Clinical decision support in the neonatal ICU

Clinical Decision Support (CDS) tools help the healthcare team diagnose, monitor, and treat patients more efficiently and consistently by executing clinical practice guidelines and recommendations. As a result, CDS has a direct impact on the delivery and healthcare outcomes. This review covers the fundamental concepts, as well as the infrastructure needed to create a CDS tool and examples of its use in the neonatal setting. This article also serves as a primer on what to think about when proposing the development of a new CDS tool, or when upgrading an existing one. We also highlight important elements that influence CDS development, such as informatics methodologies, data and device interoperability, and regulation.     

(The null) Importance of police experience on intuitive credibility of people with intellectual disabilities

In the present study, the intuitive ability of police to discriminate between real and false statements of people with mild and moderate (IQ range = 50–80, average = 60.0) intellectual disabilities (ID) was analyzed. The assessments issued by groups with different levels of experience in police techniques (psychology students, and police officers) were compared. The results showed no differences between the two groups in their ability to discriminate (d′ = 0.785 and d′ = 0.644, respectively). When the experience of the police was taken into consideration, no differences were found between “experienced” and “novice” police officers (d′ = 0.721 and d′ = 0.582, respectively). No differences were found in response criteria, which were neutral in all cases. Moreover, 34.73% of cases evaluated by the inexperienced group were incorrectly discriminated, in comparison to the 37.75% of incorrect assessments made by police. The implications of the limited ability of intuition to discriminate between real and simulated victims with ID, which did not yield significant differences between experienced and inexperienced assessors in obtaining and assessing statements, are discussed. In light of the results of this study, it is concluded that adequate resources and standardized procedures to properly address people with ID who come into contact with the police and judicial institutions need to be provided.     

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks.     

Resting‐state glutamatergic neurotransmission is related to the peak latency of the auditory mismatch negativity (MMN) for duration deviants: An 1H‐MRS‐EEG study

Mismatch negativity (MMN), an ERP elicited by a deviant stimulus in a train of standard stimuli, has been suggested to be associated to glutamatergic neurotransmission, mediated by glutamatergic NMDA receptors. In this study, we examined the relationship between interindividual variation of ¹H‐MRS‐measured glutamate+glutamine (Glx) in the superior temporal gyrus and MMN for duration and frequency deviants in 19 healthy young adults (9 male). We found a significant relationship between the peak latency of the duration‐MMN peak and creatine‐scaled Glx (p = .0003, η² = .43), with increased Glx level being associated to earlier peak of the duration‐MMN (r = ?.63). In contrast, the amplitude of the duration‐MMN was not related to Glx. There was no significant relationship between Glx and the frequency‐MMN. The present study is the first to demonstrate that interindividual variation in the glutamatergic neurotransmission affects the MMN response in healthy individuals.     

Automatic Identification of Web-Based Risk Markers for Health Events

Background

The escalating cost of global health care is driving the development of new technologies to identify early indicators of an individual’s risk of disease. Traditionally, epidemiologists have identified such risk factors using medical databases and lengthy clinical studies but these are often limited in size and cost and can fail to take full account of diseases where there are social stigmas or to identify transient acute risk factors.

Objective

Here we report that Web search engine queries coupled with information on Wikipedia access patterns can be used to infer health events associated with an individual user and automatically generate Web-based risk markers for some of the common medical conditions worldwide, from cardiovascular disease to sexually transmitted infections and mental health conditions, as well as pregnancy.

Methods

Using anonymized datasets, we present methods to first distinguish individuals likely to have experienced specific health events, and classify them into distinct categories. We then use the self-controlled case series method to find the incidence of health events in risk periods directly following a user’s search for a query category, and compare to the incidence during other periods for the same individuals.

