支持向量机法及其在中药研究中的应用

在中医药现代化进程中，有效地结合现代科学技术手段对传统中医药进行更为合理的分析是一个重要研究方向。计算机科学技术与人工智能算法在中医药研究领域已取得长足发展，尤其是分类算法所具有的快速鉴别和分析能力，使其在复杂的中医药研究体系中发挥举足轻重的作用。对支持向量机分类算法及其结合其他算法在解决中医药相关领域问题方面及成果进行综述，以相关智能算法为基础探讨中医药研究过程中的诸多难题，为推动中医药现代化进程提供支撑。     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

经鼻导管高流量吸氧与经鼻气道正压通气在重症毛细支气管炎呼吸支持中的应用价值比较

目的：比较经鼻导管高流量吸氧（HFNC）与经鼻气道正压通气（nCPAP）在重症毛细支气管炎呼吸支持中的应用价值，为临床治疗方案的选择提供参考。方法：选取2016年12月至2018年12月我院儿科收治的重症毛细支气管炎患儿90例，采用随机数字表法分为观察组和对照组各45例。两组患儿入院后均给予常规综合治疗以保证呼吸道通畅，在此基础上观察组采用HFNC治疗，对照组采用nCPAP治疗，比较两组患儿治疗前和治疗24 h后呼吸频率、经皮血氧饱和度（TcSO2）、呼吸窘迫评分体系（CSS）评分、动脉血氧分压（PaO2）等呼吸相关指标及治疗前后临床症状体征改善情况。结果：两组患儿治疗24 h后呼吸频率、CSS评分均降低，且观察组降低程度更大，TcSO2、PaO2于治疗24 h后升高，观察组升高幅度较对照组明显；治疗后两组患儿咳嗽及肺部湿啰音、肺部炎症情况均改善，观察组症状体征消失时间早于对照组，差异均有统计学意义（P<0.05）。结论：重症毛细支气管炎患儿采用HFNC治疗可明显改善通气功能和临床症状，治疗效果优于nCPAP治疗，可扩大样本量进一步观察。     

Stitching the fabric back together: child fostering attitudes in the transition to a chronic HIV epidemic

There are an estimated 56 million orphans and vulnerable children across sub-Saharan Africa. Communities typically care for orphan children through informal caring arrangements – either within or outside of kinship networks. Within Kenya, an estimated 250,000 children live on the streets. There is less research related to fostering attitudes of this special population than orphans and vulnerable children generally. Important research over the past decade has illuminated multiple ways in which children are made more vulnerable because of HIV, including parental death and street-migration from HIV-affected households. As HIV transitions from a terminal illness to a chronic, manageable one, research is also required to establish how parents living with HIV can be an asset to children. In this study, we assess whether mothers living with HIV were very willing to foster biologically-related children, and street-involved children, how these fostering attitudes differed from mothers not living with HIV, and whether differences in fostering attitudes by reported HIV status were mediated by social support, family functioning and general self-rated health. Approximately 40% of mothers living with HIV were very willing to provide long-term foster care to biologically-related or street-involved children. This was less than the percentage of mothers not living with HIV, who were very willing to foster biologically-related children (61%) or street-involved children (58%). Significant portions of these differences were explained by social support, family functioning and general self-rated health. Multi-sectoral approaches are suggested by these findings in order to improve the child-fostering capacity of mothers living with HIV. Improving social support, family functioning and general self-rated health among HIV-infected mothers may not only provide protective benefits for the mothers and their children, but also expand the community’s capacity to care for orphan and vulnerable children.     

Effects of exogenous factors on spatial accuracy in neurosurgery

The study aimed to assess the effect of exogenous factors such as surgeon posture, surgical instrument length, fatigue after a night shift, exercise and caffeine consumption on the spatial accuracy of neurosurgical manipulations. For the evaluation and simulation of neurosurgical manipulations, a testing device developed by the authors was used. The experimental results were compared using nonparametric analysis (Wilcoxon test) and multivariate analysis, which was performed using mixed models. The results were considered statistically significant at p < 0.05. The study included 11 first-year neurosurgery residents who met the inclusion criteria. Hand support in the sitting position (Wilcoxon test p value = 0.0033), caffeine consumption (p = 0.0058) and the length of the microsurgical instrument (p = 0.0032) had statistically significant influences on the spatial accuracy of surgical manipulations (univariate analysis). The spatial accuracy did not significantly depend on the type of standing position (Wilcoxon test p value = 0.2860), whether the surgeon was standing/sitting (p = 0.1029), fatigue following a night shift (p = 0.3281), or physical exertion prior to surgery (p = 0.2845).When conducting the multivariate analysis, the spatial accuracy significantly depended on the test subject (p < 0.0001), the use of support during the test (p = 0.0001), and the length of the microsurgical instrument (p = 0.0397). To increase the spatial accuracy of microsurgical manipulations, hand support and shorter tools should be used. Caffeine consumption in high doses should also be avoided prior to surgery.     

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.