Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy

Objective: To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy.Methods: This study identified indicators related to GDM through a literature review and exper t discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed. Results: We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10％, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10％. The corresponding values for the F2 model were 88.70％, 0.87, and 79.44％. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60％, AUC was 0.58, and the predictive accuracy of positive cases was 15.85％. Conclusions: In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are cer tain requirements for the propor tions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.     

机器学习XGBoost算法在医学领域的应用研究进展

机器学习XGBoost算法于2014年提出,其基于boosting算法展开,在许多数据科学大赛上都显示出了极高的可用性和优异性能。目前基于XGBoost算法构建的分类或回归预测模型已经广泛地运用于医疗保健、金融、教育、制造等领域的数据分析中。在医药学领域中XGBoost已广泛应用于疾病诊断以及疾病发生风险、转归与预后、合理安全用药和药物研发的等方面,并且在这些领域中提供了具有极大可能性的解决方案,有助于提高决策的效率和质量,降低假阳性率。同时,XGBoost算法在处理数据缺失值时,能自动学习分裂方向;在处理大型数据集时,能够模拟非线性效应,具有较高的效率和准确性。     

Informatics and machine learning to define the phenotype

Introduction: For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns.

Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data.

Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.     

Using machine learning to assess covariate balance in matching studies

In order to assess the effectiveness of matching approaches in observational studies, investigators typically present summary statistics for each observed pre‐intervention covariate, with the objective of showing that matching reduces the difference in means (or proportions) between groups to as close to zero as possible. In this paper, we introduce a new approach to distinguish between study groups based on their distributions of the covariates using a machine‐learning algorithm called optimal discriminant analysis (ODA). Assessing covariate balance using ODA as compared with the conventional method has several key advantages: the ability to ascertain how individuals self‐select based on optimal (maximum‐accuracy) cut‐points on the covariates; the application to any variable metric and number of groups; its insensitivity to skewed data or outliers; and the use of accuracy measures that can be widely applied to all analyses. Moreover, ODA accepts analytic weights, thereby extending the assessment of covariate balance to any study design where weights are used for covariate adjustment. By comparing the two approaches using empirical data, we are able to demonstrate that using measures of classification accuracy as balance diagnostics produces highly consistent results to those obtained via the conventional approach (in our matched‐pairs example, ODA revealed a weak statistically significant relationship not detected by the conventional approach). Thus, investigators should consider ODA as a robust complement, or perhaps alternative, to the conventional approach for assessing covariate balance in matching studies.     

机器学习在心脏超声领域中的应用研究

机器学习是一种可以帮助医生程序化处理问题的自动化便捷模式,近年来随着大数据时代的到来,这一技术在各个领域得到了飞速的进展,尤其是在心血管领域有很大的潜在价值。目前已成功将机器学习应用于心血管疾病的各个领域中,为临床及影像科医生提供了便捷,实现了疾病诊疗的准确性及可重复性,本文就详细阐述了机器学习在心脏超声领域的应用及发展,同时讨论了发展方向。     

基于声学分析和机器学习构建咳嗽和清嗓分类模型

摘要 目的：建立气道廓清动作的声学分类器,为实施肺康复的咳嗽训练提供监测工具。 方法：健康男性11例和女性15例,分别在平卧位、45°靠坐位和90°端坐位,根据随机视觉指令执行咳嗽和清嗓动作各10次,并同时录制声音,分析声音片段的时域、频域和信息域特征,由此构建声音的特征矢量用于机器学习。采用的模型包括：线性判别分析、分类回归决策树、随机森林和线性分类器型支持向量机。 结果：模型间比较显示随机森林方法所建分类器具有更高的准确度（0.9162）和一致性（Kappa值为0.8323）。验证结果显示该模型无论在区分体位因素或不区分体位情况下,对咳嗽音有较高的准确度、一致性、敏感度和特异度。 结论：咳嗽和清嗓动作具有声学差异,并且这种差异可以由随机森林方法构建机器学习模型加以分类,由此为肺康复治疗中采用声学手段辅助判断气道廓清动作类型提供了依据。     

Iterative processes: a review of semi-supervised machine learning in rehabilitation science

Abstract

Purpose: To define semi-supervised machine learning (SSML) and explore current and potential applications of this analytic strategy in rehabilitation research.

