AIMC Topic: Graft vs Host Disease
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Predictive modeling of outcomes in acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation using machine learning techniques.
Leukemia research
Nov 20, 2024
BACKGROUND: Leukemia necessitates continuous research for effective therapeutic techniques. Acute leukemia (AL) patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) focus on key outcomes such as overall survival (OS), re...
Middle Aged
Adolescent
Child, Preschool
Child
Survival Rate
Leukemia, Myeloid, Acute
Male
Hematopoietic Stem Cell Transplantation
Transplantation, Homologous
Precursor Cell Lymphoblastic Leukemia-Lymphoma
Leukemia
Treatment Outcome
Follow-Up Studies
Graft vs Host Disease
Machine Learning
Prognosis
Retrospective Studies
Young Adult
Humans
Adult
Female
Aged
Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia.
BMC medical research methodology
May 11, 2024
Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strat...
Embracing the age of artificial intelligence: paradigm shifts, opportunities, and challenges in the treatment of acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation.
Science China. Life sciences
Feb 27, 2024
Artificial intelligence recognition of cutaneous chronic graft-versus-host disease by a deep learning neural network.
British journal of haematology
Mar 24, 2022
Prognostic Biomarkers for Thrombotic Microangiopathy after Acute Graft-versus-Host Disease: A Nested Case-Control Study.
Transplantation and cellular therapy
Dec 16, 2020
Transplantation-associated thrombotic microangiopathy (TA-TMA) is a complication of allogeneic hematopoietic cell transplantation (HCT) that often occurs following the development of acute graft-versus-host disease (aGVHD). In this study, we aimed to...
A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).
Sensors (Basel, Switzerland)
Oct 27, 2020
Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially...
Machine Learning Classification Algorithms to Predict aGvHD following Allo-HSCT: A Systematic Review.
Methods of information in medicine
Apr 29, 2020
BACKGROUND:  The acute graft-versus-host disease (aGvHD) is the most important cause of mortality in patients receiving allogeneic hematopoietic stem cell transplantation. Given that it occurs at the stage of severe tissue damage, its diagnosis is la...
Machine learning reveals chronic graft--host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies.
Haematologica
Sep 20, 2018
The application of machine learning in medicine has been productive in multiple fields, but has not previously been applied to analyze the complexity of organ involvement by chronic graft--host disease. Chronic graft--host disease is classified by an...
A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients.
Biometrical journal. Biometrische Zeitschrift
May 16, 2018
Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft-versus-host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two st...
Editor's notes.
Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis
May 9, 2018
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