Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm.

Background: The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors.

Objective: To establish models for early prediction and intervention of HDP.

Methods: This study used the three types of risk factors and support vector machine (SVM) to establish prediction models of HDP at different gestational weeks.

Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest.

Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26 week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26 week of gestation (denoted by PL and TL group).

Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions.

The aims of this study were to apply decision tree to classify uterine activities (contractions and non-contractions) using the waveform characteristics derived from different channels of electrohysterogram (EHG) signals and then rank the importance of these characteristics. Both the tocodynamometer (TOCO) and 8-channel EHG signals were simultaneously recorded from 34 healthy pregnant women within 24 h before delivery. After preprocessing of EHG signals, EHG segments corresponding to the uterine contractions and non-contractions were manually extracted from both original and normalized EHG signals according to the TOCO signals and the human marks.

Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram.

Uterine contraction (UC) activity is commonly used to monitor the approach of labour and delivery. Electrohysterograms (EHGs) have recently been used to monitor UC and distinguish between efficient and inefficient contractions. In this study, we aimed to identify UC in EHG signals using a convolutional neural network (CNN).

Development of Electrohysterogram Recording System for Monitoring Uterine Contraction.

Uterine contraction (UC) is an important clinical indictor for monitoring uterine activity. The purpose of this study is to develop a portable electrohysterogram (EHG) recording system (called PregCare) for monitoring UCs with EHG signals. The PregCare consisted of sensors, a signal acquisition device, and a computer with application software.

DM-RPIs: Predicting ncRNA-protein interactions using stacked ensembling strategy.

ncRNA-protein interactions (ncRPIs) play an important role in a number of cellular processes, such as post-transcriptional modification, transcriptional regulation, disease progression and development. Since experimental methods are expensive and time-consuming to identify the ncRPIs, we proposed a computational method, Deep Mining ncRNA-Protein Interactions (DM-RPIs), for identifying the ncRPIs. In order to descending dimension and excavating hidden information from k-mer frequency of RNA and protein sequences, using the Deep Stacking Auto-encoders Networks (DSANs) model refined the raw data.

Adaptive changes in micromechanical environments of cancellous and cortical bone in response to in vivo loading and disuse.

The skeleton accommodates changes in mechanical environments by increasing bone mass under increased loads and decreasing bone mass under disuse. However, little is known about the adaptive changes in micromechanical behavior of cancellous and cortical tissues resulting from loading or disuse. To address this issue, in vivo tibial loading and hindlimb unloading experiments were conducted on 16-week-old female C57BL/6J mice.