AIMC Topic: Leukemia, Myeloid, Acute
Clear Filters Showing 41 to 50 of 88 articles
How to predict relapse in leukemia using time series data: A comparative in silico study.
PloS one
Nov 15, 2021
Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitat...
Job characteristics of a Malaysian bank's anti-money laundering system and its employees' job satisfaction.
F1000Research
Oct 15, 2021
Banks and financial institutions are vulnerable to money laundering (ML) as a result of crime proceeds infiltrating banks in the form of significant cash deposits. Improved financial crime compliance processes and systems enable anti-ML (AML) analys...
Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears.
Leukemia
Sep 8, 2021
The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it suffers from a lack of standardization and inter-observer variability. Deep learning (DL) can proces...
Identification of biomarkers for acute leukemia via machine learning-based stemness index.
Gene
Aug 16, 2021
Traditional methods to understand leukemia stem cell (LSC)'s biological characteristics include constructing LSC-like cells and mouse models by transgenic or knock-in methods. However, there are some potential pitfalls in using this method, such as r...
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.
eLife
Aug 5, 2021
For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrai...
Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.
PloS one
Jul 21, 2021
This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify ...
A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images.
Computer methods and programs in biomedicine
Feb 12, 2021
BACKGROUND AND OBJECTIVES: Morphological differentiation among blasts circulating in blood in acute leukaemia is challenging. Artificial intelligence decision support systems hold substantial promise as part of clinical practise in detecting haematol...
Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.
PloS one
Jan 28, 2021
Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated genetic factors must be better understood to search appropriately for anti-aging agents. We utilized an ag...
A Reliable Machine Learning Approach applied to Single-Cell Classification in Acute Myeloid Leukemia.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Machine Learning research applied to the medical field is increasing. However, few of the proposed approaches are actually deployed in clinical settings. One reason is that current methods may not be able to generalize on new unseen instances which d...
IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning.
Journal of healthcare engineering
Dec 3, 2020
For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affec...
Leukemia, Myelogenous, Chronic, BCR-ABL Positive
Leukemia
Databases, Factual
Internet of Things
Humans
Pattern Recognition, Automated
Leukemia, Lymphocytic, Chronic, B-Cell
Neural Networks, Computer
Deep Learning
Precursor Cell Lymphoblastic Leukemia-Lymphoma
Leukemia, Myeloid, Acute
Machine Learning
Diagnostic Imaging
Cloud Computing
Diagnosis, Computer-Assisted
COVID-19
Telemedicine
Algorithms
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