Machine Learning with Low-Resource Data from Psychiatric Clinics

Hongmin W. Du, Neil De Chen, Xiao Li, Miklos A. Vasarhelyi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Amidst the rapid growth of big data, the success of machine learning is critically tethered to the availability and quality of training data. A pertinent challenge faced by this symbiotic relationship is the issue of “low-resource data,” characterized by insufficient data volume, diversity, and representativeness, and exacerbated by class imbalances within datasets. This study delves into the intersection of machine learning and big data, exploring innovative methodologies to counteract the challenges of data scarcity. Focusing on psychiatric clinic data, marked by subjectivity and inconsistency, we outline the unique challenges posed by the nature of data in this domain. To address these challenges, we explore the potential of data augmentation-using transformations or operations on available data-and transfer learning, where knowledge from a pre-trained model on a large dataset is transferred to a smaller one. Through a comprehensive exploration of these methodologies, this research aims to bolster the effectiveness of machine learning in low-resource environments, with a vision of advancing the digital landscape while navigating inherent data constraints.

Original languageAmerican English
Title of host publicationCombinatorial Optimization and Applications - 16th International Conference, COCOA 2023, Proceedings
EditorsWeili Wu, Jianxiong Guo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages455-465
Number of pages11
ISBN (Print)9783031496134
DOIs
StatePublished - 2024
Event16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023 - Hawai, United States
Duration: Dec 15 2023Dec 17 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14462 LNCS

Conference

Conference16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023
Country/TerritoryUnited States
CityHawai
Period12/15/2312/17/23

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Keywords

  • Data Augmentation
  • Low Resource Data
  • Machine Learning
  • Medical Data
  • Small Data

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