常用人格测验在军校大一新生心理档案中的应用及结果分析

目的:通过总结与分析,研究适合军校新入校大学生心理档案建立的合理人格测验。方法:分别采用卡特尔十六种人格因素测验(16PF)、明尼苏达多项人格测验(MMPI)、症状自评量表(SCL-90)测量今年某军校大一新生98名,剔除无效后保留有效数据94例,根据结果筛选人员并做相关分析。结果:①16PF检测出3名问题人员,MMPI检测出18名,SCL-90检测出8名。②MMPI和SCL-90有较高的相关性,r<0.42,P<0.05,MMPI完全可以替代SCL-90③MMPI部分临床量表与16PF些许因素呈显著相关,可考虑将相关因素加入16PF评价标准里。结论:建立心理档案时,使用16PF作为初筛测验,MMPI作为复检测验,能够较完善地建立心理档案。     

某军校新生人格特征分析及建议

目的:调查某军校大一新生的人格特征及心理健康状况,为开展军校学员心理健康教育提供科学依据。方法:采用卡特尔16种个性因素测验(16PF)对1224名大一学员进行测试。结果:除乐群性和世故性外,在剩余14个因素上,新学员的结果与全国成年人常模均存在显著差异;其中,来自城市的学员与农村学员在乐群性、兴奋性、敢为性、敏感性、怀疑性、幻想性和世故性因子有显著差异;独生子女与非独生子女在兴奋性、怀疑性、实验性和独立性上,表现出显著差异。结论:该校大一学员整体心理健康状况良好,但在恃强性、兴奋性、敢为性、幻想性因子得分偏高,应进行系统教育和调控;同时,也要注重城市学员与农村学员、独生子女与非独生子女之间的心理差距,因材施教,共同提高。     

恋爱大学生人格特点与社会支持的关系研究

目的:探讨恋爱大学生人格特点与社会支持的关系。方法:采用卡特尔16种人格问卷(16PF)和社会支持量表对231名谈恋爱大学生进行问卷调查。结果:恋爱大学生的人格特征与他们的社会支持密切相关,不同社会支持水平的恋爱大学生在人格方面存在显著差异。结论:恋爱大学生的人格特征与社会支持关系密切。     

4415名新兵心理健康状况调查分析

目的:了解4415名入伍新兵的心理健康状况,预防可能出现的应激性心理问题,并进一步对新兵进行筛选,为有针对性的进行心理健康教育提供科学依据.方法:应用卡特尔16种人格因素测验(16PF)对2011年入伍的4415名普通新兵进行心理测试.结果:入伍新兵在乐群性、聪慧性、稳定性、恃强性、兴奋性、有恒性、敢为性、世故性、自律性等因子上的得分高于常模,而在怀疑性、忧虑性、独立性、紧张性等因子上的得分低于常模;男女新兵在乐群性、恃强性、兴奋性、幻想性、自律性因子以及内向与外向型、感情用事与安详机警型、新环境成长能力三个次级因子上存在显著性差异;不同学历的新兵在聪慧性、稳定性、恃强性、兴奋性、有恒性、敢为性、幻想性、忧虑性、独立性因子以及适应与焦虑型、内向与外向型、怯懦与果断型、心理健康因素、专业成就个性因素、创造能力个性因素、新环境成长能力次级因子上存在显著差异.结论:2011年4415名新入伍战士整体上具有比较良好的心理品质;男女新兵在心理健康状况方面各有优劣;高学历新兵的心理健康状况优于低学历者.列出91名心理调节能力较差者,若干疑似精神病患者送往专业医院进行鉴定,最终对16名新兵做出退兵处理.     

甲状腺乳腺手术病人术前焦虑水平、人格特征对术后恢复的影响分析

目的:探讨甲状腺、乳腺患者术前不同程度的焦虑水平及人格特征对术后恢复的影响。方法:选取我院普外科2010年7月--2011年7月甲状腺乳腺手术病人1500例,采用焦虑自评量表SAS进行焦虑程度评估,采用卡特尔16种人格因素测验(16PF)对患者进行人格特征测定。结果:甲状腺、乳腺患者术前焦虑水平与术后并发症、卧床时间、住院时间、心理适应、镇痛药使用次数五个指标存在关联性,P均<0.05;甲状腺、乳腺患者术后恢复状况与O(忧虑性)、Q4(紧张性)、(I敏感性)、C(稳定性)及A(乐群性)呈现相关性,P均<0.05,与其它因子均无相关性。结论:患者术前焦虑水平及人格特征对术后恢复有一定的影响,护理人员要及时了解患者的心理,及时给予调节和治疗,使患者以积极的心态顺利度过围手术期,保障住院患者的安全,促进患者康复和预后。     

