Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China,especially in urban areas.Early prevention strategies are needed to reduce the associated mortality and morbidity.We applied the combinati...Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China,especially in urban areas.Early prevention strategies are needed to reduce the associated mortality and morbidity.We applied the combination of rules and different machine learning techniques to assess the risk of development of T2DM in an urban Chinese adult population.A retrospective analysis was performed on 8000 people with non-diabetes and 3845 people with T2DM in Nanjing.Multilayer Perceptron (MLP),AdaBoost (AD),Trees Random Forest (TRF),Support Vector Machine (SVM),and Gradient Tree Boosting (GTB) machine learning techniques with 10 cross validation methods were used with the proposed model for the prediction of the risk of development of T2DM.The performance of these models was evaluated with accuracy,precision,sensitivity,specificity,and area under receiver operating characteristic (ROC) curve (AUC).After comparison,the prediction accuracy of the different five machine models was 0.87,0.86,0.86,0.86 and 0.86 respectively.The combination model using the same voting weight of each component was built on T2DM,which was performed better than individual models.The findings indicate that,combining machine learning models could provide an accurate assessment model for T2DM risk prediction.展开更多
Background： Q fever endocarditis, a chronic illness caused by Coxiella burnetii, can be fatal ifmisdiagnosed or left untreated. Despite a relatively high positive rate of Q fever serology in healthy individuals in th...Background： Q fever endocarditis, a chronic illness caused by Coxiella burnetii, can be fatal ifmisdiagnosed or left untreated. Despite a relatively high positive rate of Q fever serology in healthy individuals in the mainland of China, very few cases of Q fever endocarditis have been reported. This study summarized cases of Q fever endocarditis among blood culture negative endocarditis （BCNE） patients and discussed factors attributing to the low diagnostic rate. Methods： We identified confirmed cases of Q fever endocarditis among 637 consecutive patients with infective endocarditis （IE） in the Peking Union Medical College Hospital between 2006 and 2016. The clinical findings for each confirmed case were recorded. BCNE patients were also examined and each BCNE patient＇s Q fever risk factors were identified. The risk factors and presence of Q fever serologic testing between BCNE patients suspected and unsuspected of Q fever were compared using the Chi-squared or Chi-squared with Yates＇ correction for continuity. Results： Among the IE patients examined, there were 147 BCNE patients, of whom only 11 patients （7.5%） were suspected of Q fever and undergone serological testing for C. burnetii. Six out of 11 suspected cases were diagnosed as Q fever endocarditis. For the remaining 136 BCNE patients, none of them was suspected of Q fever nor underwent relevant testing. Risk factors for Q fever endocarditis were comparable between suspected and unsuspected patients, with the most common risk factors being valvulopathy in both groups. However, significantly more patients had consulted the Infectious Diseases Division and undergone comprehensive diagnostic tests in the suspected group than the unsuspected group （100% vs. 63%, P = 0.03）. Conclusions： Q fever endocarditis is a serious yet treatable condition. Lacking awareness of the disease may prevent BCNE patients from being identified, despite having Q fever risk factors. Increasing awareness and guideline adherence are crucial in avoiding misdiagnosing and missed diagnosing of the disease.展开更多
基金the National Natural Science Foundation of China(Nos.52022054,51974181,52004157)the Shanghai Rising-Star Program,China(No.19QA1403600)+4 种基金the Shanghai Sailing Program,China(No.21YF1412900)and the Iron and Steel Joint Research Fund of National Natural Science Foundation of China and China Baowu Steel Group Corporation Limited(No.U1860203)the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning,China(No.TP2019041)the Shanghai Postdoctoral Excellence Program,China(No.2021160)the“Shuguang Program”supported by the Shanghai Education Development Foundation and the Shanghai Municipal Education Commission,China(No.21SG42).
基金financial supports from the National Natural Science Foundation of China(Nos.52004157,U1860203,52022054,51974181)the Shanghai Sailing Program,China(No.21YF1412900)+5 种基金the Shanghai Rising-Star Program,China(No.19QA1403600)the Shanghai Engineering Research Center of Green Remanufacture of Metal Parts,China(No.19DZ2252900)the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning,China(No.TP2019041)the“Shuguang Program”supported by the Shanghai Education Development Foundation and the Shanghai Municipal Education Commission,China(No.21SG42)the Independent Research and Development Project of State Key Laboratory of Advanced Special Steel,Shanghai Key Laboratory of Advanced Ferrometallurgy,Shanghai University,China(No.SKLASS 2020-Z10)the Science and Technology Commission of Shanghai Municipality,China(No.19DZ2270200).
