目的探讨蒸汽热敷贴联合盆底肌生物反馈训练治疗老年女性压力性尿失禁的效果,以便为临床提供一种新的有效治疗方法。方法选择上海市某三级甲等综合医院2017年9月至2018年9月就诊于妇科、泌尿外科、老年科门诊的60例老年女性患者为研究...目的探讨蒸汽热敷贴联合盆底肌生物反馈训练治疗老年女性压力性尿失禁的效果,以便为临床提供一种新的有效治疗方法。方法选择上海市某三级甲等综合医院2017年9月至2018年9月就诊于妇科、泌尿外科、老年科门诊的60例老年女性患者为研究对象。按简单随机数字表法将患者分为对照组与试验组,每组分别30例。对照组患者采用单纯盆底肌生物反馈训练治疗,试验组采用盆底肌生物反馈训练联合蒸汽热敷贴治疗。治疗3个月后比较两组患者尿失禁严重程度、1h尿垫试验漏尿量、国际尿失禁咨询委员会尿失禁问卷表简表(International Consultation on Incontinence questionnaire-short form,ICI-Q-SF)评分及尿失禁生活质量量表(incontinence quality of life instrument,I-QOL)评分。结果干预后,两组患者尿失禁严重程度、1h尿垫试验漏尿量、ICI-Q-SF评分及I-QOL评分比较,差异具有统计学意义,试验组患者各项指标均优于对照组(均P<0.05)。结论采用蒸汽热敷贴联合盆底肌生物反馈训练可有效改善老年女性压力性尿失禁的症状及生活质量,值得临床推广应用。展开更多
In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation...In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation, the forecasters obtain the final results by combining the observations with the NWP results and giving opinions based on their experience. It is obvious that using a suitable post-processing algorithm for simulating weather consultation is an interesting and important topic. MOML is a post-processing method based on machine learning, which matches NWP forecasts against observations through a regression function. By adopting different feature engineering of datasets and training periods, the observational and model data can be processed into the corresponding training set and test set. The MOML regression function uses an existing machine learning algorithm with the processed dataset to revise the output of NWP models combined with the observations, so as to improve the results of weather forecasts. To test the new approach for grid temperature forecasts, the 2-m surface air temperature in the Beijing area from the ECMWF model is used. MOML with different feature engineering is compared against the ECMWF model and modified model output statistics (MOS) method. MOML shows a better numerical performance than the ECMWF model and MOS, especially for winter. The results of MOML with a linear algorithm, running training period, and dataset using spatial interpolation ideas, are better than others when the forecast time is within a few days. The results of MOML with the Random Forest algorithm, year-round training period, and dataset containing surrounding gridpoint information, are better when the forecast time is longer.展开更多
文摘目的探讨蒸汽热敷贴联合盆底肌生物反馈训练治疗老年女性压力性尿失禁的效果,以便为临床提供一种新的有效治疗方法。方法选择上海市某三级甲等综合医院2017年9月至2018年9月就诊于妇科、泌尿外科、老年科门诊的60例老年女性患者为研究对象。按简单随机数字表法将患者分为对照组与试验组,每组分别30例。对照组患者采用单纯盆底肌生物反馈训练治疗,试验组采用盆底肌生物反馈训练联合蒸汽热敷贴治疗。治疗3个月后比较两组患者尿失禁严重程度、1h尿垫试验漏尿量、国际尿失禁咨询委员会尿失禁问卷表简表(International Consultation on Incontinence questionnaire-short form,ICI-Q-SF)评分及尿失禁生活质量量表(incontinence quality of life instrument,I-QOL)评分。结果干预后,两组患者尿失禁严重程度、1h尿垫试验漏尿量、ICI-Q-SF评分及I-QOL评分比较,差异具有统计学意义,试验组患者各项指标均优于对照组(均P<0.05)。结论采用蒸汽热敷贴联合盆底肌生物反馈训练可有效改善老年女性压力性尿失禁的症状及生活质量,值得临床推广应用。
基金supported by the National Key Research and Development Program of China (Grant Nos. 2018YFF0300104 and 2017YFC0209804)the National Natural Science Foundation of China (Grant No. 11421101)Beijing Academy of Artifical Intelligence (BAAI)
文摘In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation, the forecasters obtain the final results by combining the observations with the NWP results and giving opinions based on their experience. It is obvious that using a suitable post-processing algorithm for simulating weather consultation is an interesting and important topic. MOML is a post-processing method based on machine learning, which matches NWP forecasts against observations through a regression function. By adopting different feature engineering of datasets and training periods, the observational and model data can be processed into the corresponding training set and test set. The MOML regression function uses an existing machine learning algorithm with the processed dataset to revise the output of NWP models combined with the observations, so as to improve the results of weather forecasts. To test the new approach for grid temperature forecasts, the 2-m surface air temperature in the Beijing area from the ECMWF model is used. MOML with different feature engineering is compared against the ECMWF model and modified model output statistics (MOS) method. MOML shows a better numerical performance than the ECMWF model and MOS, especially for winter. The results of MOML with a linear algorithm, running training period, and dataset using spatial interpolation ideas, are better than others when the forecast time is within a few days. The results of MOML with the Random Forest algorithm, year-round training period, and dataset containing surrounding gridpoint information, are better when the forecast time is longer.