基于序列特征的人类PolⅡ启动子理论预测

基于已知的人类PolII启动子序列数据,综合选取启动子序列内容和序列信号特征,构建启动子的支持向量机分类器．分别以启动子序列的6-mer频数作为离散源参数构建序列内容特征。同时选取24个位点的3-mer频数作为序列信号特征构建PWM,将所得到的两类参数输入支持向量机对人类启动子进行预测．用10折叠交叉检验和独立数据集来衡量算法的预测能力,相关系数指标达到95％以上,结果显示结合了支持向量机的离散增量算法能够有效的提高预测成功率,是进行真核生物启动子预测的一种很有效的方法．     

某军校学员心理健康情况调查分析

目的：了解军校学员心理健康状况,为军校心理辅导工作提供理论依据,提高军校教育管理水平,有效预防学员在校期间由于压力、情绪等因素造成的心理问题。方法：使用DXC-6型多项群体心理测评仪,对某军校不同年级1332名学员进行卡特尔16种人格个性因素测验。结果：①1332名被测试学员16PF得分总体呈现”五高三低”。②不同年级的学员在16PF多项因子上得分有差异（P〈0．05）。研究生在C、M、O等因子得分明显高于大三学员,而在A、F、H等得分明显低于大三学员（P〈0．05）;研究生在C因子得分明显高于大四学员（P〈0．05）,F、G、Y2因子得分明显低于大四学员（P〈0．05）,而在A、M、O、H这些因子的得分差异无统计学意义（P〉0．05）;大三学员在A、H、X2等得分明显高于大四学员,而在M、O、Q1等得分明显低于大四学员（P〈0．05）。结论：军校学员的整体心理健康水平良好;本科学员（尤其是大三学员）的心理健康水平明显优于研究生;大三的学员在乐群性、敢为性、实验性、忧虑性等方面优于大四学员,和大四学员相比,大三学员更喜欢探索新生事物。     

低压舱模拟飞行对于脑力负荷影响的初步探讨

目的：通过低压舱模拟飞行环境下被试人员生理指标的变化,探讨飞行员脑力负荷的变化规律。方法：21名男性志愿者参加低压舱模拟飞行的实验过程,座舱内的压力高度模拟2400米高空压力,持续时间1．5h,重复两次,中间出舱休息0．5h,检测指标为心率变异性（HRV）,心理运动测验以及NASA—TLX主观评定量表。结果：研究表明,NASA－TLX量表从主观感受上很好的反映了低压舱模拟飞行后脑力负荷的变化,HRV、心理运动测验也发生了相应的变化。结论：本研究结果提示低压舱模拟飞行所导致的脑力负荷变化是具有一定规律的,生理心理测验可能是测定变化规律的一种间接方法。     

基于离散增量结合支持向量机方法的果蝇启动子预测

目的：改进真核生物启动子的理论预测方法。方法：基于启动子序列的信号特征和内容特征,构建6个标准离散源,计算每条序列相对于标准离散源的离散增量;构建信号特征的启动子位置权重矩阵,计算其对应位置的位置权重打分函数,将所得到的两类参数输入支持向量机对果蝇启动子进行预测。结果：利用self-consistency和cross-validation两种方法对此算法进行检验,均获得了较高的预测成功率,结果表明五种转录因子结合位点的预测成功率均超过91%。结论：结果显示结合了支持向量机的离散增量算法能够有效的提高预测成功率,是进行真核生物启动子预测的一种很有效的方法。     

牙颌面畸形患者的人格特征分析

目的:通过明尼苏达多项人格测验评估牙颌面畸形患者术前的人格特征。方法:采用随机对照的方法,选取2012年5月~2013年5月在第四军医大学口腔医学院颅颌面创伤整形外科病区就诊的先天性牙颌面畸形患者102例,利用DXC-6型软件进行筛选,将其中64例纳入病例组;同时选取第四军医大学经过征兵心理测试且成绩合格的本科学员、八年制学员及硕/博士研究生83例,利用DXC-6型软件进行筛选,将其中57例纳入对照组。以问卷调查的方式对两组进行人格特征评估并比较其结果。结果:病例组MAS量表、Si量表评分与对照组比较存在显著性差异(P0.05),病例组显著高于对照组,两组其余各因素评分比较未见显著性差异(P0.05)。不同学历、年龄及性别的牙颌面畸形患者MAS量表和Si量表各项指标评分比较均无显著性差异(P0.05)。结论:牙颌面畸形患者存在对周围人反应过于敏感,缺乏自信等心理问题,可能与其异常面容有关,而与患者的学历、年龄及性别无关。     

