A mixture of experts network structure for modelling Doppler ultrasound blood flow signals

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.     

数据挖掘在名老中医经验传承的应用现状与智能化趋势

现在,数据挖掘技术正朝着智能化的趋势快速发展,人工智能（AI）在西医学的广泛应用,尤其是AI分析方法在疾病早期诊断与评估、疾病的治疗策略与医疗资源管理这几个方面突出的使用效果,为AI与中医药的结合提供了有价值的参考,尤其在名老中医经验的学习与传承方面。近十余年来,名老中医经验学习与传承领域数据挖掘技术的研究成果颇多,在梳理常用数据挖掘工具与分析方法的基础上,结合AI分析方法在西医学的应用,可以对中医药领域数据挖掘的智能化学习发展方向进行预测与展望,为继承与发展名老中医经验提供更高效的途径,加快中医现代化研究。     

参合农民对新农合定点医疗机构服务质量的评价

目的现场调查参合农民对新农合定点医疗机构服务质量的评价情况,为有关卫生主管部门改进新农合定点医疗机构服务质量提供科学依据.方法随机整群抽样,以问卷方式调查抽取的部分云南省寻甸县参合农民对新农合定点医疗机构服务质量的意见和建议.结果参合农民对各级定点医疗机构医疗技术水平、服务态度、收费、卫生资源配置情况的评价分布差异分别都有统计学意义(P＜0.001).经非条件Logistic回归分析表明,村卫生室虽然设备较差,只有部分需要的药,但医务人员治疗一些常见病的医疗技术水平尚可,距离近,收费低,可信度和满意程度较高,参合农民选择就诊的可能性相应较高.结论各级定点医疗机构服务质量一般,卫生资源配置不合理,县级医疗机构服务态度较差,收费过高.建议有关卫生主管部门大力提高乡村医生的医技水平,合理配置卫生资源,改善县级医疗机构的服务态度,降低并统一新农合定点医疗机构的收费.     

Learning classification models from multiple experts

Building classification models from clinical data using machine learning methods often relies on labeling of patient examples by human experts. Standard machine learning framework assumes the labels are assigned by a homogeneous process. However, in reality the labels may come from multiple experts and it may be difficult to obtain a set of class labels everybody agrees on; it is not uncommon that different experts have different subjective opinions on how a specific patient example should be classified. In this work we propose and study a new multi-expert learning framework that assumes the class labels are provided by multiple experts and that these experts may differ in their class label assessments. The framework explicitly models different sources of disagreements and lets us naturally combine labels from different human experts to obtain: (1) a consensus classification model representing the model the group of experts converge to, as well as, and (2) individual expert models. We test the proposed framework by building a model for the problem of detection of the Heparin Induced Thrombocytopenia (HIT) where examples are labeled by three experts. We show that our framework is superior to multiple baselines (including standard machine learning framework in which expert differences are ignored) and that our framework leads to both improved consensus and individual expert models.     

Expert for Whom? Court-Appointed Versus Party-Appointed Experts

We investigated differences in judges' attitudes between court-appointed and party-appointed forensic psychiatric expert evidence, including who commissioned the report and how this impacted on the judges' perception of the evidence presented. We also investigated the experts' views on being either a party-appointed or court-appointed expert. This survey has no explicit preconceptions. It sought to gather information empirically. The attitudes towards the experts are seen both from the legal professionals' view and from that of the experts themselves. Two groups, one of 107 forensic psychiatric experts (39 psychologists, 65 psychiatrists and three non-specialist medical doctors), and one of 157 legal professionals (44 judges, 74 attorneys and 39 prosecutors) responded to an online survey. We found that 88% of the experts had been appointed by the court and of these, 73% preferred to be court-appointed. Fifty-one per cent had also been party-appointed and the most common principal was the defence attorney (46%). Sixty-two per cent of the judges who had an opinion stated that the party-appointed experts did not assist the court. This indicates that the judges clearly preferred independent court-appointed experts.     

