抑郁症和高血压病共病患者临床对照研究

目的 探讨抑郁症和高血压病共病患者的临床特征及高血压病时抑郁症的影响.方法 使用自制的一般情况调查表,收集36例抑郁症和高血压病共病患者和36例同期住院的单纯抑郁症患者的社会人口学资料、病情特征、临床症状资料及治疗前、治疗后的HAMD量表总分进行统计分析.结果 抑郁症和高血压病共病患者的平均发病次数(f=2.90,P＜...     

脑卒中后抑郁症患者治疗前后事件相关电位P300变化的对比研究

目的:比较脑卒中后抑郁症患者在治疗前后的事件相关电位(ERP)P300的改变特征,为客观评价临床疗效和早期诊断抑郁症提供更灵敏的参考依据.方法:从住院治疗的卒中后抑郁症患者中选60例作为研究组,分别观测治疗前后P300之潜伏期与波幅变化.结果:与治疗后相比较,治疗前波形不稳定,N1、N2、P3波潜伏期延迟,P3波波幅降低.结论:ERP P300波之潜伏期与波幅可以作为评价抑郁症临床疗效的参考指标.     

青少年抑郁症的首诊状况调查与临床特征分析

目的 探讨青少年抑郁症的首诊状况与临床特征,为早期诊断提供依据.方法 精神科门诊确诊的青少年抑郁症患者53例,采用躯体症状和自制社会功能自评量表进行评定分析.结果 58.49％患者首诊于精神专科,其中,77.42％的患者因学习及人际关系问题就诊;48.51％首诊于非精神科,主要因失眠、疲乏无力、头痛、头晕等症状就诊.结...     

伴精神病性特征抑郁症患者自杀未遂的危险因素

目的:分析伴精神病性特征抑郁症患者自杀未遂的危险因素.方法:对2010年9月1日-2011年2月28日“中国双相障碍患者诊断评估服务”研究项目的数据进行二次分析,1068例抑郁症患者中伴精神病性特征抑郁症患者112人(10.5％).采用简明国际神经精神访谈(M.I.N.I)中抑郁发作模块、自杀模块和精神病性疾患模块,分析伴精神病性特征抑郁症患者自杀未遂的危险因素.结果:伴精神病性特征抑郁症患者较不伴精神病性特征抑郁症患者的自杀未遂风险高(OR =2.22),多因素logistic回归分析显示被控制体验(OR =3.54)、幻听(OR =3.84)和无价值感/罪恶感(OR =4.78)的患者更易有自杀未遂风险.结论:本研究提示伴精神病性特征抑郁症患者的自杀未遂风险高,存在被控制体验、幻听和无价值感/罪恶感症状的患者发生自杀行为的危险性可能更高.     

狂躁抑郁症患者在安静状态下的闭眼眼球活动研究

目的 探讨狂躁抑郁症患者安静状态下的闭眼眼球活动(GEM)特征,为临床诊断提供依据.方法 随机选取我院2011年9月～2013年11月收治的确诊为狂躁抑郁症的患者45例作为狂躁抑郁症组,另选取同期我院收治的精神分裂症患者45例(精神分裂症组)和正常人45例(对照组)作为对照,对三组入选对象进行GEM观察记录,并对结果进行分析.结果 狂躁抑郁症组合精神分裂症组患者主要为R波,正常组主要为S波,其中狂躁抑郁症组患者的S波和R波次数在正常组和精神分裂症组患者之间,各组CEM指标存在显著差异(P＜0.05).结论 狂躁抑郁症患者安静状态下闭眼眼球活动具有一定特征,可作为同正常人和精神分裂症患者进行区别的依据.     

