Training Program Title

I2S2 Seminar: Clinical AI From Time Series to Free Text

Contributor

Advance-CTR

CTSA Hub Administered

yes

Contact Information

Advance-CTR, AdvanceRI@Brown.edu

Training Program Description

Data-driven clinical decision making, e.g., via machine learning, holds considerable promise for evidence based medicine. This session will present three ongoing advances from BCBI’s AI Lab that tackle very different types of input data ranging from numerical vital signs used to predict pediatric sepsis at Hasbro Children’s Hospital, via automatic localization of pneumonia on chest X-rays at Memorial Sloan Kettering Cancer Center to free-text analysis of PubMed articles. In each of these talks, we will discuss the clinical relevance of the studied task, the studied input data modality, methodological considerations of the machine learning models, and real-world applicability of the derived methods. https://www.youtube.com/watch?v=vfEFlZHktCo&t=23s

Competency Keywords

Study design, Grant proposal, Responsible conduct of research, Product development, Regulation, Best practice, Documentation, Research study management, Data management, Biostatistics, Leadership, Workforce development, Scientific writing, Teamwork

Other Keywords

AI, I2S2, Free Text, Clinical AI

Digital Commons Disciplines

Biomedical Engineering and Bioengineering | Education | Life Sciences | Medical Education | Medicine and Health Sciences | Physical Sciences and Mathematics | Social and Behavioral Sciences

Public

yes

Cost to Access

no

Competency Domains

Scientific Concepts and Research Design; Ethical and Participant Safety Considerations; Medicines Development and Regulation; Clinical Trials Operations; Study and Site Management; Data Management and Informatics; Leadership and Professionalism; Communication and Teamwork

Learning Objectives

  1. Data-driven clinical decision making, e.g., via machine learning, holds considerable promise for evidence based medicine.
  2. This session will present three ongoing advances from BCBI’s AI Lab that tackle very different types of input data ranging from numerical vital signs used to predict pediatric sepsis at Hasbro Children’s Hospital, via automatic localization of pneumonia on chest X-rays at Memorial Sloan Kettering Cancer Center to free-text analysis of PubMed articles.
  3. Discussions about the clinical relevance of the studied task, the studied input data modality, methodological considerations of the machine learning models, and real-world applicability of the derived methods.

Delivery Method

Online

Target Learners

Principal Investigators; Clinical Research Professionals (other than PI); MDs including residents, house officers, fellows, hospitalists; Undergraduate, graduate or professional degree students; Post-doctoral scholars; Community Partners

Learning Level

Skilled

Onboarding

no

Frequency

On demand

Time Needed

57 minutes

Associated Assessment

no

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