Research graph
References from Exploring machine learning algorithms in sickle cell disease patient data: A systematic review. Local targets link to admitted publications; unresolved targets remain external evidence.
Sickle cell disease: current treatment and emerging therapies
2019 · External reference
Sickle cell disease
2018 · External reference
10.1109/icrest.2019.8644410
10.1109/icrest.2019.8644410 · External reference
Sickle-cell disease diagnosis support selecting the most appropriate machine learning method: Towards a general and interpretable approach for cell morphology analysis from microscopy images
10.1016/j.compbiomed.2020.104027 · 2020 · External reference
Automated screening of sickle cells using a smartphone-based microscope and deep learning
10.1038/s41746-020-0282-y · 2020 · External reference
An algorithm to detect overlapping red blood cells for sickle cell disease diagnosis
10.1049/ipr2.12439 · 2022 · External reference
10.1109/icccnt51525.2021.9580165
10.1109/icccnt51525.2021.9580165 · External reference
Identification of sickle cell anemia using deep neural networks
10.28991/esj-2021-01270 · 2021 · External reference
10.1109/gc-eleceng52322.2021.9788131
10.1109/gc-eleceng52322.2021.9788131 · External reference
10.1109/iciem51511.2021.9445293
10.1109/iciem51511.2021.9445293 · External reference
10.1109/icpc2t53885.2022.9776656
10.1109/icpc2t53885.2022.9776656 · External reference
An Artificial Intelligence Approach for Data Modelling Patients Inheritance of Sickle Cell Disease (SCD) in the Eastern Regions of Saudi Arabia
10.18280/mmep.090426 · 2022 · External reference
Sickle cell disease classification using deep learning
10.1016/j.heliyon.2023.e22203 · 2023 · External reference
An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears
10.4108/eetpht.9.3913 · 2023 · External reference
10.1109/conit59222.2023.10205802
10.1109/conit59222.2023.10205802 · External reference
10.1109/icpcsn58827.2023.00146
10.1109/icpcsn58827.2023.00146 · External reference
Detection of sickle cell disease using deep neural networks and explainable artificial intelligence
10.1515/jisys-2023-0179 · 2024 · External reference
Identifying elevated risk for future pain crises in sickle-cell disease using photoplethysmogram patterns measured during sleep: A machine learning approach
10.3389/fdgth.2021.714741 · 2021 · External reference
10.1007/978-3-030-68790-8_7
10.1007/978-3-030-68790-8_7 · External reference
Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study
10.2196/36998 · 2022 · External reference
Use of consumer wearables to monitor and predict pain in patients with sickle cell disease
10.3389/fdgth.2023.1285207 · 2023 · External reference
10.1109/ismsit50672.2020.9254994
10.1109/ismsit50672.2020.9254994 · External reference
10.1109/icict50816.2021.9358788
10.1109/icict50816.2021.9358788 · External reference
10.1007/978-981-15-6329-4_37
10.1007/978-981-15-6329-4_37 · External reference
Phenotypes of sickle cell intensive care admissions: an unsupervised machine learning approach in a single-center retrospective cohort
10.1007/s00277-022-04918-4 · 2022 · External reference
Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease
10.1111/bjh.17107 · 2021 · External reference
10.1109/itikd56332.2023.10099773
10.1109/itikd56332.2023.10099773 · External reference
Using machine learning to predict rapid decline of kidney function in sickle cell anemia
10.1002/jha2.168 · 2021 · External reference
Using machine learning to predict early onset acute organ failure in critically ill intensive care unit patients with sickle cell disease: retrospective study
10.2196/14693 · 2020 · External reference
10.5220/0012343800003651
10.5220/0012343800003651 · External reference
A phenotypic risk score for predicting mortality in sickle cell disease
10.1111/bjh.17342 · 2021 · External reference
