Machine learning with generative mixture models for fetal monitoring

用于胎儿监测的生成混合模型的机器学习

基本信息

批准号：
8816208
负责人：
Petar M Djuric
金额：
$ 23.46万
依托单位：
STATE UNIVERSITY NEW YORK STONY BROOK
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-03-01 至 2017-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8816208
关键词：
Agreement Algorithms Apgar Score Bayesian Method Behavioral Birth Categories Childbirth Classification Clinical Communities Consensus Data Data Set Decision Support Systems Delivery Rooms Dependence Dependency Diagnosis Diagnostic Distress Electrodes Evolution Feedback Fetal Heart Fetal Heart Rate Fetal Monitoring Fetus Freedom Gold Individual Intraobserver Variability Joints Knowledge Label Learning Litigation Machine Learning Measurement Methodology Methods Modeling Monitor Monte Carlo Method Mothers Motivation Noise Outcome Outcome Measure Output Pattern Performance Phase Physicians Probability Procedures Process Reading Records Research Risk Sampling Scalp structure Series Signal Transduction System Testing Time Training Umbilical cord structure Uncertainty Uterine Contraction Validation Work base cost fetal improved interest pressure public health relevance stem theories vector

Agreement Algorithms Apgar Score Bayesian Method Behavioral Birth Categories Childbirth Classification Clinical Communities Consensus Data Data Set Decision Support Systems Delivery Rooms Dependence Dependency Diagnosis Diagnostic Distress Electrodes Evolution Feedback Fetal Heart Fetal Heart Rate Fetal Monitoring Fetus Freedom Gold Individual Intraobserver Variability Joints Knowledge Label Learning Litigation Machine Learning Measurement Methodology Methods Modeling Monitor Monte Carlo Method Mothers Motivation Noise Outcome Outcome Measure Output Pattern Performance Phase Physicians Probability Procedures Process Reading Records Research Risk Sampling Scalp structure Series Signal Transduction System Testing Time Training Umbilical cord structure Uncertainty Uterine Contraction Validation Work base cost fetal improved interest pressure public health relevance stem theories vector

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项目摘要

DESCRIPTION (provided by applicant): For many years, there has been a concerted effort to automate the analysis of fetal heart rate (FHR) rhythms. However, despite significant advances in biomedical signal analysis, there has not been any significant improvement in automated decision support systems. FHR monitoring is now ubiquitous throughout delivery rooms, especially using the non-invasive Doppler monitor, but also using the fetal scalp electrode. Physician classification of fetal heart rate patterns is known to be a non-trivial problem because of significant inter and intra-observer variability of diagnosis. This has led to a marked increase in the number of caesarean deliveries, thereby increasing risk to the fetus and mother in many cases. This has further motivated the machine learning community to automate the classification procedure in the interest of accuracy and consistency as well as robustness with respect to noise. Usual approaches to this involve some type of supervised classification procedure, where the algorithm output on training data is compared with a "gold-standard" physician classification, followed by testing and validation on new datasets. However, since physician classification can be unreliable in the presence of the aforementioned diagnostic variability, as well as significant tracing noise, we propose the use of unsupervised algorithms to cluster FHR data records into clinically useful categories. We use nonparametric Bayes theory and Markov-time-dependence models for the evolution of feature sequences to propose methods that will achieve improved accuracy. The methods involve extraction of feature sequences from FHR time series data, which are modeled as samples from finite or infinite Dirichlet mixture models. We then use Gibbs sampling to obtain the cluster probabilities for each dataset. Clustering outcomes are compared against direct physician diagnosis and our current results are seen to be in broad agreement with them, while still giving new information on the character of different sub-groups of FHR records. With the proposed research, further gains in classification performance will be made.

描述（由申请人提供）：多年来，一直在努力自动进行胎儿心率（FHR）节奏的分析。然而，尽管生物医学信号分析取得了重大进展，但自动化决策支持系统却没有任何显着改善。现在，FHR监视在整个输送室中无处不在，尤其是使用非侵入性多普勒监视器，但也使用胎儿头皮电极。胎儿心率模式的医师分类已知是一个非平凡的问题，因为诊断的跨间和观察者内差异很大。这导致显着增加在剖腹产的数量中，在许多情况下，胎儿和母亲的风险增加了。这进一步激发了机器学习社区以准确性和一致性以及在噪声方面的稳健性而自动化分类程序。对此的常规方法涉及某种类型的监督分类程序，其中将训练数据上的算法输出与“金标准”医师分类进行了比较，然后在新数据集上进行测试和验证。但是，由于医师的分类在存在上述诊断变异性以及明显的跟踪噪声的情况下可能是不可靠的，因此我们建议将无监督算法使用到集群FHR数据记录属于临床上有用的类别。我们使用非参数贝叶斯理论和Markov时间依赖性模型进行特征序列的演变，以提出可以提高准确性的方法。该方法涉及从FHR时间序列数据中提取特征序列，这些数据被建模为来自有限或无限的Dirichlet混合模型的样品。然后，我们使用Gibbs采样来获取每个数据集的群集概率。将聚类结果与直接医师诊断进行了比较，我们当前的结果被认为与它们一致，同时仍提供有关FHR记录不同子组特征的新信息。通过拟议的研究，将取得进一步的分类性能。