National Cheng Kung University

Systemic sclerosis (SSc) is a chronic autoimmune disease characterized by fibrosis and immune dysregulation, often involving pulmonary and cardiovascular systems. Limited data exist regarding its impact on outcomes among patients hospitalized with influenza.

Using the US Nationwide Inpatient Sample (2016-2020), we identified adults hospitalized with a principal diagnosis of influenza using ICD-10 codes. Patients were categorized into SSc and non-SSc groups. Propensity score matching (1:4) was applied to balance baseline characteristics. Logistic and linear regression analyses estimated adjusted odds ratios (aORs) or coefficients with 95% confidence intervals (CIs) for in-hospital mortality, complications, length of stay (LOS), and adverse discharge outcomes.

After matching, 1,265 patients (253 with SSc, 1,012 without SSc) were included, representing 6,248 hospitalizations nationwide after weighting. Compared with non-SSc patients, those with SSc had significantly higher risks of secondary bacterial or fungal infections (aOR = 1.42; 95% CI: 1.05-1.92; p = 0.025) and bronchiectasis (aOR = 3.91; 95% CI: 1.77-8.65; p < 0.001). Among patients aged ≥60 years, SSc was associated with increased risks of bronchiectasis (aOR = 4.67; 95% CI: 2.13-10.24) and respiratory failure requiring mechanical ventilation (aOR = 1.47; 95% CI: 1.06-2.02). Smokers with SSc had a higher risk of shock, while non-smokers with SSc remained at elevated risk for secondary infections, bronchiectasis, and respiratory failure.

SSc was associated with excess risks of severe in-hospital complications among influenza patients, particularly in older adults and smokers, underscoring the need for targeted prevention and early intervention strategies.

Depression is a major global health concern, still individuals with depressive tendencies remain undetected in outpatient settings due to the limitations of self-report, stigma, and reporting bias. Advances in artificial intelligence (AI) offer opportunities for objective, scalable, and unobtrusive methods of early detection. This study examined predictive accuracy of two AI-driven systems, the iSeeME facial-expression model and the Event-Driven Depression Tendency Warning System (EDDTW-V2) in identifying depressive symptoms among high-risk outpatients in Taiwan.

A cross-sectional study was conducted with 62 outpatients recruited from psychiatric and surgical-oncology clinics at a medical center in southern Taiwan. Participants completed standardized depression assessments, including the Hamilton Depression Rating Scale (HDRS), Beck Depression Inventory-II (BDI-II), and Patient Health Questionnaire-9 (PHQ-9). Facial-expression data were analyzed using iSeeME, while narrative transcripts were processed with EDDTW-V2. Predictive validity was assessed through accurate metrics, correlations with clinical scales, and k-means cluster analysis.

Of the 62 participants, 39 were clinically depressed (HDRS ≥7). The iSeeME system achieved precision of 0.761, recall of 0.854, F1-score of 0.805, and accuracy of 0.770, showing stronger correlations with BDI-II (r = 0.442, p < 0.01) and PHQ-9 (r = 0.335, p < 0.01) than EDDTW-V2. Cluster analysis revealed a high-distress subgroup characterized by younger age, single status, fewer children, and predominance of psychiatric patients.

Both AI systems showed potential as complementary tools to traditional assessments. iSeeME was more sensitive to affective-behavioral symptoms, while EDDTW-V2 captured cognitive-linguistic features. They support earlier detection, monitoring, and targeted interventions in outpatient settings.

Motorcycle crashes remain a major global safety concern, particularly in many Asian countries where motorcycles are the primary mode of transportation. While previous studies have identified factors associated with motorcycle crashes using traditional variable selection methods, they often overlook spatial patterns and contextual constraints (such as motorcycles being prohibited from freeways), leading to lower model accuracy. To address this limitation, we introduce a novel variable selection framework, designated as Spatial Random Forest with Network Distance and Barriers (spatialRF-NDBAR), which incorporates network distance and road barrier constraints to improve spatial variable selection. The study focuses on the Central Business District of Taipei City, where motorcycle access to certain roads is explicitly prohibited by road barriers. We utilize motorcycle crash data from 2016 to 2020, combined with Point of Interest (POI) data extracted from OpenStreetMap. We compare the performance of the proposed framework with that of the conventional spatialRF and spatialRF-ND (network distance constraint only) methods using both a traditional Ordinary Least Squares model and a Multiscale Geographically Weighted Regression (MGWR) model. Among the evaluated models, MGWR using the variables selected by spatialRF-NDBAR shows the best performance, yielding the lowest AICc (1801.212) and highest R² (0.867). Overall, our results show that incorporating spatial predictors reduces residual spatial autocorrelation, improving model performance. Furthermore, POIs such as marketplaces, restaurants, bars, hotels, banks, bus stops, and metro stations are associated with a higher crash risk. These findings provide valuable insights for local governments and support targeted traffic enforcement efforts around high-risk POIs to improve motorcycle safety.