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.     

Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.     

Development of a Simulation-Based Mastery Learning Curriculum for Breaking Bad News

Introduction

Physician communication impacts patient outcomes. However, communication skills, especially around difficult conversations, remain suboptimal, and there is no clear way to determine the validity of entrustment decisions. The aims of this study were to 1) describe the development of a simulation-based mastery learning (SBML) curriculum for breaking bad news (BBN) conversation skills and 2) set a defensible minimum passing standard (MPS) to ensure uniform skill acquisition among learners.

亚慢性硝酸钇暴露对雌性大鼠学习记忆能力的影响

目的：观察稀土化合物硝酸钇[Y（NO₃）₃]亚慢性（90 d）暴露对大鼠学习记忆能力的影响，并探究其可能的机制，为全面评估稀土元素钇的健康风险提供科学依据。方法：选用刚离乳（PND21）SD雌性大鼠，根据质量随机分为4组，分别为对照组（ddH₂O），Y（NO₃）₃低剂量组[10 mg/（kg·d）]、中剂量组[40 mg/（kg·d）]和高剂量组[160 mg/（kg·d）]，每组15只。连续灌胃受试物90 d后进行旷场实验、高架十字迷宫实验、转棒实验、Morris水迷宫实验。水迷宫检测后，每组取5只雌鼠心脏原位灌注，进行脑组织病理学检查。每组剩余10只雌鼠摘取脑组织，紫外分光光度计检测雌鼠大脑皮质和海马中谷氨酸（Glu）的含量；Western blot检测海马组织中N-甲基-D-天冬氨酸（NMDA）受体蛋白表达情况。结果：与对照组相比，硝酸钇90 d暴露后，转棒实验中160 mg/（kg·d）剂量组大鼠在棒时间、在棒圈数、掉落速度明显升高（P<0.05）。在Morris水迷宫定位航向实验第4天时，40、160 mg/（kg·d）剂量组大鼠逃避潜伏期明显降低（P<0.05）；Morris水迷宫空间探索实验中，40 mg/（kg·d）剂量组大鼠穿越平台次数、目标象限游泳时间明显增加（P<0.05）；160 mg/（kg·d）剂量组穿越平台次数、进入目标象限次数、目标象限游泳时间明显增加（P<0.05）；160 mg/（kg·d）剂量组大鼠东北、东南、西南象限的逃避潜伏期明显低于西北象限的逃避潜伏期（P<0.05）。160 mg/（kg·d）剂量组大鼠海马中Glu含量和NMDA受体NR1含量均明显低于对照组（P<0.05）。40和160 mg/（kg·d）剂量组大鼠海马中NMDA受体NR2A含量明显低于对照组（P<0.05）。结论：硝酸钇亚慢性（90 d）暴露可以引起雌性大鼠空间学习记忆能力增强；硝酸钇可能通过降低海马组织神经元细胞外Glu含量，抑制NMDA受体激活，增强雌性大鼠的空间学习记忆能力。     

慕课在医学生假期课前预习中的应用探讨

高校医学生会利用假期时间对将要学习的科目进行预习,力求提前了解或掌握,以利于日后对知识的充分理解。预习对于医学科目学习有一定必要性,然而传统的假期预习模式常存在缺乏引导、效率难以提升等问题。目前,慕课在医学教育领域的应用有所发展。对于医学生,其自主学习的模式、课程设计等契合医学课程的预习需求和假期学习的特点,有助于提升医学生的学习能力和假期预习效果。医学生将慕课作为假期预习资源可有多种学习模式选择。慕课平台的功能拓展和学校教师的辅助可以有效深入慕课在医学生假期预习中的应用,为医学生带来更合理的假期学习安排。