葛兰素史克GSK-201401-32nd Annual JP Morgan Healthcare Conference_By_CFO.pdf

蛋白质纯化手册(GE_Healthcare_中文版)

交互式脚手架 目标是准备一个工具集，以便在 Kinja 上快速轻松地创建用于编辑目的的交互式 Web 元素。 首先， ，然后执行以下操作： # Checkout repository git clone git@github.com:adampash/gulp-starter.git cd gulp-starter # Remove old git repo, rename folder to , # and push to new repo . ./new_project.sh 接下来，为这个项目安装任何新的依赖项，如下所示： # e.g., to install d3 npm install d3 --save-dev 大口开胃菜 Starter Gulp + Browserify 项目以及如何完成一些常见任务和工作流程的示

Stroke_Prediction_6ML_models 该项目使用六个机器学习模型（XGBoost，随机森林分类器，支持向量机，逻辑回归，单决策树分类器和TabNet）进行笔画预测。 为此，我使用了Kaggle的“ healthcare-dataset-stroke-data”。 为了确定哪种模型最适合进行笔画预测，我绘制了每种模型的曲线下面积（AUC）。 AUC越高，模型越好

使用卫生保健数据预测抑郁 作者：Vivienne DiFrancesco 可以在找到用于探索该项目中使用的数据的配套仪表板 该存储库的内容是对使用机器学习模型来预测使用医疗保健数据的人的抑郁症的分析。 希望可以使这项工作易于访问和复制，因此对这种分析进行了详细说明。 储存库结构 README.md：此项目审阅者的顶级自述文件 first_notebook.ipynb：从数据清理阶段开始在jupyter笔记本中进行分析的叙述性文档 second_notebook.ipynb：在项目的探索阶段清理数据之后开始的叙述性文档的延续 PredictingDepressionSlides.pdf：项目演示幻灯片的PDF版本 project_functions文件夹：包含编写用于first_notebook和second_notebook的自定义函数 仪表板文件夹：包含用于创建此项目的配套仪表板的文件

This book is a wake-up call explaining how to detect and prevent the hacking of medical equipment at hospitals and healthcare facilities. The vulnerability of the medical equipment inside the hospital to cyber-attacks far eclipses the actual building equipment. A cyber-physical attack on building equipment pales in comparison to the damage a determined hacker can do if he/she gains access to a medical grade network. A medical grade network controls the diagnostic, treatment and life support equipment on which lives depend. Recent news reports how hackers struck hospitals with ransomware that prevented staff from accessing patient records or scheduling appointments. Unfortunately, medical equipment can also be hacked and shut down remotely as a form of extortion. Criminal hackers will not ask for a $500 payment to unlock an MRI, PET, CT Scan, or X-Ray machine—they will ask for much more. Litigation is bound to follow and the resulting punitive awards will drive up hospital insurance costs and healthcare costs in general. This will undoubtedly result in increased regulations for hospitals and higher costs for compliance. Unless hospitals and other healthcare facilities take the steps necessary now to secure their medical grade networks, they will be targeted for cyber-physical attack, possibly with life-threatening consequences. Cybersecurity for Hospitals and Healthcare Facilities shows what hackers can do, why hackers would target a hospital, the way they research a target, ways they can gain access to a medical grade network (cyber-attack vectors), and ways they hope to monetize their cyber-attack. By understanding and detecting the threats, hospital administrators can take action now – before their hospital becomes the next victim. Table of Contents Chapter 1: Hacker Reconnaissance of a Hospital Network Chapter 2: How Hackers Gain Access to a Healthcare Facility or Hospital Network Chapter 3: Active Medical Device Cyber-Attacks Chapter 4: Medical Facility Cybe

来自斯坦福大学和 Google Research 的研究者对医疗领域中的深度学习应用进行了综述，并将研究文章发表在《Nature Medicine》上。这篇文章从应用于医疗行业的计算机视觉、自然语言处理、强化学习和通用方法入手，详细介绍了深度学习在医疗中的应用。