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Data Science

World Health Organization Releases ICD-11

SCR Data Science - Tue, 2018-06-26 08:42
Medical Photo

“Picture.” by RawPixels via Unsplash, March 18, 2018, CCO.

The World Health Organization has released the newest version of the International Classification of Diseases, ICD-11.  The ICD tracks health trends and statistics globally.  The nearly 55,000 unique codes identify injuries, diseases, symptoms, and causes of death.  These codes are the common language that health care professionals use to share information worldwide.

This new version of ICD has been in progress for several years and involved a large team of contributors.  Due to the scope of the project, it will not start being used until 2022.  This will allow time for users to familiarize themselves with the new product and prepare for implementation.

One new feature that is being touted as user friendly is a fully electronic version of the product which is a first for ICD.  There are also new chapters that include traditional medicine and sexual health.  The sexual health chapter is most notable for reclassifying transgender so that is no longer a mental health condition.  Another well publicized addition to ICD-11 is gaming disorder is now listed as an addictive disorder.

WHO’s Assistant Director-General for Health Metrics and Measurement, Dr Lubna Alansari, says: “ICD is a cornerstone of health information and ICD-11 will deliver an up-to-date view of the patterns of disease.”

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Categories: Data Science

New Research Looks at Long-Term Impact of Tonsillectomies

SCR Data Science - Tue, 2018-06-19 10:20

“Tonsillitis.” via MedlinePlus.gov, April 11, 2017, Public Domain.

When I was in grade school, it seemed as if nearly every kid would miss a week of school to have their tonsils removed. They would return to school bragging about their recovery spent eating ice cream, drinking milkshakes, and watching cartons.  I can almost acutely recall being jealous of these classmates.  After reading new research that evaluates the long-term health risks of tonsillectomies, I realized maybe I shouldn’t have been quite so jealous!

Tonsils are located at the back of the throat. These are knobs of tissue with one located on either side.  Tonsils are part of the lymphatic system which works to clear infections and keep the balance between body fluids.  Specifically, the tonsils, in concert with the adenoids, work by preventing germs from coming in through the mouse and nose.

A tonsillectomy is a procedure to remove the tonsils. This is typically recommended for those that suffer from recurrent infections of the tonsils or when the tonsils are enlarged enough that they obstruct breathing.  For adults, the tonsils are occasionally removed when there is concern for a tumor.

Over half a million tonsillectomies are performed annually in the United States but little research has been done to determine the long-term health risks associated with this procedure. A new study released by the University of Melbourne is the first to look at potential risks.  Their results suggest that individuals who undergo a tonsillectomy are at 3x the risk of their counterparts for diseases of the upper respiratory tract such as asthma, influenza, pneumonia and chronic obstructive pulmonary disease – COPD.

Read the entire study findings to learn more.

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Categories: Data Science

June PNR Rendezvous webinar next week

PNR Data Science - Wed, 2018-06-13 07:41

The next PNR Rendezvous monthly webinar is coming up.

Session title: Unlocking the Potential of De-identified Clinical Datasets

Presenter: Bas de Veer, Bio-Medical Informatics Services Manager for UW Medicine IT Services

When: Wednesday, June 20 starting at 1:00pm PT, Noon Alaska Time, 2:00pm MT

Healthcare systems generate a ton of data on a daily basis. The primary purpose of this data is billing and clinical decision making. But great secondary use of this data is research. This webinar will discuss the potential uses, best practices and common hurdles of de-identified clinical datasets.

Registration is encouraged but not required. However, attending the live session will allow for questions. The session will be recorded and posted on the PNR Rendezvous web page a few days after the live session.

Medical Library Association CE credit is available for both the live and the recorded session.

More information about how to join the session is available on the PNR Rendezvous webpage.

Categories: Data Science

Moodle Class Announcement: Big Data in Healthcare: Exploring Emerging Roles – July 9 – August 31, 2018

SEA Data Science - Tue, 2018-06-12 15:45

The National Network of Librarians of Medicine (NNLM) invites you to participate in Big Data in Healthcare: Exploring Emerging Roles. This course will be primarily held via the Moodle platform with optional WebEx discussions. This course is designed to help health sciences librarians understand the issues of big data in clinical outcomes and what roles health sciences librarians can take on in this service area.

DatesJuly 9 – August 31, 2018

Register: To register for this course, please visit the class details page.

The class size for this course is limited to 40 students. We will begin a waitlist if there are more interested in participating.