Stroke Treatment Prediction Using Features Selection Methods and Machine Learning Classifiers

ObjectivesFeature selection in data sets is an important task allowing to alleviate various machine learning and data mining issues. The main objectives of a feature selection method consist on building simpler and more understandable classifier models in order to improve the data mining and processing performances. Therefore, a comparative evaluation of the Chi-square method, recursive feature elimination method, and tree-based method (using Random Forest) used on the three common machine learning methods (K-Nearest Neighbor, naïve Bayesian classifier and decision tree classifier) are performed to select the most relevant primitives from a large set of attributes. Furthermore, determining the most suitable couple (i.e., feature selection method-machine learning method) that provides the best performance is performed.Materials and methodsIn this paper, an overview of the most common feature selection techniques is first provided: the Chi-Square method, the Recursive Feature Elimination method (RFE) and the tree-based method (using Random Forest). A comparative evaluation of the improvement (brought by such feature selection methods) to the three common machine learning methods (K- Nearest Neighbor, naïve Bayesian classifier and decision tree classifier) are performed. For evaluation purposes, the following measures: micro-F1, accuracy and root mean square error are used on the stroke disease data set.ResultsThe obtained results show that the proposed approach (i.e., Tree Based Method using Random Forest, TBM-RF, decision tree classifier, DTC) provides accuracy higher than 85%, F1-score higher than 88%, thus, better than the KNN and NB using the Chi-Square, RFE and TBM-RF methods.ConclusionThis study shows that the couple - Tree Based Method using Random Forest (TBM-RF) decision tree classifier successfully and efficiently contributes to find the most relevant features and to predict and classify patient suffering of stroke disease.”     

PCP: a program for supervised classification of gene expression profiles

PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns (vectors of measurements). The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression. PCP implements leading pattern classification and gene selection algorithms and incorporates cross-validation estimation of classifier performance. Importantly, the implementation integrates gene selection and class prediction stages, which is vital for computing reliable performance estimates in small-sample scenarios. Additionally, the program includes automated and efficient model selection (optimization of parameters) for support vector machine (SVM) classifier. The distribution includes Linux and Windows/Cygwin binaries. The program can easily be ported to other platforms. AVAILABILITY: Free download at http://pcp.sourceforge.net     

Classification of Early Stages of Esophageal Cancer Using Transfer Learning

ObjectivesEsophageal Cancer is the sixth most common cancer with a high fatality rate. Early prognosis of esophageal abnormalities can improve the survival rate of the patients. The sequence of the progress of the esophageal cancer is from esophagitis to non-dysplasia Barrett's esophagus to dysplasia Barrett's esophagus to esophageal adenocarcinoma (EAC). Many studies revealed a 5-fold increase in EAC patients diagnosed with esophagitis, and those diagnosed with Barrett's esophagus have a greater risk of EAC.Material and methodsConvolutional Neural Network (CNN) with efficient feature extractors enable better prognosis of the pre cancerous stage, Barrett's esophagus and esophagitis. The transfer learning techniques with CNN can extract more relevant features for the automated classification of Barrett's esophagus and esophagitis. This paper presents a study on the classification of the esophagitis and Barrett's esophagus (BE) using Deep Convolution Neural Networks (DCNN).ResultsIn the first experiment, the DCNN models perform as a feature extractor, and standard classifiers do the classification. The performance analysis shows that the CNN model ResNet50 with Support Vector Machine (SVM) has an accuracy of 93.5%, recall 93.5%, precision 93.4%, f score 93.5%, AUC 89.8%. In the second experiment, the DCNN classification models perform the classification with Transfer Learning and fine-tuning. The ResNet50 model has improved accuracy of 94.46%, precision 94.46%, f score 94.46%, AUC 96.20%.ConclusionThe ResNet50 model with transfer learning and fine-tuning gives a better performance than the ResNet50 model with SVM classifier. Our experiments show that the DCNN is effective for diagnosing EAC, both as feature extractors and classification models with transfer learning and fine-tuning.     

