Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD.

Background: With the continuous increase of Alzheimer's disease (AD), it is also imminent to treat patients with AD for medication reconciliation. Objective: To explore the role and value of medication reconciliation in AD treatment. Methods: 100 patients over 65 years of age diagnosed with AD were randomly separated into two groups: conventional treatment and medication reforming. The list of medical orders of all subjects was obtained within 24 hours after admission with Beers criteria, STOPP/START criteria, and Chinese Pharmacopoeia used as the MED intervention criteria. Medication reconciliation was performed at 2 weeks, 1 month, and 2 months after hospital admission. The number of medications prescribed, the quantity of the medication, medication error rate, therapeutic effect, adverse drug reactions, and satisfaction levels of family members and main caregivers were compared between the two groups. Results: After the intervention, the types and amount of medication in the MED group were less compared to the CON group along with a reduced medication deviation rate. The Mini-mental state examination (MMSE) score and the proportion of well-nourished patients in the MED group were higher than those in the CON group. It was also observed that the physical self-care ability score and the proportion of patients with abnormal swallowing were lower when in comparison with the CON group. The incidence of adverse drug reactions in the MED group was lower than that in the CON group. However, the satisfaction rate was higher than that in the CON group. Conclusion: Medication reconciliation can reduce the medication deviation in AD patients.