Publications by authors named "Zitian Duan"
Article Synopsis
- The study aims to create a predictive model using machine learning to foresee aggressive behaviors in hospitalized schizophrenia patients, which can help in prevention efforts.
- Researchers conducted a survey of 2,037 patients, categorizing them into aggressive and non-aggressive groups, and utilized various questionnaires to gather data for model building.
- Among the tested machine learning algorithms, the Random Forest model showed the highest predictive accuracy, indicating its potential as a clinical tool for identifying risks of aggression in these patients.
Intracerebral hemorrhage (ICH) is the second most common type of stroke and has one of the highest fatality rates of any disease. There are many clinical signs and symptoms after ICH due to brain cell injury and network disruption resulted from the rupture of a tiny artery and activation of inflammatory cells, such as motor dysfunction, sensory impairment, cognitive impairment, and emotional disturbance, etc. Thus, researchers have established many tests to evaluate behavioral changes in rodent ICH models, in order to achieve a better understanding and thus improvements in the prognosis for the clinical treatment of stroke.
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