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1.
背景 新生儿出生体质量与个体健康息息相关,低出生体质量是早期新生儿死亡的高危因素,而巨大儿的发生不仅可增加母婴产时并发症发生风险,还可增加个体成年后罹患各种慢性病的风险。因此,寻找新生儿出生体质量的影响因素十分重要。 目的 探讨孕妇肠道菌群对新生儿出生体质量的影响。 方法 以2017年1—9月在广州市妇女儿童医疗中心出生的516例新生儿及其孕母为研究对象,根据新生儿出生体质量将其分为低出生体质量儿组(LW组,n=24)、正常体质量儿组(NW组,n=479)、巨大儿组(OW组,n=13)。采集孕母的肠道菌群参数及临床实验室检测指标,采用QIIME软件进行孕期肠道菌群组成分析和多样性分析;采用LEfSe分析,分别对三组孕妇肠道菌群属水平上的相对丰度进行两两比较,识别组间具有明显差异的菌群;通过线性模型MaAsLin进行多元分析,以捕获各实验室检测指标与微生物属之间的相关性;通过Boruta随机森林分类器模型分别基于实验室检测指标和肠道菌群分类操作单元(OTUs)构建新生儿出生体质量分类预测模型,探究孕妇肠道菌群对新生儿体质量的影响。 结果 三组孕母的肠道菌群组成分析发现,门水平中厚壁菌门(Firmicutes)物种丰富度最高,属水平里普拉梭菌(Faecalibacterium)明显富集,三组间门水平的香农指数和辛普森指数比较,差异无统计学意义(P>0.05)。三组间的LEfSe分析发现:与LW组比较,NW组链球菌(Streptococcus)和罗氏菌(Roseburia)明显富集(P<0.05),而芽孢杆菌(Bacillaceae)、萝卜属菌(Raphanus)、甲烷球形菌(Methanosphaera)、巴氏杆菌(Barnesiellaceae)、普雷沃氏菌(Paraprevotella)丰度明显降低(P<0.05);与NW组比较,OW组属巨单胞菌(Megamonas)、属粪球菌(Coprococcus)、韦荣氏菌(Veillonellaceae)、cc-115、梭菌(Closrtidiaceae)、另枝杆菌(Alistipes)明显富集(P<0.05),而布劳特氏菌(Blautia)和伊格尔兹氏菌(Eggerthella)丰度明显降低(P<0.05);与LW组比较,OW组Closrtidiaceae、Alistipes菌群明显富集(P<0.05),而Barnesiellaceae丰度明显降低(P<0.05)。基于实验室检测指标分类器模型、肠道菌群OTUs分类器模型,区分NW组与LW组的受试者工作特征曲线下面积(AUC)分别为0.62、0.77,区分NW组与OW组的AUC分别为0.65、0.78。 结论 不同出生体质量新生儿对应孕母的肠道菌群存在差异,孕母肠道菌群OTUs模型可区分新生儿出生体质量的大小,孕妇肠道菌群可能是预测新生儿体质量的一个良好指标。  相似文献   
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目的:选择相应的机器学习算法构建二型糖尿病肾病风险预测模型,为疾病的早期预防提供科学依据。方法:基于解放军总医院提供的糖尿病数据集,通过对缺失值、异常值等进行一系列预处理,得到894条二型糖尿病患者数据。利用单因素逻辑回归筛选出24个有效检查指标作为特征,并基于随机森林、BP神经网络、支持向量机分别构建二型糖尿病肾病风险预测模型,同时对其查准率、召回率进行对比,以验证其应用性能。结果:随机森林预测模型的总体性能最优,3种算法的训练效果均较好。结论:二型糖尿病肾病风险预测模型能为疾病早期预防控制提供参考依据。  相似文献   
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目的 探索贵州省男性主要少数民族骨量异常的影响因素,为骨量异常的防控提供参考依据。 方法 基于中国多民族队列,采用多阶段分层整群抽样方法调查贵州省苗族、布依族、侗族男性共5 727名。采用随机森林算法、非条件logistic回归和限制性立方样条回归探讨骨量异常的影响因素。结果 骨量异常重要的前5名的因素依次是职业、午睡时长、年龄、BMI、静态行为。布依族(OR=1.223,95%CI:1.065~1.405)、年龄≥50岁(OR=1.254,95%CI:1.038~1.515)、吸烟(OR=1.191,95%CI:1.060~1.338)、有关节炎(OR=1.259,95%CI:1.001~1.583)和有骨折史(OR=1.528,95%CI:1.227~1.902)可能是骨量异常的危险因素。而农林牧渔劳动者(OR=0.787,95%CI:0.626~0.990)、午睡时长≥90分钟(OR=0.725,95%CI:0.612~0.858)、中水平体力活动(OR=0.818,95%CI:0.708~0.946)和高水平体力活动(OR=0.824,95%CI:0.696~0.975)可能是贵州省主要男性少数民族骨量异常的保护因素。BMI与骨量异常的患病关联强度呈非线性关系,业余静态行为时长与骨量异常的患病关联强度呈线性关系。结论 影响贵州省男性主要少数民族骨量异常的因素包括多方面,包括非可控因素和可控因素,建议加强对可控因素的管理以预防骨量异常的发生。  相似文献   
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目的:建立随机森林模型预测急性心肌梗死(acute myocardial infarction,AMI)患者并发急性肾损伤(acute kidney injury, AKI)。方法:使用温州医科大学附属东阳医院大数据平台,筛选出1 363例患AMI的病例,确定30个变量后,统计分析样本临床特点,将样本划分为75%的训练集建立随机森林模型,以及25%的测试集进行验证,使用R语言进行数据的筛选及模型的建立。最后根据特异性、敏感性、准确性、受试者特征工作特征曲线(relative operating characteristic curve, ROC曲线)等来评估模型性能,同时与其他三种常用的机器学习算法(神经网络,朴素贝叶斯,支持向量机)的模型性能进行比较。结果:AMI合并AKI的患者的人口学信息、心血管疾病的危险因素、入院时的生命体征、实验室检查等与未合并急性肾损伤的患者存在差异性。模型评估后得出测试集的ROC曲线下面积为0.893,特异度为0.791,灵敏度为0.866,其中入院首次肌酐、首次尿素、D-二聚体、年龄、机械通气是其最重要的影响因素。在本研究中,多种机器学习算法比较后,随机森林模型较有优势。结论:建立的随机森林模型具有帮助预测AMI并发AKI的潜力。  相似文献   
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Objective Alzheimer’s disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient’s severity of neurodegeneration independent from the patient’s clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI). Methods We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer’s which is a commonly used segmentation software. Results Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer’s (6–8 hours). Conclusion Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.  相似文献   
