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Tutorial Information on 17 MAY, ICMHI 2024

Time: 14:00-17:00

Registration: 50USD fee for each participant

Deadline: April 15, 2024

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The Multimodal Learning for Electronic Medical Records Cookbook

This tutorial is intended to cover the needs and interests of researchers and analysts who want to build prediction models with multiple types of clinical data, such as tabular data, time-series data, and others. The main targeted participants of this tutorial are clinical data analysts and other personnel who have a chance to access EHR or clinical data.

With the advent of EHRs, leveraging big data analytics has become indispensable for advancing clinical research. Enriched EHRs harbor vital insights into disease progression, offering a wealth of information for treatment selection and disease diagnosis. Despite this, most studies predominantly rely on a single type of clinical data, limiting the full utilization of EHRs' rich information.

This instructional guide aims to bridge this gap by introducing multimodal learning mechanisms, enabling the construction of predictive models using Python and Google Colab. The tutorial unfolds in three parts:

EHR Overview and Standards:
In this initial section, we delve into Electronic Health Records (EHRs) and explore relevant standards, providing a foundation drawn from state-of-the-art research papers.

Building Models with Multimodal Learning:
The second segment focuses on fundamental concepts and strategies for constructing models using multiple types of clinical data.

Practical Implementation:
In the third and final section, participants will engage in a hands-on experience, walking through the step-by-step process of building models with multimodal learning.

By the end of this tutorial, you will be equipped with the knowledge and skills to leverage a diverse array of clinical data types, revolutionizing the way predictive models are developed and utilized in healthcare research. Let's embark on this transformative journey together!

#About the lecturers:
Yi-Ju Tseng, PhD
Yi-Ju Tseng is an associate professor at National Yang Ming Chiao Tung with extensive experience in claims data and electronic medical records analysis and machine learning. Tseng's work focuses on improving infection surveillance by using informatics techniques and applying machine learning techniques to infectious disease diagnosis and prognosis prediction. Tseng received the MOST Young Scholar Fellowship and Special Outstanding Talent Award from the Ministry of Science and Technology, Taiwan. Her research interests include medical informatics, public health informatics, clinical decision support, data science, and infection surveillance.

Submission Method

Electronic Submission System ( .pdf)

Formatting Instructions (DOC)

Contact Information

Conference Secretary: Ms. Alice Lin

E-mail: icmhi@cbees.org

Contact number: +86-18117801445