Expert Tool Users Show Increased Differentiation between Visual Representations of Hands and Tools

The idea that when we use a tool we incorporate it into the neural representation of our body (embodiment) has been a major inspiration for philosophy, science, and engineering. While theoretically appealing, there is little direct evidence for tool embodiment at the neural level. Using functional magnetic resonance imaging (fMRI) in male and female human subjects, we investigated whether expert tool users (London litter pickers: n = 7) represent their expert tool more like a hand (neural embodiment) or less like a hand (neural differentiation), as compared with a group of tool novices (n = 12). During fMRI scans, participants viewed first-person videos depicting grasps performed by either a hand, litter picker, or a non-expert grasping tool. Using representational similarity analysis (RSA), differences in the representational structure of hands and tools were measured within occipitotemporal cortex (OTC). Contrary to the neural embodiment theory, we find that the experts group represent their own tool less like a hand (not more) relative to novices. Using a case-study approach, we further replicated this effect, independently, in five of the seven individual expert litter pickers, as compared with the novices. An exploratory analysis in left parietal cortex, a region implicated in visuomotor representations of hands and tools, also indicated that experts do not visually represent their tool more similar to hands, compared with novices. Together, our findings suggest that extensive tool use leads to an increased neural differentiation between visual representations of hands and tools. This evidence provides an important alternative framework to the prominent tool embodiment theory.SIGNIFICANCE STATEMENT It is commonly thought that tool use leads to the assimilation of the tool into the neural representation of the body, a process referred to as embodiment. Here, we demonstrate that expert tool users (London litter pickers) neurally represent their own tool less like a hand (not more), compared with novices. Our findings advance our current understanding for how experience shapes functional organization in high-order visual cortex. Further, this evidence provides an alternative framework to the prominent tool embodiment theory, suggesting instead that experience with tools leads to more distinct, separable hand and tool representations.     

Joint ESH excellence centers' national meeting on renal sympathetic denervation: A Greek experts’ survey

ObjectiveThe efficacy of renal sympathetic denervation (RDN) has been affirmed by a number of recent clinical studies, despite controversies in this field over the last five years. Therefore, it is of paramount importance that hypertension experts debate the merits of RDN by revealing and expressing their personal beliefs and perspectives regarding this procedure.MethodsA cross-sectional survey was conducted among Greek leaders of the Hypertension Excellence Centers with the use of a closed-type questionnaire specifically designed to elicit information and evaluate the respondent's views and perspectives about RDN efficacy, safety and ideal target patient population.ResultsA total of 36 participants completed the survey. Based on the results, RDN was considered efficient (91.7%) and safe (94.5%), while the overwhelming majority of the participants felt confident in the long-term efficacy (88.9%) of the intervention and that it lacks reliable predictors of blood pressure response (94.5%). Patients with resistant (91.7%), ultra-resistant (94.4%), and uncontrolled hypertension (80.6%) were suggested as ideal candidates for RDN. Establishing a close co-operation between interventionalists and hypertension experts was considered essential to ensure the efficacy (97.2%) as well as the safety (97.3%) of the procedure.ConclusionThe vast majority of Greek hypertension experts surveyed were convinced of the efficacy and safety of RDN based on the preponderance of available scientific and clinical data. Identification of the ideal patient group remains controversial. Respondents generally agreed on the necessity of building close collaborative relationships between interventionalists and hypertension experts in order to improve RDN clinical outcome.     

影响住院医师规范化培训开展的因素分析

目的 探讨影响住院医师规范化培训开展的相关因素。方法 本文选取解放军总参谋部总医院住院医师规范化培训的相关数据,以到位率为参考,研究影响培训开展的因素。结果 通过回顾性统计分析,发现培训时间、院方政策、外请专家等与培训的到位率有关。结论 住院医师规范化培训的培训时间、院方政策、外请专家等对顺利开展培训、提高培训质量有潜在的影响。