Course instructors for the winter session are Ann Glusker, Pacific Northwest RegionDerek Johnson, Greater Midwest RegionAlicia Lillich, MidContinental RegionAnn Madhavan, Pacific Northwest RegionAimee Gogan, Southeastern/Atlantic Region, and Elaina Vitale, Mid-Atlantic Region.

Please contact Aimee Gogan with questions.

Description: Class Overview

Big Data in Healthcare: Exploring Emerging Roles

The Big Data in Healthcare: Exploring Emerging Roles course will help health sciences librarians better understand the issues of big data in clinical outcomes and what roles health sciences librarians can take on in this service area. Course content comes from information shared by the presenters at the March 7, 2016 NNLM Using Data to Improve Clinical Patient Outcomes Forum, top selections from the NNLM MCR Data Curation/Management Journal Club and NNLM PSR Data Curation/Management Journal Club’s articles, NINR’s Nursing Research Boot Camp, recommended readings from previous cohorts, and Big Data University’s Big Data Fundamentals online course.

Participants will have the opportunity to share what they learned with the instructor from each section of the course content either through WebEx discussions or Moodle Discussions within each Module. These submissions can be used to help support the student’s views expressed in the final essay assignment.

Objectives: Students who successfully complete the course will:

  • Explain the role big data plays in clinical patient outcomes.
  • Explain current/potential roles in which librarians are supporting big data initiatives
  • Illustrate the fundamentals of big data from a systems perspective
  • Articulate their views/options on the role health sciences sector librarians is in supporting big data initiatives

NOTE: Participants will articulate their views on why health sciences librarians should or should not become involved in supporting big data initiatives by sharing a 500-800 word essay. Students are encouraged to be brave and bold in their views so as to elicit discussions about the roles librarians should play in this emerging field. Participants are encouraged to allow their views to be published on a NNLM online blog/newsletter as part of a dialog with the wider health sciences librarian community engaging in this topic. Your course instructors will reach out to you following the completion of the course.

On top of information gained, being a part of the big data in clinical care dialog, and earning 9 continuing education credits from the Medical Library Association, students may earn an IBM Open Badge program from the Big Data University.

This is a semi-self-paced course (“semi” meaning there are completion deadlines).

Course Expectations: To complete this course for nine hours of MLA contact hours, participants are expected to:

  • Spend 1-2 hours completed the work within each module.
  • Commit to complete all activities and articulate your views within each module.
  • Complete course requirements by the deadline established in each module.
  • Coordinate with a course instructor to publish your observations/final assignments on a NNLM blog/newsletter
  • Provide course feedback on the Online Course Evaluation Form

Grading: Grades for this course is simply a pass/fail grading system. When your submission meets the assignment’s expectations, you will receive full credit for the contact hours for that Module. For submissions that are unclear or incomplete, you may be requested for more information until your instructor approves.

  • For discussion posts, your activity will be marked as complete after you’ve submitted a discussion AND your instructor assigns a point to mark as complete
  • If you participate in WebEx Journal Club Discussions (when available), your instructor will assign points in the Discussions for that module.
  • Students have the option to accept fewer contact hours. However, you will need to inform your course instructors ahead of time.

This is a Medical Library Association approved course that will earn students 9 contact hours.

 

The National Network of Librarians of Medicine (NNLM) invites you to participate in Big Data in Healthcare: Exploring Emerging Roles. This course will be primarily held via the Moodle platform with optional WebEx discussions. This course is designed to help health sciences librarians understand the issues of big data in clinical outcomes and what roles health sciences librarians can take on in this service area.

DatesJuly 9 – August 31, 2018

Register: To register for this course, please visit the class details page.

The class size for this course is limited to 40 students. We will begin a waitlist if there are more interested in participating.

Course instructors for the winter session are Ann Glusker, Pacific Northwest RegionDerek Johnson, Greater Midwest RegionAlicia Lillich, MidContinental RegionAnn Madhavan, Pacific Northwest RegionAimee Gogan, Southeastern/Atlantic Region, and Elaina Vitale, Mid-Atlantic Region.

Please contact Aimee Gogan with questions.

Description: Class Overview

Big Data in Healthcare: Exploring Emerging Roles

The Big Data in Healthcare: Exploring Emerging Roles course will help health sciences librarians better understand the issues of big data in clinical outcomes and what roles health sciences librarians can take on in this service area. Course content comes from information shared by the presenters at the March 7, 2016 NNLM Using Data to Improve Clinical Patient Outcomes Forum, top selections from the NNLM MCR Data Curation/Management Journal Club and NNLM PSR Data Curation/Management Journal Club’s articles, NINR’s Nursing Research Boot Camp, recommended readings from previous cohorts, and Big Data University’s Big Data Fundamentals online course.