Method: We conducted a scoping review using PubMed, GoogleScholar and Medline. Studies were included if they: (1) described a semi-supervised approach to apply machine learning algorithms during data analysis and (2) examined constructs encompassed by the International Classification of Functioning, Disability and Health (ICF). The first two authors reviewed identified articles and recorded study and participant characteristics. The ICF domain used in each study was also identified.

Results: After combining information from the eight studies, we established that SSML was a feasible approach for analysis of complex data in rehabilitation research. We also determined that semi-supervised approaches may be more accurate than supervised machine learning approaches.

Conclusions: A semi-supervised approach to machine learning has potential to enhance our understanding of complex data sets in rehabilitation science. SSML mirrors the iterative process of rehabilitation, making this approach ideal for calibrating devices, classifying activities or identifying just-in-time interventions. Rehabilitation scientists who are interested in conducting SSML should collaborate with data scientists to advance the application of this approach within our field.

• Implications for rehabilitation

• Semi-supervised machine learning applications may be a feasible approach for analyses of complex data sets in rehabilitation research.

• Semi-supervised machine learning approaches uses a combination of labelled and unlabelled data to produce accurate predictive models, thereby requiring less user-input data than other machine learning approaches (i.e., supervised, unsupervised), reducing resource cost and user-burden.

• Semi-supervised machine learning is an iterative process that, when applied to rehabilitation assessment and outcomes, could produce accurate personalized models for treatment.

• Rehabilitation researchers and data scientists should collaborate to implement semi-supervised machine learning approaches in rehabilitation research, optimizing the power of large datasets that are becoming more readily available within the field (e.g., EEG signals, sensors, smarthomes).

     

机器学习用于慢性肾衰竭研究进展

慢性肾衰竭(CRF)为多因素所致慢性进行性肾实质损害,进而可累及全身多系统。机器学习(ML)可对高维医学数据进行数据分析和挖掘,对于解决临床复杂问题具有显著潜力。本文围绕ML用于CRF研究进展进行综述。     

基于功能MRI机器学习用于诊断和治疗精神分裂症研究进展

精神分裂症(SZ)是一种严重精神疾病,采用传统方法进行诊断易漏、误诊。利用机器学习(ML)算法可从功能MRI(fMRI)数据中提取SZ相关特征,并进行诊断及疗效预测。本文就基于fMRI的ML用于诊断和治疗SZ的研究进展进行综述。     

基于多参数MRI及影像组学建立机器学习模型诊断临床显著性前列腺癌

目的 建立基于多参数MRI（mpMRI）和影像组学特征的机器学习模型,评价其诊断临床显著性前列腺癌（CSPC）的价值。方法 结合纹理分析、MR动态增强定量分析、前列腺影像报告与数据系统（PI-RADS）评分和部分临床资料建立Logistic回归（LR）、逐步回归（SR）、经典决策树（cDT）、条件推断树（CIT）、随机森林（RF）和支持向量机（SVM）模型,运用ROC曲线和决策曲线分析法（DCA）评价上述模型和变量的重要性。结果 验证组中RF模型诊断CSPC的AUC大于SVM、cDT、SR模型（P均<0.05）,RF模型与LR、CIT模型诊断CSPC的AUC差异无统计学意义（P均>0.05）,其余各模型间诊断CSPC的AUC差异无统计学意义（P均>0.05）。概率阈值为16%~91%时,RF模型的净获益最大,优于其他模型;概率阈值为23%~91%时,SVM模型的净获益仅次于RF模型而优于其他模型。前列腺特异性抗原密度（PSAD）和部分纹理分析参数的重要性较高。结论 RF模型诊断CSPC优于其他模型,SVM模型次之。PSAD和纹理分析相关参数诊断CSPC的重要性高于PI-RADS评分和动态增强MRI定量参数。     

