Machine learning-based patient classification system for adult patients in intensive care units: A cross-sectional study.

AIM: This study aimed to develop a patient classification system that stratifies patients admitted to the intensive care unit based on their disease severity and care needs. BACKGROUND: Classifying patients into homogenous groups based on clinical characteristics can optimize nursing care. However, an objective method for determining such groups remains unclear. METHODS: Predictors representing disease severity and nursing workload were considered. Patients were clustered into subgroups with different characteristics based on the results of a clustering algorithm. A patient classification system was developed using a partial least squares regression model. RESULTS: Data of 300 patients were analysed. Cluster analysis identified three subgroups of critically patients with different levels of clinical trajectories. Except for blood potassium levels (p = .29), the subgroups were significantly different according to disease severity and nursing workload. The predicted value ranges of the regression model for Classes A, B and C were <1.44, 1.44-2.03 and >2.03. The model was shown to have good fit and satisfactory prediction efficiency using 200 permutation tests. CONCLUSIONS: Classifying patients based on disease severity and care needs enables the development of tailored nursing management for each subgroup. IMPLICATIONS FOR NURSING MANAGEMENT: The patient classification system can help nurse managers identify homogeneous patient groups and further improve the management of critically ill patients.

The neuroprotection of repetitive transcranial magnetic stimulation pre-treatment in vascular dementia rats.

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive technique that could interfere cortical excitability though brief electric currents induced by alternating magnetic fields from the inductive coil. Currently, it has been applied in many fields of basic and clinical neuro-research. The aims of the present study are to investigate the effect of rTMS pre-treatment on cognitive function in vascular dementia (VaD) rats and further explore the molecular mechanism of rTMS neuroprotection on VaD. We found that rTMS pre-treated VaD rats showed significantly better memory and learning abilities in Morris water maze test compared to the untreated group. Moreover, the mRNA and protein expression levels of BDNF, TrkB, and SYN were significantly higher in the rTMS pre-treated group, indicating that rTMS pre-treatment has neuroprotective effect for VaD, which may have resulted from the increased level of BDNF, TrkB, and SYN in the hippocampal CA1 area.