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Smart Imitator: Learning from Imperfect Clinical Decisions.
J Am Med Inform Assoc
January 2025
Institute of Data Science, National University of Singapore, 117602, Singapore.
Objectives: This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.
Materials And Methods: Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL.
A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.
PLoS Comput Biol
January 2025
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches.
View Article and Find Full Text PDFEnhancing semantical text understanding with fine-tuned large language models: A case study on Quora Question Pair duplicate identification.
PLoS One
January 2025
Department of Biomedical and Health Informatics, Tsui Laboratory, Children's Hospital of Philadelphia, Philadelphia, PA, United States of America.
Semantical text understanding holds significant importance in natural language processing (NLP). Numerous datasets, such as Quora Question Pairs (QQP), have been devised for this purpose. In our previous study, we developed a Siamese Convolutional Neural Network (S-CNN) that achieved an F1 score of 82.
View Article and Find Full Text PDFThe advantages of lexicon-based sentiment analysis in an age of machine learning.
PLoS One
January 2025
Department of Political Science, Middlebury College, Middlebury, Vermont, United States of America.
Assessing whether texts are positive or negative-sentiment analysis-has wide-ranging applications across many disciplines. Automated approaches make it possible to code near unlimited quantities of texts rapidly, replicably, and with high accuracy. Compared to machine learning and large language model (LLM) approaches, lexicon-based methods may sacrifice some in performance, but in exchange they provide generalizability and domain independence, while crucially offering the possibility of identifying gradations in sentiment.
View Article and Find Full Text PDFUnsupervised clustering of intraventricular hemodynamic forces for the phenotyping of left-ventricular function in non-ischemic left ventricular cardiomyopathy.
Eur Heart J Cardiovasc Imaging
January 2025
Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
Background: Cardiac magnetic resonance (CMR) is essential for diagnosing cardiomyopathy, serving as the gold standard for assessing heart chamber volumes and tissue characterization. Hemodynamic forces (HDF) analysis, a novel approach using standard cine CMR images, estimates energy exchange between the left ventricular (LV) wall and blood. While prior research has focused on peak or mean longitudinal HDF values, this study aims to investigate whether unsupervised clustering of HDF curves can identify clinically significant patterns and stratify cardiovascular risk in non-ischemic LV cardiomyopathy (NILVC).
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