使用生物标志物建立机器学习模型用于菌血症的早期预测

目的 菌血症的早期识别和及时干预是降低发病率和死亡率的关键。血培养是诊断菌血症的金标准,但其敏感性低且周转时间长,难以满足临床诊断的需求。本研究的目的是通过回顾性分析,从若干与感染相关的生物标志物中筛选出最佳特征子集用于构建机器学习模型,用于菌血症的早期预测。方法 采用回顾性分析筛选出符合研究标准的菌血症和局部感染患者,并收集参与者的生物标志物数据。通过特征选择算法筛选最佳特征子集,并以此构建机器学习模型。使用精确率,召回率和准确率综合评估机器学习模型的性能。结果 本研究收集到247例菌血症患者为病例组,262例局部感染患者为对照组,并纳入12项生物标志物。通过特征选择算法,我们发现支持向量机(support vector machine, SVM)模型只须纳入5项生物标志物,其模型准确率为90.2%,AUC高达0.967。结论 通过特征选择算法与机器学习模型相结合的策略,本研究开发了3种菌血症的预测模型,其中SVM的性能最佳,为菌血症的早期诊断提供了数据支持。     

基于CT影像组学和机器学习脑出血研究进展

脑出血指非外伤性脑实质内出血,发病急,进展迅速,致死率和致残率高。对于疑诊急性脑出血患者,CT为首选影像学检查手段。影像组学高通量从CT图像中提取特征信息,结合机器学习算法,能快速、准确地诊断疾病、评估病情和预测预后。本文就基于CT影像组学和机器学习脑出血研究进展进行综述。     

基于MRI影像组学和机器学习诊断抑郁症研究进展

抑郁症为常见神经心理疾病,目前尚无客观诊断标准。MRI可提供脑结构、白质纤维束完整性及静息态和任务态下脑功能等多方面信息;MRI影像组学和机器学习(ML)有助于建立个体化诊断抑郁症模型。本文对基于MRI影像组学及ML诊断抑郁症研究进展进行综述。     

机器学习在急诊医学中应用的研究进展及展望

随着信息技术及医疗数据信息化的不断发展,越来越多的临床医生认识到人工智能或将彻底改变医学实践。机器学习可对大量医疗数据进行学习,探索数据集中的依赖关系,从而形成相应的医学模型;模型可对新的数据进行快速准确预测,有利于疾病早期诊断分级、辅助制定临床决策等。急诊医学面临着医疗资源相对短缺、急危重症患者识别及快速诊治需求等现状。在大数据时代,以临床需求为导向,机器学习为手段的智慧医疗或将成为解决上述问题的关键之一。     

基于机器学习和脑网络的阿尔茨海默病多模态影像研究进展

阿尔茨海默病是发生在中老年人群体中的神经退行性疾病,以记忆障碍和认知能力下降为主要特征。目前大多数关于阿尔茨海默病的多模态影像研究主要集中在局部特定脑区,缺乏对全脑网络模式的深入探讨。本文针对基于机器学习和脑网络的阿尔茨海默病多模态影像研究进行概述。首先,介绍了阿尔茨海默病的定义以及机器学习技术在脑疾病影像研究中的局限性;其次,阐述了机器学习在脑网络预测中的通用流程,主要包括：特征提取、特征选择与特征降维、模型构建、模型评价;最后,依次介绍了机器学习在阿尔茨海默病的灰质结构脑网络、白质结构脑网络、静息态功能脑网络以及多模态融合脑网络的研究成果。通过对近年来研究成果的梳理,本文对该领域未来发展方向进行了以下三点展望：大样本多中心研究,具有可解释性的深度学习技术,建立纵向预测模型。