A Machine Learning Model for Predicting Fetal Hemoglobin Levels in Sickle Cell Disease Patients
Author:
Oikonomou KonstantinosORCID,
Steinhöfel KathleelORCID,
Menzel StephanORCID
Publisher
Springer Singapore
Reference14 articles.
1. Platt OS, Brambilla DJ, Rosse WF, Milner PF, Castro O, Steinberg MH, Klug PP (1994) Mortality in sickle cell disease–life expectancy and risk factors for early death. N Engl J Med 330(23):1639–1644
2. Platt OS, Thorington BD, Brambilla DJ, Milner PF, Rosse WF, Vichinsky E, Kinney TR (1991) Pain in sickle cell disease: rates and risk factors. N Engl J Med 325(1):11–16
3. Paikari A, Sheehan VA (2018) Fetal haemoglobin induction in sickle cell disease. Br J Haematol 180(2):189–200
4. Adams R, McKie V, Nichols F, Carl E, Zhang DL, McKie K, Hess D (1992) The use of transcranial ultrasonography to predict stroke in sickle cell disease. N Engl J Med 326(9):605–610
5. Miller ST, Sleeper LA, Pegelow CH, Enos LE, Wang WC, Weiner SJ, Kinney TR (2000) Prediction of adverse outcomes in children with sickle cell disease. N Engl J Med 342(2):83–89