TY - GEN
T1 - Machine Learning with Low-Resource Data from Psychiatric Clinics
AU - Du, Hongmin W.
AU - De Chen, Neil
AU - Li, Xiao
AU - Vasarhelyi, Miklos A.
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Data Augmentation
KW - Low Resource Data
KW - Machine Learning
KW - Medical Data
KW - Small Data
UR - http://www.scopus.com/inward/record.url?scp=85180626282&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85180626282&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-49614-1_34
DO - 10.1007/978-3-031-49614-1_34
M3 - Conference contribution
SN - 9783031496134
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 455
EP - 465
BT - Combinatorial Optimization and Applications - 16th International Conference, COCOA 2023, Proceedings
A2 - Wu, Weili
A2 - Guo, Jianxiong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023
Y2 - 15 December 2023 through 17 December 2023
ER -