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Scientific Communication Skills for Pre-Doctoral Trainees
Scientific Communication Skills for Pre-Doctoral Trainees

A key factor for success in science is the ability to communicate clearly and succinctly utilizing language appropriate to the audience.  NIH has defined communication competency as the ability to “communicate clinical and translational research findings to different groups of individuals, including colleagues, students, the lay public and the media.” A scientist’s career will “soar” or “sink” based on their ability to communicate their science through oral and written communication channels.  This workshop, recorded at Translational Science 2019, focuses on oral presentation skills and through brief presentations and utilization of tools provides critical information to:

  1. Understand the professional VALUE of oral presentations
  2. Identify WHO your audience is
  3. Identify WHAT is the relevant information that they need to hear
  4. Identify WHY this audience need to know this
  5. Give an effective presentation

Speakers:

  1. Stephen C. Ekker, Mayo Clinic
  2. Becca Gas, Mayo Clinic
  3. Anthony Windebank, Mayo Clinic
  4. Moderator: Karen Weavers, Mayo Clinic
Trends in Genomic Analysis: Science, Technology, Translation and Policy
Trends in Genomic Analysis: Science, Technology, Translation and Policy

This mini-symposia, recorded at Translational Science 2019, examined current and emerging genomic sequencing and other genomic analysis approaches and programs, exploring the scientific, regulatory, and policy opportunities and gaps to be addressed in this area of precision medicine.


Speakers:

  1. Teri Manolio, MD, PhD, National Institue for Human Genome Research, NIH
  2. Greg Feero, MD, PhD, Geisel School of Medicine at Dartmouth
  3. Matthew R. Nelson, GSK
  4. John W. Belmonth, MD, PhD, Clinical Genomics Group, Illumina Inc.
  5. Zivana Tezak, PhD, Center for Devices and Radiological Heath, FDA


Moderator: Scott Steele, PhD, University of Rochester Medical Center

Predictive Modeling and AI in Healthcare: What to Do with Garbage
Predictive Modeling and AI in Healthcare: What to Do with Garbage

Data from the electronic health record (EHR) are frequently used to generate predictive models, or as the basis of artificial intelligence tools, to support medical decision making and health care operations. There are fundamental limitations based on the quality of the underlying data. Missingness and errors are unlikely to be random, and shouldn’t be ignored. This is true both for developing the models and then for using these tools to make decisions at the individual patient level. In this session, recorded at Translational Science 2019, the presenter covers the principal challenges, how errors propagate through modeling processes, and how they translate to sub-optimal decision making. Potential solutions and opportunities for research are discussed.


Speaker: Jareen Meinzen-Derr, Cincinnati Children’s Hospital Medical Center