Conference proceeding
Advances in Mining Heterogeneous Healthcare Data
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp.4050-4051
ACM Conferences
KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
08/14/2021
DOI: 10.1145/3447548.3470789
Abstract
Thanks to the explosion of heterogeneous healthcare data and advanced machine learning and data mining techniques, specifically deep learning methods, we now have an opportunity to make difference in healthcare. In this tutorial, we will present state-of-the-art deep learning methods and their real-world applications, specifically focusing on exploring the unique characteristics of different types of healthcare data. The first half will be spent on introducing recent advances in mining structured healthcare data, including computational phenotyping, disease early detection/risk prediction and treatment recommendation. In the second half, we will focus on challenges specific to the unstructured healthcare data, and introduce advanced deep learning methods in automated ICD coding, understandable medical language translation, clinical trial mining, and medical report generation. This tutorial is intended for students, engineers and researchers who are interested in applying deep learning methods to healthcare, and prerequisite knowledge will be minimal. The tutorial will be concluded with open problems and a Q&A session.
Details
- Title: Subtitle
- Advances in Mining Heterogeneous Healthcare Data
- Creators
- Fenglong Ma - Pennsylvania State UniversityMuchao Ye - Pennsylvania State UniversityJunyu Luo - Pennsylvania State UniversityCao Xiao - Amplitude, San Francisco, CA, USAJimeng Sun - UIUC, Champaign, IL, USA
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp.4050-4051
- Conference
- KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Series
- ACM Conferences
- DOI
- 10.1145/3447548.3470789
- Language
- English
- Date published
- 08/14/2021
- Academic Unit
- Computer Science
- Record Identifier
- 9984696565902771
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