[HTML][HTML] A review of artificial intelligence applications in hematology management: current practices and future prospects

Y El Alaoui, A Elomri, M Qaraqe… - Journal of Medical …, 2022 - jmir.org
Background Machine learning (ML) and deep learning (DL) methods have recently
garnered a great deal of attention in the field of cancer research by making a noticeable …

Predictive models for clinical decision making: Deep dives in practical machine learning

S Eloranta, M Boman - Journal of Internal Medicine, 2022 - Wiley Online Library
The deployment of machine learning for tasks relevant to complementing standard of care
and advancing tools for precision health has gained much attention in the clinical …

Prognostic indices in diffuse large B-cell lymphoma: a population-based comparison and validation study of multiple models

J Jelicic, K Juul-Jensen, Z Bukumiric… - Blood Cancer …, 2023 - nature.com
Abstract Currently, the International Prognostic Index (IPI) is the most used and reported
model for prognostication in patients with newly diagnosed diffuse large B-cell lymphoma …

Sequencing therapy in relapsed DLBCL

CR Flowers, OO Odejide - Hematology, 2022 - ashpublications.org
Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid malignancy
worldwide, comprising approximately 30% of all lymphomas. Currently, 50% to 60% of …

Ferroptosis markers predict the survival, immune infiltration, and ibrutinib resistance of diffuse large B cell lymphoma

J Weng, L Chen, H Liu, XP Yang, L Huang - Inflammation, 2022 - Springer
Diffuse large B cell lymphoma (DLBCL) is the most common hematological malignancy in
adults. Ferroptosis is an iron-dependent programmed cell death caused by lipid …

Smart variant filtering-A blueprint solution for massively parallel sequencing-based variant analysis

O Brahimllari, S Eloranta… - Health informatics …, 2024 - journals.sagepub.com
Massively parallel sequencing helps create new knowledge on genes, variants and their
association with disease phenotype. This important technological advancement …

Classifying 2-year recurrence in patients with dlbcl using clinical variables with imbalanced data and machine learning methods

L Wang, ZQ Zhao, YH Luo, HM Yu, SQ Wu… - Computer Methods and …, 2020 - Elsevier
Background Treatments are limited for patients with relapsed/refractory Diffuse large B-cell
lymphoma (DLBCL), and their survival rate is low. Prediction of the recurrence hazard for …

Iterated cross validation method for prediction of survival in diffuse large B-cell lymphoma for small size dataset

CC Chang, CH Chen, JG Hsieh, JH Jeng - Scientific reports, 2023 - nature.com
Efforts have been made to improve the risk stratification model for patients with diffuse large
B-cell lymphoma (DLBCL). This study aimed to evaluate the disease prognosis using …

Predicting liver disorder based on machine learning models

J Zhao, P Wang, Y Pan - The Journal of Engineering, 2022 - Wiley Online Library
As the main detoxification organ of human body, liver is very important in humans' health by
metabolizing a lot of substances that are taken in, including alcohol and the medicine …

[HTML][HTML] Value of total lesion glycolysis and cell-of-origin subtypes for prognostic stratification of diffuse large B-cell lymphoma patients

C Jiang, Y Teng, Z Zheng, Z Zhou… - Quantitative imaging in …, 2021 - ncbi.nlm.nih.gov
Background This study aimed to explore the added prognostic value of baseline metabolic
volumetric parameters and cell of origin subtypes to the National Comprehensive Cancer …