临床抑郁症患者的注意偏倚特征

目的：探讨抑郁症患者是否存在注意偏倚，并分析其注意偏倚的时程特征。方法：采用点探测任务，要求受试尽快对探测点的位置进行判断。采用重复测量方差分析。比较抑郁症患者和对照组在不同条件（不同的情绪图片配对、不同的图片呈现时间）下，对探测点作出反应的平均反应时差异。结果：抑郁症患者的平均反应时显著长于对照组；当图片呈现时间为500ms时，抑郁组对负性图片的脱离指数显著大于控制组的脱离指数。结论：抑郁症患者在500ms时对负性图片存在注意脱离困难，而在100ms时则不存在这种注意偏倚特征。     

抑郁症和精神分裂症与正常人群的心理防御机制对照研究

目的：探讨抑郁症和精神分裂症患者的心理防御机制.方法：采用防御机制问卷对30名抑郁症和34名精神分裂症患者进行测试,并分别与30名正常人做对照研究.结果：①抑郁症与精神分裂症患者不成熟的防御机制及其投射、潜意显现、抱怨、幻想、退缩得分明显高于正常对照组,差异有显著性（P＜0.01,或P＜0.05）;②抑郁症组和精神分裂症组患者成熟防御机制得分明显低于正常对照组,差异有显著性（P＜0.01,或P＜0.05）.结论：抑郁症和精神分裂症患者在心理防御机制的使用上与正常人不同,抑郁症和精神分裂症患者均采用防御方式大致相同.     

综合性医院抑郁症的特点及疗效分析

目的:了解综合性医院神经内科门诊抑郁症的临床特点及抗抑郁治疗的临床疗效.方法:对46例抑郁症患者的临床特点进行研究分析,同时对其中24例患者给予帕罗西汀治疗,对汉密尔顿抑郁评定表(HAMD)的平均总分数、汉密尔顿4因子及临床总体印象量表(CGI)评分在治疗前后进行比较.结果:46例抑郁症患者均以不同程度的躯体症状为主诉,临床表现多种多样,可涉及全身各系统;其中24例抑郁症患者给予帕罗西汀治疗后,汉密尔顿抑郁评定表的平均总分数、汉密尔顿4因子及临床总体印象量表评分分数在治疗后较治疗前显著减少(P＜0.01).结论:了解抑郁症患者躯体症状的临床特点,对于抑郁症的诊断和治疗至关重要.帕罗西汀对抑郁症患者具有明显的抗焦虑作用及早期改善睡眠的作用且整体抗抑郁疗效好.     

基于特征向量法和支持向量机的抑郁症脑电信号分类

目的:从抑郁症患者EEG信号中提取与疾病相关的信息以实现对抑郁症患者与健康人的自动分类.方法:用特征向量法对抑郁症患者与健康人脑电进行特征提取,得到脑电信号功率谱幅度的最大值、最小值、平均值和标准偏差等特征参数,然后用支持向量机分类器进行训练和分类,并进行测试验证.结果:相对于用小波变换提取的频率相关参数作为分类特征,采用本文特征向量法功率谱估计提取的特征参数为分类特征的分类器具有更好的分类效果,其抑郁症患者和健康人脑电信号的分类准确率可以达到95.6%.结论:该研究成果为抑郁症疾病的物理诊断提供了一种新的途径.     

不同程度冠脉狭窄对心电及R-R间期序列Lyapun0V指数的影响

目的已有的研究表明,心脏的活动是混沌的。心电时间序列的非线性动力学数值指标可反映心脏的总体动态活动特征。本文试图探讨不同程度冠脉狭窄对心电及R-R间期序列Lyapunov指数的影响。方法通过对不同程度冠脉狭窄的冠心病患者和正常人的海电和R-R间期时间序列的Lyapunov指数计算,以期从医学数据统计中发现有价值的规律性。结果初步研究表明,正常人和冠脉重度狭窄患者心电序列的Lyapunov指数有显著统计差别,但正常人和冠脉轻度狭窄患者心电序列的Lyapunov指数无显著差别;正常人和冠脉轻、重度狭窄患者心电R-R间期时间序列的Lyapunov指数均有显著统计差别。结论R-R间期序列比心电图能提供更多心脏电活动的信息。而正常人和冠脉轻度狭窄患者R-R间期时间序列的Lyapunov指数的显著差别,提示在心电信号的异常波形尚不足以被识别时,可能利用该指数对心脏的健康状况进行早期评估。     

Weighted spherical 1-mean with phase shift and its application in electrocardiogram discord detection

