DOTmed Home MRI Oncology Ultrasound Molecular Imaging X-Ray Cardiology Health IT Business Affairs
News Home Parts & Service Operating Room CT Women's Health Proton Therapy Endoscopy HTMs Pediatrics
SEARCH
当前地点:
>
> This Story


注册记数器 to rate this News Story
Forward Printable StoryPrint Comment
advertisement

 

advertisement

 

HTMs Homepage

How can OEMs and in-house service teams work better together? Experts from both sides get together and share their views at AAMI

Understanding the value of data analytics in HTM Helps with purchasing, keeping inventory of equipment, end-of-life decisions for devices

EQ2 showcases advancements and new products at AAMI Exchange Added data analytics, supply chain management and tracking capabilities

GE adds multi-vendor parts and new functions to online Service Shop Showcased at AAMI Exchange

Tips for creating better collaboration between HTM and IT Streamlining these increasingly complex partnerships

AAMI Product Showcase A sneak peek at some of the products to check out on the show floor

Clinical engineering and the science of the capital budget process Purchasing insights from the experts at MD Buyline

Q & A with Robert Jensen, president and CEO of AAMI Find out what to expect at AAMI Exchange, the premier event for the HTM community

Barriers to genuine service collaboration with OEMs are hurting hospitals A call to better-align objectives toward value-based care

Testing equipment continues to advance Managing your systems and scanners requires the right tools for the job

Michael Garel discusses using machine
learning and data analytics to clean
data and process work orders faster
>

Understanding 'data cleaning' in equipment service, and the tools used to do it

John R. Fischer , Staff Reporter
More than 50 percent of a data scientist’s time is spent cleaning data, according to a Cloudflower 2017 Data Scientist Report.

Michael Garel, director of data strategy for Accruent, gave a presentation at AAMI Exchange in Cleveland this weekend, in which he argued that estimate is actually "quite low."

Story Continues Below Advertisement

THE (LEADER) IN MEDICAL IMAGING TECHNOLOGY SINCE 1982. SALES-SERVICE-REPAIR

Special-Pricing Available on Medical Displays, Patient Monitors, Recorders, Printers, Media, Ultrasound Machines, and Cameras.This includes Top Brands such as SONY, BARCO, NDS, NEC, LG, EDAN, EIZO, ELO, FSN, PANASONIC, MITSUBISHI, OLYMPUS, & WIDE.



“From what I’ve seen, data scientists spend most, if not 80 or 90 percent, of their time cleaning data,” he said. “Everybody thinks this data scientist role is the greatest ever. It’s really processing a lot of data.”

Cleaning data refers to the process of detecting and correcting corrupt or inaccurate records so that the true analytic insight can shine through the information.

With over 500 million work orders — more than 230 million in healthcare — Accruent utilizes a number of tools in data analytics, machine learning and deep learning to clean data and uncover insights for completing hospital equipment work orders more efficiently. Which tools to use comes down to what information the user is trying to uncover, the type of work order and the variables involved.

In his presentation, entitled Big Data Insights on Capital Equipment from 500 Million Work Orders, Garel examined specific uses and scenarios that a few of these tools are best suited for addressing:

Data Analytics
Data science is the ability to comprehend and process data, and to extract value from it, visualize it and communicate it. Applying data analytics can be helpful for this, depending on the type of scenarios users are faced with.

  • Descriptive analytics – Describes what has happened in the past to understand current conditions, and visualize and communicate insights extracted from data to peers or management.

  • Predictive analytics – Predicts what will happen in the future. This form is inherently probabilistic in nature and utilizes historical data to anticipate future performance, events and results.

  • Prescriptive analytics – Maps out recommendations for next steps to achieve objectives and goals.

Machine Learning
Machine learning is data analysis that automates analytical model building. While most software requires training on where to look, the aim of this technology is to uncover hidden insights without explicitly being programmed where to search. It can instead learn from data, by identifying patterns and by making predictions.

  • Supervised Learning – Utilizing tagged data, the machine is trained to identify features in select images and applied to identify them in images not used in their training. This is especially helpful for risk assessment, fraud detection, and image and speech recognition.

  Pages: 1 - 2 >>

HTMs Homepage


You Must Be Logged In To Post A Comment

Advertise
提升您的品牌知名度
拍卖+私人销售
获得最好的价格
买设备/配件
找到最低价格
每日新闻
阅读最新信息
目录
浏览所有的DOTmed用户
DOTmed上的伦理
查看我们的伦理计划
金子分开供营商节目
接收PH要求
金子服务经销商节目
接收请求
提供保健服务者
查看所有的HCP(简称医疗保健提供商)的工具
Jobs/训练
查找/申请工作
Parts Hunter +EasyPay
获取配件报价
Recently Certified
查看最近通过认证的用户
Recently Rated
查看最近通过认证的用户
出租中央
租用设备优惠
卖设备/配件
得到最划算
Service Technicians Forum
查找帮助和建议
简单的征求建议书
获取设备报价
真正商业展览
查找对设备的服务
对这个站点的通入和用途是受期限和条件我们支配 法律公告 & 保密性通知
物产和业主对 DOTmed.com,公司 Copyright ©2001-2019 DOTmed.com, Inc.
版权所有