基于隐马尔科夫模型的胎盘植入产前诊断方法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201704007

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Abstract Placenta accreta is one of the most serious complications of obstetrics. As a gold standard, the postnatal pathological examination has hysteresis and limitation. In this paper, the multi-feature associations of medical history information and color Doppler ultrasound data are used as observation sequences and the postpartum pathological results are used as hidden state sequences. The prenatal prediction method of placenta accreta based on hidden Markov model is proposed. The algorithm of Gini is used to extract the disease factors. Then, the hidden Markov model is built by the set of factors. Through the observation and hidden sequences, the prenatal prediction of placenta accreta is accomplished using Baum-Welch and Viterbi algorithms. The experimental results show that the proposed method achieves better diagnostic accuracy, sensitivity and specificity.

Key words： Placenta Accreta Feature Extraction Hidden Markov Model Observation Sequence

Received: 12 June 2016

ZTFLH:

TP 18

Fund:Supported by Leading Key Project of Fujian Province(No.2016Y0060,2014Y0005)

About author:: (ZHANG Dong, born in 1981, Ph.D., associate professor. His research interests include computer network and artificial intelligence.)
(CHEN Kai, born in 1991, master student. His research interests include computational intelligence.)
(YAN Jianying, born in 1967, master, professor. Her research interests include gyneco-
logy.)
(ZHU Danhong, born in 1981, master, lecturer. Her research interests include intelligent image processing and computational inte-lligence.)
(YE Dongyi(Corresponding author), born in 1964, Ph.D., professor. His research interests include computational intelligence and data mining.)

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	ZHANG Dong
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	YAN Jianying
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	YE Dongyi

Cite this article:

ZHANG Dong,CHEN Kai,YAN Jianying等. Prenatal Diagnosis Method of Placenta Accreta Based on Hidden Markov Model[J]. , 2017, 30(4): 353-358.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201704007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I4/353

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