AIMC Topic: Hematopoietic Stem Cell Transplantation

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Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes.

Blood advances
We investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machine learning approach, the Nonparametric Failure Time Bayesian Additive Regression Trees (NFT BART). N...

[LORENZO'S OIL AND ADRENOLEUKODYSTROPHY EXAMINING AN ARTIFICIAL INTELLIGENCE TOOL INTENDED FOR CONDUCTING LITERATURE SEARCHES AND ANALYSES].

Harefuah
Adrenoleukodystrophy is a genetic metabolic disorder characterized by a heterogeneous phenotype. Its severe form, known as cerebral adrenoleukodystrophy, involves unpredictable cerebral damage and progressive central nervous system deterioration. Thi...

High-dimensional Immune Profiles and Machine Learning May Predict Acute Myeloid Leukemia Relapse Early following Transplant.

Journal of immunology (Baltimore, Md. : 1950)
Identification of early immune signatures associated with acute myeloid leukemia (AML) relapse following hematopoietic stem cell transplant (HSCT) is critical for patient outcomes. We analyzed PBMCs from 58 patients with AML undergoing HSCT, focusing...

Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity.

Blood advances
Artificial intelligence (AI)-enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk...

Prediction of early-phase cytomegalovirus pneumonia in post-stem cell transplantation using a deep learning model.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Diagnostic challenges exist for CMV pneumonia in post-hematopoietic stem cell transplantation (post-HSCT) patients, despite early-phase radiographic changes.

Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation.

Blood advances
Acute graft-versus-host disease (aGVHD) is 1 of the critical complications that often occurs following allogeneic hematopoietic stem cell transplantation (HSCT). Thus far, various types of prediction scores have been created using statistical calcula...

A BLSTM with Attention Network for Predicting Acute Myeloid Leukemia Patient's Prognosis using Comprehensive Clinical Parameters.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The prognosis management is crucial for highrisk disease like Acute Myeloid Leukemia (AML) in order to support decisions of clinical treatment. However, the challenges of accurate and consistent forecasting lie in the high variability of the disease ...

[Stability of high-dose etoposide dilutions for use in hematopoietic stem cell transplantation conditioning regimens].

Journal de pharmacie de Belgique
High-dose etoposide is used in conditioning regimens for allogeneic stem cell transplantation. The limited stability of the drug induces barriers for its use for pharmacists, nurses and patients. When using a concentration of 10 mg/mL etoposide in ph...