基于支持向量机的表面肌电信号和加速度融合跌倒识别方法

目的:针对老人易跌倒和跌倒过后可能产生严重后果这一现实问题,通过将表面肌电信号和加速度融合,进一步优化采用支持向量机分类器下的包含跌倒在内的几种不同动作的分类效果。方法:提出基于表面肌电和加速度信号融合的跌倒识别算法,首先采集股直肌,股内侧肌,胫骨前肌和腓肠肌的表面肌电信号以及位于腰部的三轴加速度信号作为实验数据,然后利用滑动窗口法提取表面肌电和加速度信号的均方根值,最后针对人体日常活动和跌倒的运动特征,构建了支持向量机的分类器。结果:实验数据共计320组数据,包括3种日常活动和向前跌倒,其中160组数据作为训练集,另外160组数据作为测试集。对4种动作进行识别实验,算法的准确度为93.23%、灵敏度为92.4%、特异度为100%,达到了良好的分类效果。结论:基于支持向量机的表面肌电信号和加速度融合的跌倒识别算法分类效果良好,对于老人跌倒防护具有现实意义。     

Exploring Radiologic Criteria for Glioma Grade Classification on the BraTS Dataset

ObjectivesGlioma grading using maching learning on magnetic resonance data is a growing topic. According to the World Health Organization (WHO), the classification of glioma discriminates between low grade gliomas (LGG), grades I, II; and high grade gliomas (HGG), grades III, IV, leading to major issues in oncology for therapeutic management of patients. A well-known dataset for machine-based grade prediction is the MICCAI Brain Tumor Segmentation (BraTS) dataset. However this dataset is not divided into WHO-defined LGG and HGG, since it combines grades I, II and III as “lower grades gliomas”, while its HGG category only presents grade IV glioblastoma multiform. In this paper we want to train a binary grade classifier and investigate the consistency of the original BraTS labels with radiologic criteria using machine-aided predictions.Material and methodsUsing WHO-based radiomic features, we trained a SVM classifier on the BraTS dataset, and used the prediction score histogram to investigate the behaviour of our classifier on the lower grade population. We also asked 5 expert radiologists to annotate BraTS images between low (as opposed to lower) grade and high grade glioma classes, resulting in a new groundtruth.ResultsOur first training reached 84.1% accuracy. The prediction score histogram allows us to identify the radiologically high grade patients among the original lower grade population of the BraTS dataset. Training another SVM on our new radiologically WHO-aligned groundtruth shows robust performances despite important class imbalance, reaching 82.4% accuracy.ConclusionOur results highlight the coherence of radiologic criteria for low grade versus high grade classification under WHO terms. We also show how the histogram of prediction scores and crossed prediction scores can be used as tools for data exploration and performance evaluation. Therefore, we propose to use our radiological groundtruth for future development on binary glioma grading.     

A Computationally Efficient Correlational Neural Network for Automated Prediction of Chronic Kidney Disease

ObjectivesIn this paper, we propose a computationally efficient Correlational Neural Network (CorrNN) learning model and an automated diagnosis system for detecting Chronic Kidney Disease (CKD). A Support Vector Machine (SVM) classifier is integrated with the CorrNN model for improving the prediction accuracy.Material and methodsThe proposed hybrid model is trained and tested with a novel sensing module. We have monitored the concentration of urea in the saliva sample to detect the disease. Experiments are carried out to test the model with real-time samples and to compare its performance with conventional Convolutional Neural Network (CNN) and other traditional data classification methods.ResultsThe proposed method outperforms the conventional methods in terms of computational speed and prediction accuracy. The CorrNN-SVM combined network achieved a prediction accuracy of 98.67%. The experimental evaluations show a reduction in overall computation time of about 9.85% compared to the conventional CNN algorithm.ConclusionThe use of the SVM classifier has improved the capability of the network to make predictions more accurately. The proposed framework substantially advances the current methodology, and it provides more precise results compared to other data classification methods.     

Investigating the gene expression profiles of cells in seven embryonic stages with machine learning algorithms

The development of embryonic cells involves several continuous stages, and some genes are related to embryogenesis. To date, few studies have systematically investigated changes in gene expression profiles during mammalian embryogenesis. In this study, a computational analysis using machine learning algorithms was performed on the gene expression profiles of mouse embryonic cells at seven stages. First, the profiles were analyzed through a powerful Monte Carlo feature selection method for the generation of a feature list. Second, increment feature selection was applied on the list by incorporating two classification algorithms: support vector machine (SVM) and repeated incremental pruning to produce error reduction (RIPPER). Through SVM, we extracted several latent gene biomarkers, indicating the stages of embryonic cells, and constructed an optimal SVM classifier that produced a nearly perfect classification of embryonic cells. Furthermore, some interesting rules were accessed by the RIPPER algorithm, suggesting different expression patterns for different stages.