AIMC Topic: Blood Cell Count
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Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning.
Scientific reports
Jul 21, 2025
Mild to severe anemia is caused by thalassemia, a common genetic disorder affecting over 100 countries worldwide, that results from the abnormality of one or several of the four globin genes. This leads to chronic hemolytic anemia and disrupted synth...
Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks.
PloS one
Jul 8, 2025
PURPOSE: Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. As show...
Analysis of maternal fetal outcomes and complete blood count parameters according to the stages of placental abruption: a retrospective study.
European journal of medical research
Jun 18, 2025
BACKGROUND: To compare the demographic characteristics, maternal and perinatal outcomes and hemoglobin parameters according to stages diagnosed with placental abruption.
Advancing blood cell detection and classification: performance evaluation of modern deep learning models.
BMC medical informatics and decision making
Jun 4, 2025
The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leukemia, and infection, all of which may cause significant mortality. Accurate blood cell identification...
Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according to machine learning.
BMC infectious diseases
Apr 22, 2025
INTRODUCTION: Sepsis is a life-threatening condition characterized by widespread inflammatory response syndrome in the body resulting from infection. Previous studies have demonstrated that some inflammatory factors or nutritional elements contribute...
Complete Blood Count and Monocyte Distribution Width-Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study.
Journal of medical Internet research
Feb 26, 2025
BACKGROUND: Sepsis is an organ dysfunction caused by a dysregulated host response to infection. Early detection is fundamental to improving the patient outcome. Laboratory medicine can play a crucial role by providing biomarkers whose alteration can ...
AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.
Journal of medical Internet research
Jan 24, 2025
BACKGROUND: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection...
Classification of α-thalassemia data using machine learning models.
Computer methods and programs in biomedicine
Jan 6, 2025
BACKGROUND: Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis is costly and inaccurate in low-income regions, often relying on complete blood count (CBC) tests. Th...
Comparison between statistical and machine learning methods to detect the hematological indices with the greatest influence on elevated serum levels of low-density lipoprotein cholesterol.
Chemistry and physics of lipids
Oct 5, 2024
INTRODUCTION: Elevated levels of low-density lipoprotein-cholesterol (LDL-C) is a significant risk factor for the development of cardiovascular diseases (CVD)s. Furthermore, studies have revealed an association between indices of the complete blood c...
Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study.
Scientific reports
May 12, 2024
Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally a...
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