperative chronic subdural hematoma.

Objective: Chronic subdural hematoma (CSDH) is a neurological condition with high recurrence rates, primarily observed in the elderly population. Although several risk factors have been identified, predicting CSDH recurrence remains a challenge. Given the potential of machine learning (ML) to extract meaningful insights from complex data sets, our study aims to develop and validate ML models capable of accurately predicting postoperative CSDH recurrence.