基金This work was supported by grants from the National Natural Science Foundation of China (No.81570737, No.81370947, No.81570736, No.81770819, No.81500612, No.81400832, No.81600637, No.81600632, and No.81703294)the National Key Research and Development Program of China (No.2016YFC1304804 and No.2017YFC1309605)+4 种基金the Jiangsu Provincial Key Medical Discipline (No.ZDXKB2016012)the Key Project of Nanjing Clinical Medical Sciencethe Key Research and Development Program of Jiangsu Province of China (No.BE2015604 and No.BE2016606)the Jiangsu Provincial Medical Talent (No.ZDRCA2016062)the Nanjing Science and Technology Development Project (No.201605019).
文摘Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China,especially in urban areas.Early prevention strategies are needed to reduce the associated mortality and morbidity.We applied the combination of rules and different machine learning techniques to assess the risk of development of T2DM in an urban Chinese adult population.A retrospective analysis was performed on 8000 people with non-diabetes and 3845 people with T2DM in Nanjing.Multilayer Perceptron (MLP),AdaBoost (AD),Trees Random Forest (TRF),Support Vector Machine (SVM),and Gradient Tree Boosting (GTB) machine learning techniques with 10 cross validation methods were used with the proposed model for the prediction of the risk of development of T2DM.The performance of these models was evaluated with accuracy,precision,sensitivity,specificity,and area under receiver operating characteristic (ROC) curve (AUC).After comparison,the prediction accuracy of the different five machine models was 0.87,0.86,0.86,0.86 and 0.86 respectively.The combination model using the same voting weight of each component was built on T2DM,which was performed better than individual models.The findings indicate that,combining machine learning models could provide an accurate assessment model for T2DM risk prediction.
基金This work was supported by grants from the National Natural Science Foundation of China （No. 81470426）, the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry （SRF for ROCS, and SEM）, Elite Class, and PUMCH Young and Middle-aged Investigation Fund, Key Project （No. PUMCH-2016-1.12）.
文摘Background： Q fever endocarditis, a chronic illness caused by Coxiella burnetii, can be fatal ifmisdiagnosed or left untreated. Despite a relatively high positive rate of Q fever serology in healthy individuals in the mainland of China, very few cases of Q fever endocarditis have been reported. This study summarized cases of Q fever endocarditis among blood culture negative endocarditis （BCNE） patients and discussed factors attributing to the low diagnostic rate. Methods： We identified confirmed cases of Q fever endocarditis among 637 consecutive patients with infective endocarditis （IE） in the Peking Union Medical College Hospital between 2006 and 2016. The clinical findings for each confirmed case were recorded. BCNE patients were also examined and each BCNE patient＇s Q fever risk factors were identified. The risk factors and presence of Q fever serologic testing between BCNE patients suspected and unsuspected of Q fever were compared using the Chi-squared or Chi-squared with Yates＇ correction for continuity. Results： Among the IE patients examined, there were 147 BCNE patients, of whom only 11 patients （7.5%） were suspected of Q fever and undergone serological testing for C. burnetii. Six out of 11 suspected cases were diagnosed as Q fever endocarditis. For the remaining 136 BCNE patients, none of them was suspected of Q fever nor underwent relevant testing. Risk factors for Q fever endocarditis were comparable between suspected and unsuspected patients, with the most common risk factors being valvulopathy in both groups. However, significantly more patients had consulted the Infectious Diseases Division and undergone comprehensive diagnostic tests in the suspected group than the unsuspected group （100% vs. 63%, P = 0.03）. Conclusions： Q fever endocarditis is a serious yet treatable condition. Lacking awareness of the disease may prevent BCNE patients from being identified, despite having Q fever risk factors. Increasing awareness and guideline adherence are crucial in avoiding misdiagnosing and missed diagnosing of the disease.
