AIMC Topic: Hematologic Neoplasms
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An ontology for representing hematologic malignancies: the cancer cell ontology.
BMC bioinformatics
Apr 25, 2019
BACKGROUND: Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell...
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...
Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.
PloS one
Aug 17, 2016
INTRODUCTION: Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used r...
Heart Arrest
Monitoring, Physiologic
Critical Care
Early Diagnosis
Vital Signs
Cohort Studies
Models, Theoretical
Prognosis
Treatment Outcome
Retrospective Studies
Hematologic Neoplasms
Electronic Health Records
Male
Humans
Female
Aged
Neural Networks, Computer
Middle Aged
Aged, 80 and over
Adolescent
Machine Learning
Young Adult
Algorithms
Adult
Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.
Anticancer research
Jun 1, 2025
BACKGROUND/AIM: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed to develop a model for predicting the severity of bacteremia in patients with FN and hematologic ma...
[Advancements in artificial intelligence for the precise diagnosis and treatment of hematological malignancies].
Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi
Feb 14, 2025
Hematological malignancy is a highly heterogeneous disease with complex biological characteristics and diverse clinical manifestations. Therefore, precise diagnosis and treatment are crucial and urgently needed. To further improve the accuracy of dia...
Identification of Clonal Hematopoiesis Driver Mutations through In Silico Saturation Mutagenesis.
Cancer discovery
Sep 4, 2024
Clonal hematopoiesis (CH) is a phenomenon of clonal expansion of hematopoietic stem cells driven by somatic mutations affecting certain genes. Recently, CH has been linked to the development of hematologic malignancies, cardiovascular diseases, and o...
A Greek Conversational Agent for Hematologic Malignancies: Usability and User Experience Assessment.
Studies in health technology and informatics
Aug 22, 2024
Enabling patients to actively document their health information significantly improves understanding of how therapies work, disease progression, and overall life quality affects for those living with chronic disorders such as hematologic malignancies...
An Integral R-Banded Karyotype Analysis System of Bone Marrow Metaphases Based on Deep Learning.
Archives of pathology & laboratory medicine
Aug 1, 2024
CONTEXT.—: Conventional karyotype analysis, which provides comprehensive cytogenetic information, plays a significant role in the diagnosis and risk stratification of hematologic neoplasms. The main limitations of this approach include long turnaroun...
[Flow cytometry increases the proportion of valuable samples in cerebrospinal fluid with normal cell count in malignant blood diseases].
Revista medica de Chile
May 1, 2023
BACKGROUND: The alteration of cerebrospinal fluid (CSF) in hematologic neoplasms is a poor prognostic marker. The characteristics of CSF are usually analyzed by flow cytometry or cytology. However, paucicellular CSF samples (≤5 cells/dL) can sometime...
Clinical insights into hematologic malignancies and comparative analysis of molecular signatures of acute myeloid leukemia in different ethnicities using an artificial intelligence offering.
Medicine
Dec 23, 2021
Next generation sequencing generates copious amounts of genomics data, causing manual interpretation to be laborious and non-scalable while remaining subjective (even for highly trained specialists). We evaluated the performance of the artificial int...
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