Participants will have the opportunity to share what they learned with the instructor from each section of the course content either through WebEx discussions or Moodle Discussions within each Module. These submissions can be used to help support the student’s views expressed in the final essay assignment.

Objectives: Students who successfully complete the course will:

  • Explain the role big data plays in clinical patient outcomes.
  • Explain current/potential roles in which librarians are supporting big data initiatives
  • Illustrate the fundamentals of big data from a systems perspective
  • Articulate their views/options on the role health sciences sector librarians is in supporting big data initiatives

NOTE: Participants will articulate their views on why health sciences librarians should or should not become involved in supporting big data initiatives by sharing a 500-800 word essay. Students are encouraged to be brave and bold in their views so as to elicit discussions about the roles librarians should play in this emerging field. Participants are encouraged to allow their views to be published on a NNLM online blog/newsletter as part of a dialog with the wider health sciences librarian community engaging in this topic. Your course instructors will reach out to you following the completion of the course.

On top of information gained, being a part of the big data in clinical care dialog, and earning 9 continuing education credits from the Medical Library Association, students may earn an IBM Open Badge program from the Big Data University.

This is a semi-self-paced course (“semi” meaning there are completion deadlines).

Course Expectations: To complete this course for nine hours of MLA contact hours, participants are expected to:

  • Spend 1-2 hours completed the work within each module.
  • Commit to complete all activities and articulate your views within each module.
  • Complete course requirements by the deadline established in each module.
  • Coordinate with a course instructor to publish your observations/final assignments on a NNLM blog/newsletter
  • Provide course feedback on the Online Course Evaluation Form

Grading: Grades for this course is simply a pass/fail grading system. When your submission meets the assignment’s expectations, you will receive full credit for the contact hours for that Module. For submissions that are unclear or incomplete, you may be requested for more information until your instructor approves.

  • For discussion posts, your activity will be marked as complete after you’ve submitted a discussion AND your instructor assigns a point to mark as complete
  • If you participate in WebEx Journal Club Discussions (when available), your instructor will assign points in the Discussions for that module.
  • Students have the option to accept fewer contact hours. However, you will need to inform your course instructors ahead of time.

This is a Medical Library Association approved course that will earn students 9 contact hours.

 

 

Categories: Data Science

NIH Strategic Plan for Data Science

MCR Data Science - Mon, 2018-06-04 18:41

The National Institutes of Health (NIH) today released its first ever Strategic Plan for Data Science (PDF). The plan describes NIH’s overarching goals, strategic objectives, and implementation tactics for promoting the modernization of the NIH-funded biomedical data science ecosystem.

Wondering how libraries fit into the plan? NIH will partner with institutions to engage librarians and information specialists in finding new paths in areas such as library science that have the potential to enrich the data-science ecosystem for biomedical research. /da

Categories: Data Science

NIH Releases Inaugural Strategic Plan for Data Science!

PSR Data Science - Mon, 2018-06-04 18:04

Storing, managing, standardizing and publishing the vast amounts of data produced by biomedical research is a critical mission for the National Institutes of Health. In support of this effort, NIH has just released its first Strategic Plan for Data Science that provides a roadmap for modernizing the NIH-funded biomedical data science ecosystem. Over the course of the next year, NIH will begin implementing its strategy, with some elements of the plan already underway. NIH will continue to seek community input during the implementation phase.

Accessible, well-organized, secure, and efficiently operated data resources are critical to modern scientific inquiry. By maximizing the value of data generated through NIH-funded efforts, the pace of biomedical discoveries and medical breakthroughs for better health outcomes can be substantially accelerated. To keep pace with rapid changes in biomedical data science, NIH will work to address the:

  • findability, interconnectivity, and interoperability of NIH-funded biomedical data sets and resources
  • integration of existing data management tools and development of new ones
  • universalizing innovative algorithms and tools created by academic scientists into enterprise-ready resources that meet industry standards of ease of use and efficiency of operation
  • growing costs of data management

To advance NIH data science across the extramural and intramural research communities, the agency will hire a Chief Data Strategist. This management function will guide the development and implementation of NIH’s data science activities and provide leadership within the broader biomedical research data ecosystem. Jon R. Lorsch, Ph.D., director of the National Institute of General Medical Sciences, is currently available to comment on this strategic plan.