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ObjectiveTo create a sleep duration classification technique for waist-worn ActiGraph accelerometers in preschool-aged children.MethodsChildren wore ActiGraph wGT3X-BT accelerometers on their right hip for 7 days (24 h/day). Ground truth nap, sleep, and wake were estimated through visual inspection of accelerometer data, guided by sleep log-sheets and previously published visual inspection heuristics. Raw accelerometer data (30Hz) were used to generate 144 features aggregated to 1-min epochs. Machine learning classification (ie, Random Forest and Hidden Markov Modeling [HMM]) predicted nap, sleep, and wake. A simplified prediction formula was also created using features (n = 10) with the highest mean decrease in Gini index during training of Random Forests, and temporally smoothed with rolling median calculations.ResultsChildren (n = 89, mean age = 4.5 years, 67% boys) contributed >600,000 min of accelerometer data. Overall classification accuracy of the Random Forest and HMM classifier was 96.2% (95%CI: 96.1, 96.2%), with a Kappa score of 0.93. Additionally, overall classification accuracy for the temporally smoothed simplified formula was 93.7% (95%CI: 93.6, 93.7%) with Kappa = 0.87. Nap prediction accuracy was 99.8% for the final machine learning model, and 86.1% for the simplified formula. For participant-level daily summaries, generally small but statistically significant differences were found between machine learning and ground truth behaviour predictions, whereas non-significant differences were found between the simplified formulas and ground truth predictions.ConclusionsPredictions for both machine learning and the simplified formula had almost perfect agreement with visual inspection ground truth measurements. Future research is needed to confirm these findings using polysomnography ground truth sleep measurements.  相似文献   
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PurposeTraumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI.MethodsA retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets.ResultsThere were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2–179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78.ConclusionThe ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.  相似文献   
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目的:建立一种快速、合理,且识别率高的新型冠状病毒感染肺炎的辅助诊断模型。方法:来自8个医疗机构的30例确诊病例的血清样本检测血常规指标,选取被排除COVID-19的其他患者和健康体检者的血清样本作为对照组,采用随机森林(random forest)方法建立识别模型,最终选取了8个重要指标,模型总准确率86.57%,对阳性样本的预测正确率(即敏感性)可达91.67%,使用内部、外部交互检验方法分别对模型进行了验证,结果证明了所选模型的稳定性和可靠性。结论:本工作提出了一种快速、经济、低人工成本且方便的COVID-19预诊断工具,有助于临床医生提供有价值的诊断信息。  相似文献   
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PurposeMachine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.MethodsThe hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.ResultsThe appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.ConclusionsA subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.  相似文献   
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