Research and applications: Learning regular expressions for clinical text classification

Objectives

Natural language processing (NLP) applications typically use regular expressions that have been developed manually by human experts. Our goal is to automate both the creation and utilization of regular expressions in text classification.

Methods

We designed a novel regular expression discovery (RED) algorithm and implemented two text classifiers based on RED. The RED+ALIGN classifier combines RED with an alignment algorithm, and RED+SVM combines RED with a support vector machine (SVM) classifier. Two clinical datasets were used for testing and evaluation: the SMOKE dataset, containing 1091 text snippets describing smoking status; and the PAIN dataset, containing 702 snippets describing pain status. We performed 10-fold cross-validation to calculate accuracy, precision, recall, and F-measure metrics. In the evaluation, an SVM classifier was trained as the control.

Results

The two RED classifiers achieved 80.9–83.0% in overall accuracy on the two datasets, which is 1.3–3% higher than SVM''s accuracy (p<0.001). Similarly, small but consistent improvements have been observed in precision, recall, and F-measure when RED classifiers are compared with SVM alone. More significantly, RED+ALIGN correctly classified many instances that were misclassified by the SVM classifier (8.1–10.3% of the total instances and 43.8–53.0% of SVM''s misclassifications).

Conclusions

Machine-generated regular expressions can be effectively used in clinical text classification. The regular expression-based classifier can be combined with other classifiers, like SVM, to improve classification performance.     

全基因组寡核苷酸芯片筛选与胰腺癌相关的基因信号通路

背景：胰腺癌的发病率有所上升,且缺乏诊断标记物和治疗措施。近年研究表明,基因信号通路在胰腺癌发病中起重要作用。目的：探讨与胰腺癌相关的基因信号通路。方法：6例经病理证实的胰腺癌及其癌旁组织纳入研究。抽提总RNA．合成两种组织探针,探针荧光标记和纯化后,与Agilent全基因组寡核苷酸芯片进行杂交,对差异基因进行生物信息学分析,筛选与胰腺癌相关的信号通路。结果：差异基因的KEGG Pathway分析发现肾细胞癌通路分类对胰腺癌最具生物学意义,其中TGFβ3、EPASI、PIK3R3、EGLN1、PGF、ETS1、VEGFB、CREBBP和PIK3R5九个关键基因的表达有显著差异（P〈0．05）。结论：胰腺癌发病与肾细胞癌通路激活密切相关,可能为胰腺癌的研究提供新的思路。     

述情障碍对身份相关表情后遗效应影响

目的 探讨述情障碍对身份相关表情后遗效应影响.方法 使用多伦多述情障碍评定量表(TAS-20)在大学生中筛选出高述情障碍者和低述情障碍者各20人,要求被试完成身份相关的表情后遗效应测试.结果 ①述情障碍高分组与低分组在无适应性干扰实验中差异无统计学意义(F=2.1,P=0.15);②述情障碍高分组与低分组在表情后遗效应...     

Amélioration de la reconnaissance et de l’imitation d’expressions faciales chez des enfants autistes grâce à une présentation visuelle et sonore ralentie

An increasing amount of studies have demonstrated the existence of visual, auditive and motion integration deficit in individuals with autism, especially when movement is rapid. Since visual motion is intrinsically involved in social interactions through movements of the body and face, visual motion processing deficit could therefore account for their verbal and non-verbal comprehension and social interaction disorders. Besides, other studies have revealed the existence of speech integration deficit in this population which could also account for their verbal comprehension deficits. The aim of our study is to test whether slowing down sound flow and facial movement enhance facial recognition and imitation by autistic children compared to normal control children. Results show that a slow dynamic presentation enhances recognition and imitation of facial expressions by autistic children. Our perspective is to elaborate software that could simultaneously slow down facial movements and speech flow, and to assess its impact on the comprehension of words and instructions by autistic children.     

脑胶质瘤P53,PKC,VEGF的表达及与预后关系

目的 了解脑胶质瘤中Ｐ５３、ＰＫＣ、ＶＥＧＦ阳性表达与脑胶质瘤分级和预后的关系。方法 采用免疫组化ＳＡＢＣ法观察Ｐ５３、ＰＫＣ、ＶＥＧＦ在脑胶质瘤中的表达；同时，随访了解患者生存情况。结果Ｐ５３、ＰＫＣ、ＶＥＧＦ在ＢＧ、ＭＧ、ＨＭＧ三组中都有不同程度的表达，而且，随着病理分级的增加，其表达率也逐渐增加，但各组间缺乏统计学差异，三者表达在脑胶质瘤中的意义有待进一步宗病例的分析。Ｐ５３、ＰＫＣ、ＶＥＧ