Realizing Specific Weather Forecast through Machine Learning Enabled Prediction Model

2019 ◽  
Author(s):  
I-Ching Chen ◽  
Shueh-Cheng Hu
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Weather Forecast

scholarly journals A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4606
Author(s):  
Sunguk Hong ◽  
Cheoljeong Park ◽  
Seongjin Cho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Thermal Environment ◽  
Management Plan ◽  
Temperature Prediction ◽  
Railway Systems ◽  
Train Speed ◽  
Speed Restrictions ◽  
Entire Network

Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

Constructing a prediction model for difficult intubation of obese patients based on machine learning

2021 ◽  
Vol 72 ◽  
pp. 110278
Author(s):  
Cheng-Mao Zhou ◽  
Qiong Xue ◽  
Hao-Tian Ye ◽  
Ying Wang ◽  
Jianhua Tong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Difficult Intubation ◽  
Obese Patients

scholarly journals Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Miyoshi ◽  
Tsubasa Maeda ◽  
Katsuyoshi Matsuoka ◽  
Daisuke Saito ◽  
Sawako Miyoshi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ulcerative Colitis ◽  
Prediction Model ◽  
Clinical Features ◽  
Unmet Need ◽  
Patient Treatment ◽  
Clinical Activity ◽  
Prediction Tool ◽  
Real World Data ◽  
Predictive Values

AbstractPredicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (VDZ) before administration is an unmet need for optimizing individual patient treatment. We hypothesized that the machine-learning approach with daily clinical information can be a new, promising strategy for developing a drug-efficacy prediction tool. Random forest with grid search and cross-validation was employed in Cohort 1 to determine the contribution of clinical features at baseline (week 0) to steroid-free clinical remission (SFCR) with VDZ at week 22. Among 49 clinical features including sex, age, height, body weight, BMI, disease duration/phenotype, treatment history, clinical activity, endoscopic activity, and blood test items, the top eight features (partial Mayo score, MCH, BMI, BUN, concomitant use of AZA, lymphocyte fraction, height, and CRP) were selected for logistic regression to develop a prediction model for SFCR at week 22. In the validation using the external Cohort 2, the positive and negative predictive values of the prediction model were 54.5% and 92.3%, respectively. The prediction tool appeared useful for identifying patients with UC who would not achieve SFCR at week 22 during VDZ therapy. This study provides a proof-of-concept that machine learning using real-world data could permit personalized treatment for UC.

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