ObjectiveDetecting discords in time series is a special novelty detection task that has found many interesting applications. Unlike the traditional novelty detection methods which can make use of a separate set of normal samples to build up the model, discord detection is often provided with mixed data containing both normal and abnormal data. The objective of this work is to present an effective method to detect discords in unsynchronized periodic time series data.MethodsThe task of discord detection is considered as a problem of unsupervised learning with noise data. A new clustering algorithm named weighted spherical 1-mean with phase shift (PS-WS1M) is proposed in this work. It introduces a phase adjustment procedure into the iterative clustering process and produces a set of anomaly scores based upon which an unsupervised approach is employed to locate the discords automatically. A theoretical analysis on the robustness and convergence of PS-WS1M is also given.ResultsThe proposed algorithm is evaluated via real-world electrocardiograms datasets extracted from the MIT-BIH database. The experimental results show that the proposed algorithm is effective and competitive for the problem of discord detection in periodic time series. Meanwhile, the robustness of PS-WS1M is also experimentally verified. As compared to some of the other discord detection methods, the proposed algorithm can always achieve ideal FScore values with most of which exceeding 0.98.ConclusionThe proposed PS-WS1M algorithm allows the integration of a phase adjustment procedure into the iterative clustering process and it can be successfully applied to detect discords in time series.     

An unsupervised machine learning model for discovering latent infectious diseases using social media data

IntroductionThe authors of this work propose an unsupervised machine learning model that has the ability to identify real-world latent infectious diseases by mining social media data. In this study, a latent infectious disease is defined as a communicable disease that has not yet been formalized by national public health institutes and explicitly communicated to the general public. Most existing approaches to modeling infectious-disease-related knowledge discovery through social media networks are top-down approaches that are based on already known information, such as the names of diseases and their symptoms. In existing top-down approaches, necessary but unknown information, such as disease names and symptoms, is mostly unidentified in social media data until national public health institutes have formalized that disease. Most of the formalizing processes for latent infectious diseases are time consuming. Therefore, this study presents a bottom-up approach for latent infectious disease discovery in a given location without prior information, such as disease names and related symptoms.MethodsSocial media messages with user and temporal information are extracted during the data preprocessing stage. An unsupervised sentiment analysis model is then presented. Users’ expressions about symptoms, body parts, and pain locations are also identified from social media data. Then, symptom weighting vectors for each individual and time period are created, based on their sentiment and social media expressions. Finally, latent-infectious-disease-related information is retrieved from individuals’ symptom weighting vectors.Datasets and resultsTwitter data from August 2012 to May 2013 are used to validate this study. Real electronic medical records for 104 individuals, who were diagnosed with influenza in the same period, are used to serve as ground truth validation. The results are promising, with the highest precision, recall, and F₁ score values of 0.773, 0.680, and 0.724, respectively.ConclusionThis work uses individuals’ social media messages to identify latent infectious diseases, without prior information, quicker than when the disease(s) is formalized by national public health institutes. In particular, the unsupervised machine learning model using user, textual, and temporal information in social media data, along with sentiment analysis, identifies latent infectious diseases in a given location.     

基于心电图的心肌梗死识别分类研究

心肌梗死（MI）是一种严重的心脏病,症状前的健康检查可以发现早期的MI。心电图（ECG）是一种常用的无创健康检查诊断工具。一些使用ECG预测MI的研究存在基于私人数据集、样本量小、分析方法简单等不足。为了解决这些问题,本研究提出在英国最大的开放采集生物信息资源平台UK Biobank上进行MI的首次基准预测实验,涵盖基于临床特征的机器学习方法和基于ECG信号的深度学习方法。结果显示,基于临床特征的AUC为0.690,深度学习使用原始ECG信号的AUC为0.728,提升近4%。证明深度学习基于原始ECG信号能学习到比临床特征更多的信息。另外,对XGBoost和ResNet方法的结果进行了初步的可解释性分析,发现ST波与MI的关联更密切。     

Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder

Anomaly detection has been applied in the various disease of medical practice, such as breast cancer, retinal, lung lesion, and skin disease. However, in real-world anomaly detection, there exist a large number of healthy samples, and but very few sick samples. To alleviate the problem of data imbalance in anomaly detection, this paper proposes an unsupervised learning method for deep anomaly detection based on an improved adversarial autoencoder, in which a module called chain of convolutional block (CCB) is employed instead of the conventional skip-connections used in adversarial autoencoder. Such CCB connections provide considerable advantages via direct connections, not only preserving both global and local information but also alleviating the problem of semantic disparity between the encoding features and the corresponding decoding features. The proposed method is thus able to capture the distribution of normal samples within both image space and latent vector space. By means of minimizing the reconstruction error within both spaces during training phase, higher reconstruction error during test phase is indicative of an anomaly. Our method is trained only on the healthy persons in order to learn the distribution of normal samples and can detect sick samples based on high deviation from the distribution of normality in an unsupervised way. Experimental results for multiple datasets from different fields demonstrate that the proposed method yields superior performance to state-of-the-art methods.

     

Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation

Background

The threat of a global pandemic posed by outbreaks of influenza H5N1 (1997) and Severe Acute Respiratory Syndrome (SARS, 2002), both diseases of zoonotic origin, provoked interest in improving early warning systems and reinforced the need for combining data from different sources. It led to the use of search query data from search engines such as Google and Yahoo! as an indicator of when and where influenza was occurring. This methodology has subsequently been extended to other diseases and has led to experimentation with new types of social media for disease surveillance.

Objective

The objective of this scoping review was to formally assess the current state of knowledge regarding the use of search queries and social media for disease surveillance in order to inform future work on early detection and more effective mitigation of the effects of foodborne illness.

Methods

Structured scoping review methods were used to identify, characterize, and evaluate all published primary research, expert review, and commentary articles regarding the use of social media in surveillance of infectious diseases from 2002-2011.

Results

Thirty-two primary research articles and 19 reviews and case studies were identified as relevant. Most relevant citations were peer-reviewed journal articles (29/32, 91%) published in 2010-11 (28/32, 88%) and reported use of a Google program for surveillance of influenza. Only four primary research articles investigated social media in the context of foodborne disease or gastroenteritis. Most authors (21/32 articles, 66%) reported that social media-based surveillance had comparable performance when compared to an existing surveillance program. The most commonly reported strengths of social media surveillance programs included their effectiveness (21/32, 66%) and rapid detection of disease (21/32, 66%). The most commonly reported weaknesses were the potential for false positive (16/32, 50%) and false negative (11/32, 34%) results. Most authors (24/32, 75%) recommended that social media programs should primarily be used to support existing surveillance programs.

Conclusions

The use of search queries and social media for disease surveillance are relatively recent phenomena (first reported in 2006). Both the tools themselves and the methodologies for exploiting them are evolving over time. While their accuracy, speed, and cost compare favorably with existing surveillance systems, the primary challenge is to refine the data signal by reducing surrounding noise. Further developments in digital disease surveillance have the potential to improve sensitivity and specificity, passively through advances in machine learning and actively through engagement of users. Adoption, even as supporting systems for existing surveillance, will entail a high level of familiarity with the tools and collaboration across jurisdictions.     

An automatic patient-specific seizure onset detection method in intracranial EEG based on incremental nonlinear dimensionality reduction

Epileptic seizure features always include the morphology and spatial distribution of nonlinear waveforms in the electroencephalographic (EEG) signals. In this study, we propose a novel incremental learning scheme based on nonlinear dimensionality reduction for automatic patient-specific seizure onset detection. The method allows for identification of seizure onset times in long-term EEG signals acquired from epileptic patients. Firstly, a nonlinear dimensionality reduction (NDR) method called local tangent space alignment (LTSA) is used to reduce the dimensionality of available initial feature sets extracted with continuous wavelet transform (CWT). One-dimensional manifold which reflects the intrinsic dynamics of seizure onset is obtained. For each patient, IEEG recordings containing one seizure onset is sufficient to train the initial one-dimensional manifold. Secondly, an unsupervised incremental learning scheme is proposed to update the initial manifold when the unlabelled EEG segments flow in sequentially. The incremental learning scheme can cluster the new coming samples into the trained patterns (containing or not containing seizure onsets). Intracranial EEG recordings from 21 patients with duration of 193.8h and 82 seizures are used for the evaluation of the method. Average sensitivity of 98.8%, average uninteresting false positive rate of 0.24/h, average interesting false positives rate of 0.25/h, and average detection delay of 10.8s are obtained. Our method offers simple, accurate training with less human intervening and can be well used in off-line seizure detection. The unsupervised incremental learning scheme has the potential in identifying novel IEEG classes (different onset patterns) within the data.     