Results

Searches for pet stores were risk markers for allergy. We also identified some possible new risk markers; for example: searching for fast food and theme restaurants was associated with a transient increase in risk of myocardial infarction, suggesting this exposure goes beyond a long-term risk factor but may also act as an acute trigger of myocardial infarction. Dating and adult content websites were risk markers for sexually transmitted infections, such as human immunodeficiency virus (HIV).

Conclusions

Web-based methods provide a powerful, low-cost approach to automatically identify risk factors, and support more timely and personalized public health efforts to bring human and economic benefits.     

中文专利文档关键词自动提取方法研究进展

专利是一种包含学术、商业、法律等信息的科技文献,记录了大量新颖、实用的研究成果,近年来关注度不断提高。利用共词分析、文本聚类等方法对专利文献进行信息分析时,往往需要借助关键词提取技术达到降低数据复杂度、过滤噪声的目的。关键词提取技术多数基于统计规律。本文对基于词频、关联信息和多特征的关键词提取方法研究进展进行了总结,介绍了常用的分别以TF-IDF、熵、词汇链、TextRank、遗传算法、决策树学习、朴素贝叶斯分类器、支持向量机等为主导的方法。另外,本文还总结了在专利文档关键词提取中可能用到的词频、位置、语义、关联、自身等方面的特征。实际应用中,关键词自动提取技术可作为一种有力的辅助手段,降低数据处理过程中的人力和时间成本。     

医学生的学习动机状况与家庭硬环境状况及其相关性分析

目的 探讨医学生的学习动机状况,分析其与家庭硬环境的相关性,为制定有针对性的医学生教学策略提供科学依据.方法 选取广州医科大学的本科生为对象,通过随机抽样的方法,共抽取1080个学生.利用学习动机量表,对医学生的家庭硬环境与学习动机进行评价和相关分析.结果 医学生的学习动机外生动机分数为(22.850 ±0.201)分;内生动机平均分为(27.030 ±0.218)分.“自评家庭经济情况很好”的学生的外生动机得分最高为(27.000 ±1.421)分,“对本专业很喜欢”的学生内生动机得分最高为(30.850±0.548)分,差异有统计学意义(P＜0.05);母亲学历与外生动机分数及内生动机分数的差异均有统计学意义(P＜0.05);学习动机与家庭硬环境的关联性分析得出自评家庭经济情况与外生动机分数呈负相关(r=-0.122,P＜0.05),与内生动机分数呈负相关(r=-0.145,P＜0.05).结论 医学生的总体学习动机状况一般,家庭硬环境越好,医学生的学习动机水平越高.     

High‐dimensional propensity score algorithm in comparative effectiveness research with time‐varying interventions

The high‐dimensional propensity score (hdPS) algorithm was proposed for automation of confounding adjustment in problems involving large healthcare databases. It has been evaluated in comparative effectiveness research (CER) with point treatments to handle baseline confounding through matching or covariance adjustment on the hdPS. In observational studies with time‐varying interventions, such hdPS approaches are often inadequate to handle time‐dependent confounding and selection bias. Inverse probability weighting (IPW) estimation to fit marginal structural models can adequately handle these biases under the fundamental assumption of no unmeasured confounders. Upholding of this assumption relies on the selection of an adequate set of covariates for bias adjustment. We describe the application and performance of the hdPS algorithm to improve covariate selection in CER with time‐varying interventions based on IPW estimation and explore stabilization of the resulting estimates using Super Learning. The evaluation is based on both the analysis of electronic health records data in a real‐world CER study of adults with type 2 diabetes and a simulation study. This report (i) establishes the feasibility of IPW estimation with the hdPS algorithm based on large electronic health records databases, (ii) demonstrates little impact on inferences when supplementing the set of expert‐selected covariates using the hdPS algorithm in a setting with extensive background knowledge, (iii) supports the application of the hdPS algorithm in discovery settings with little background knowledge or limited data availability, and (iv) motivates the application of Super Learning to stabilize effect estimates based on the hdPS algorithm. Copyright © 2014 John Wiley & Sons, Ltd.