Introduction Neurosurgical emergencies such as spontaneous intracerebral hemorrhage(ICH),traumatic brain injury(TBI),and acute brain herniation are among the most time-sensitive and high-stakes conditions in modern me...Introduction Neurosurgical emergencies such as spontaneous intracerebral hemorrhage(ICH),traumatic brain injury(TBI),and acute brain herniation are among the most time-sensitive and high-stakes conditions in modern medicine.Clinical decisions often must be made within minutes,yet these decisions are traditionally guided by limited information,heuristic reasoning,and past experience.In this context,the rise of medical data mining and real-time analytics offers a transformative opportunity:to extract actionable intelligence from the flood of clinical,imaging,and physiological data already being collected,and to use this intelligence to guide care in real time[1–3](Figure 1).展开更多
Background:Spontaneous intracerebral hemorrhage(ICH)is a severe neurological emergency with high morbidity and mortality.The effectiveness of surgical intervention remains controversial,partly due to significant heter...Background:Spontaneous intracerebral hemorrhage(ICH)is a severe neurological emergency with high morbidity and mortality.The effectiveness of surgical intervention remains controversial,partly due to significant heterogeneity among patients.Traditional clinical criteria often fail to identify those most likely to benefit from surgery.Methods:This nationwide retrospective study in China included 2,167 ICH patients from 31 hospitals.Using machine learning techniques,we integrated clinical and radiomic data to perform unsupervised clustering and identify distinct phenogroups.Dimensionality reduction and cross-validation were applied to minimize overfitting.External validation was conducted using data from the INTERACT3 trial,and a prospective cohort was used to assess real-world applicability.Results:Three phenogroups were identified.Among them,only Phenogroup 1-characterized by older age,moderate hematoma volume,and intermediate Glasgow Coma Scale scores-showed significant benefit from early surgical intervention,with a 42%reduction in 3-month mortality and improved functional outcomes.In contrast,surgery did not significantly affect outcomes in Phenogroups 0 and 2.These findings were consistent across multiple machine learning models and validated externally.Conclusion:Machine learning-driven phenotypic stratification can effectively identify ICH patients who are most likely to benefit from surgical treatment.This approach supports personalized treatment strategies and may improve clinical decision-making in ICH management.Further validation in diverse populations is warranted.展开更多
文摘Introduction Neurosurgical emergencies such as spontaneous intracerebral hemorrhage(ICH),traumatic brain injury(TBI),and acute brain herniation are among the most time-sensitive and high-stakes conditions in modern medicine.Clinical decisions often must be made within minutes,yet these decisions are traditionally guided by limited information,heuristic reasoning,and past experience.In this context,the rise of medical data mining and real-time analytics offers a transformative opportunity:to extract actionable intelligence from the flood of clinical,imaging,and physiological data already being collected,and to use this intelligence to guide care in real time[1–3](Figure 1).
基金supported by the Shanghai Municipal Health Commission(201840063,201801075)the Science and Technology Commission of Shanghai Municipality(18441903300).
文摘Background:Spontaneous intracerebral hemorrhage(ICH)is a severe neurological emergency with high morbidity and mortality.The effectiveness of surgical intervention remains controversial,partly due to significant heterogeneity among patients.Traditional clinical criteria often fail to identify those most likely to benefit from surgery.Methods:This nationwide retrospective study in China included 2,167 ICH patients from 31 hospitals.Using machine learning techniques,we integrated clinical and radiomic data to perform unsupervised clustering and identify distinct phenogroups.Dimensionality reduction and cross-validation were applied to minimize overfitting.External validation was conducted using data from the INTERACT3 trial,and a prospective cohort was used to assess real-world applicability.Results:Three phenogroups were identified.Among them,only Phenogroup 1-characterized by older age,moderate hematoma volume,and intermediate Glasgow Coma Scale scores-showed significant benefit from early surgical intervention,with a 42%reduction in 3-month mortality and improved functional outcomes.In contrast,surgery did not significantly affect outcomes in Phenogroups 0 and 2.These findings were consistent across multiple machine learning models and validated externally.Conclusion:Machine learning-driven phenotypic stratification can effectively identify ICH patients who are most likely to benefit from surgical treatment.This approach supports personalized treatment strategies and may improve clinical decision-making in ICH management.Further validation in diverse populations is warranted.