Detection of Food Intake from Swallowing Sequences by Supervised and Unsupervised Methods

Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone.     

Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods

OBJECTIVE: In this work, methods utilizing supervised and unsupervised machine learning are applied to analyze radiologically derived morphological and calculated kinetic tumour features. The features are extracted from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) time-course data. MATERIAL: The DCE-MRI data of the female breast are obtained within the UK Multicenter Breast Screening Study. The group of patients imaged in this study is selected on the basis of an increased genetic risk for developing breast cancer. METHODS: The k-means clustering and self-organizing maps (SOM) are applied to analyze the signal structure in terms of visualization. We employ k-nearest neighbor classifiers (k-nn), support vector machines (SVM) and decision trees (DT) to classify features using a computer aided diagnosis (CAD) approach. RESULTS: Regarding the unsupervised techniques, clustering according to features indicating benign and malignant characteristics is observed to a limited extend. The supervised approaches classified the data with 74% accuracy (DT) and providing an area under the receiver-operator-characteristics (ROC) curve (AUC) of 0.88 (SVM). CONCLUSION: It was found that contour and wash-out type (WOT) features determined by the radiologists lead to the best SVM classification results. Although a fast signal uptake in early time-point measurements is an important feature for malignant/benign classification of tumours, our results indicate that the wash-out characteristics might be considered as important.     

SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media

With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., drug indication and beneficial effect) could hold between the drug and adverse event mentions, making ADE relation extraction – distinguishing ADE relationship from other relation types – necessary. However, conducting ADE relation extraction in social media environment is not a trivial task because of the expertise-dependent, time-consuming and costly annotation process, and the feature space’s high-dimensionality attributed to intrinsic characteristics of social media data. This study aims to develop a framework for ADE relation extraction using patient-generated content in social media with better performance than that delivered by previous efforts. To achieve the objective, a general semi-supervised ensemble learning framework, SSEL-ADE, was developed. The framework exploited various lexical, semantic, and syntactic features, and integrated ensemble learning and semi-supervised learning. A series of experiments were conducted to verify the effectiveness of the proposed framework. Empirical results demonstrate the effectiveness of each component of SSEL-ADE and reveal that our proposed framework outperforms most of existing ADE relation extraction methods The SSEL-ADE can facilitate enhanced ADE relation extraction performance, thereby providing more reliable support for pharmacovigilance. Moreover, the proposed semi-supervised ensemble methods have the potential of being applied to effectively deal with other social media-based problems.     

A comparative study for the detection of Mycobacteria by BACTEC MGIT 960, Lowenstein Jensen media and direct AFB smear examination

PURPOSE: To compare BACTEC MGIT 960 (M960) with conventional culture on Lowenstein Jensen (LJ) media and direct acid fast bacilli (AFB) smear examination for the detection of Mycobacteria in clinical samples obtained from suspected cases of pulmonary and extra pulmonary tuberculosis (TB). METHODS: A total of 500 samples were processed for direct AFB smear examination, and culture on M960 and LJ media. RESULTS: Two hundred fifty-eight out of 500 (51.6%) isolates of Mycobacteria were obtained by combined use of the two culture methods. Two hundred and fifty-three (50.6%) were positive in culture by M960 and LJ media and 28% (140/500) by direct AFB smear examination. The positivity rate of M960 system alone was 34.10% (88/258) and of LJ alone was 1.93% (5/258). Average time to detect growth (TTD) was 9.66 days by M960 and 28.81 days by LJ. CONCLUSIONS: M960 system is a rapid and sensitive method for early diagnosis of pulmonary and extrapulmonary TB. But for maximum recovery of Mycobacteria , a combination of both M960 and LJ media should be used.