Categories: Data Science

UMN Now Accepting Applications for Data Management Training Session

GMR Data Science - Wed, 2018-05-30 16:50

 

Data Management for Librarians CE Course

Monday, August 6, 2018

Health science librarians from states represented by the Greater Midwest Region (GMR) are invited to participate in a data management for health sciences librarians CE course, hosted by the University of Minnesota Health Sciences Libraries in Minneapolis, MN.

The overall objective of this session is to introduce librarians to research data management and allow them to develop practical strategies for incorporating data into their existing roles.

Course Components

This 4-hour workshop will introduce participants to key elements of research data management in the health sciences, including best practices for documentation, metadata, backup, storage, and preservation. We will also explore advanced areas of research data management such as de-identification and intellectual property. The session will incorporate several activities to enable participants to apply best practices of data management when creating their own data management plans and critiquing existing data management plans (DMP). Beyond understanding the basics of research data management and applying those in the creation and assessment of DMPs, this session will also give participants an opportunity to consider the ways in which research data services can be incorporated into existing roles and responsibilities, including highlighting searching for research data for secondary analysis and integrating research data services into instruction and reference activities.

Data Management Skills Community of Practice (CoP)

Participants in the CE course may also participate in an online data management skills community of practice (CoP). The CoP will meet quarterly to take a deeper dive into a data management topic that could include federal funding compliance, data preservation & sharing, and open science. Topics are TBD and will be developed based on cohort needs.

CE Credits

Participants who complete the course will receive 4 MLA CE credits.

Instructor & CoP Facilitator:

Caitlin Bakker, MLIS: Caitlin Bakker is a health sciences librarian specializing in research support services, including data management, scholarly publishing, and citation tracking and analysis. She received her Masters in Library and Information Studies from McGill University in 2011 and is a Senior Member of the Academy of Health Information Professionals. Caitlin is interested in meta-research, and her projects have focused on publication models, systematic reviews, research ethics, and research impact.

Who can apply?

  • Applications are open to health science librarians in the Greater Midwest Region (Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, North Dakota, Ohio, South Dakota, Wisconsin)
  • Twelve librarians from the GMR will be awarded a stipend to have their travel costs to/from Minneapolis reimbursed up to $1000. Applications for the stipend must include a personal statement, cv and letter of support from their supervisor (see Application Instructions below).
  • Enrollment is limited to 35 participants

What does it cost?

  • There is no charge for the CE course
  • Twelve participants from the GMR will receive a reimbursement up to $1000 for travel costs.
  • Individuals who are not selected to receive the reimbursement but still wish to take the course are responsible for their own travel costs

How can I get there?

  • All stipend award attendees who elect to fly to Minneapolis-St. Paul International Airport must book their air travel on a U.S. air carrier per our grant award. MSP is served by all the major US carriers including American, Delta, JetBlue, Southwest, and United.

Where can I stay?

  • There is a block of 12 rooms being held at the Graduate Hotel, which is conveniently located on the Minneapolis East Bank campus. These 12 rooms are reservable at the discounted event rate ($160/night) on a first-come, first-served basis. Other hotels in walking-distance to campus include the Courtyard by Marriott, DoubleTree by Hilton, and the Hampton Inn and Suites. Each of these hotels is connected to campus via the Green Line light rail system. The closest light rail station to Bruininks Hall is the East Bank station.

Session Agenda:

  • Lunch and networking 12-1:00pm
  • CE course 1-5:00pm
  • Complete session evaluations 5:00-5:15pm

Important Dates

 

  • Stipend application deadline: Friday, June 22, 2018
  • Non-stipend application deadline: open until filled
  • Notifications: Friday, June 29, 2018
  • Course Date: Monday, August 6, 2018

 

Application Details

  • Name and Contact Information
  • Current Role/Title
  • Place of Employment

If Applying for Travel Stipend, please include:

  • Personal statement (1-2 paragraphs) describing your individual goals, why the training is needed and how you will apply the training in practice
  • CV
  • Letter of Support from your supervisor describing why you should attend and how your participation in the workshop and the quarterly online data management skills CoP will impact the organization moving forward

Application Instructions

Please fill out the online Application Form. If applying for the travel stipend, please upload a PDF of your current CV, your personal statement and your letter of support from your supervisor.

Questions?

Contact Lisa McGuire at: lmcguire@umn.edu

This activity is supported by the National Library of Medicine (NLM), National Institutes of Health (NIH) under cooperative agreement number 1UG4LM012346. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Categories: Data Science

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