Sickle Cell Crisis Prediction Dataset

Domain:

healthcare

Record type:

dataset

Creator:

ele

Host:

A synthetic tabular dataset for predicting sickle cell disease crises in African populations. SCD affects 300,000+ African infants yearly and is the most common genetic condition globally. Property Value Total rows 10,000 Positive cases (label=1) 5,000 Control cases (label=0) 5,000 Countries represented 20 Temporal coverage 2019–2024

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Application of Pattern Recognition to Early Detection of symptoms of sickle cell crisis

Application of Pattern Recognition to Early Detection of symptoms of sickle cell crisis

Poster presented at the Deep Learning Indaba 2022 by Toluwani Adegoke

Revolutionizing Sickle Cell Treatment: Harnessing AI for Personalized Hydroxyurea Response Prediction

Revolutionizing Sickle Cell Treatment: Harnessing AI for Personalized Hydroxyurea Response Prediction

Poster presented at the Deep Learning Indaba 2023 by Abdulhameed Dere

Patro331/sickle-cell-detection

Deep Learning for Automated Sickle Cell Detection in Peripheral Blood Smears using African Clinical

ASSESSING PREDICTION OUTCOMES OF CONVOLUTIONAL NEURAL NETWORKS TRAINED ON PARASITE AND HEALTHY CELL IMAGES FOR SICKLE-CELL AND MALARIA

To address the implications of using a CNN, a machine learning model was trained with parasite (posi

Establishing Sickle Cell Diagnostics and Characterizing a Pediatric Sickle Cell Disease Cohort in Malawi

Abstract Sickle cell disease (SCD) is highly prevalent in sub-Saharan Africa; howev

Cell-Free Mitochondrial DNA as a Damage-Associated Molecular Pattern and Candidate Biomarker in Sickle Cell Disease Vaso-Occlusive Crisis: A Scoping Review

Sickle cell disease (SCD) is defined by consistent hemolysis, recurrent vaso-occlusive crises and co