OBJECTIVE: To assess the short-term lag effects of climate and air pollution on hospital admissions for cardiovascular and respiratory diseases, and to develop deep learning-based models for daily hospital admission prediction. METHODS: A multi-city study was conducted in Tokyo's 23 wards, Osaka City, and Nagoya City. Random forest models were employed to assess the synergistic short-term lag effects (lag0, lag3, and lag7) of climate and air pollutants on hospitalization for five cardiovascular diseases (CVDs) and two respiratory diseases (RDs). Furthermore, we developed hybrid deep learning models that integrated an autoencoder (AE) with a Long Short-Term Memory network (AE+LSTM) to predict daily hospital admissions. RESULTS: On the day of exposure (lag0), air pollutants, particularly nitrogen oxides (NO x), exhibited the strongest influence on hospital admissions for CVD and RD, with pronounced effects observed for hypertension (I10-I15), ischemic heart disease (I20), arterial and capillary diseases (I70-I79), and lower respiratory infections (J20-J22 and J40-J47). At longer lags (lag3 and lag7), temperature and precipitation were more influential predictors. The AE+LSTM model outperformed the standard LSTM, improving the prediction accuracy by 32.4% for RD in Osaka and 20.94% for CVD in Nagoya. CONCLUSION Our findings reveal the dynamic, time-varying health risks associated with environmental exposure and demonstrate the utility of deep learnings in predicting short-term hospital admissions. This framework can inform early warning systems, enhance healthcare resource allocation, and support climate-adaptive public health strategies.
Humans ; Hospitalization/statistics & numerical data* ; Cardiovascular Diseases/epidemiology* ; Japan/epidemiology* ; Air Pollutants/analysis* ; Air Pollution/adverse effects* ; Cities/epidemiology* ; Climate ; Respiratory Tract Diseases/epidemiology* ; Deep Learning ; Male

BACKGROUND/AIMS: Gastrointestinal stromal tumor (GIST) is one of the most common types of submucosal tumors (SMTs). Because of GIST's malignant potential, it is crucial to differentiate it from other SMTs. The present study aimed to identify characteristic endoscopic findings of GISTs in the small intestine. METHODS: We reviewed the clinicopathological and endoscopic findings of 38 patients with endoscopically or surgically resected SMTs in the small intestine. SMTs were classified into GIST and non-GIST groups, and clinicopathological and endoscopic findings were compared between the 2 groups. RESULTS: Fifteen patients had GIST and 23 patients had other types of SMTs in the small intestine. Comparison of the endoscopic findings between the 2 groups revealed that dilated vessels in the surrounding mucosa were significantly more in number in the GIST group than in the non-GIST group (P<0.05). However, there were no other differences in endoscopic findings between the 2 groups. Among patients with GISTs, the presence of dilated vessels in the surrounding mucosa was not associated with bleeding risk, tumor size, or metastasis rate at diagnosis. CONCLUSIONS: Dilated vessels in the surrounding mucosa, identified during balloon-assisted endoscopy, may be a diagnostic indicator for GIST in the small intestine. However, its clinical significance should be further analyzed.
Diagnosis ; Endoscopy ; Gastrointestinal Stromal Tumors ; Hemorrhage ; Humans ; Intestine, Small ; Mucous Membrane ; Neoplasm Metastasis