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Prediction of post-delivery hemoglobin levels with machine learning algorithms.
Aghajanian, Sepehr; Jafarabady, Kyana; Abbasi, Mohammad; Mohammadifard, Fateme; Bakhshali Bakhtiari, Mina; Shokouhi, Nasim; Saleh Gargari, Soraya; Bakhtiyari, Mahmood.
Afiliación
  • Aghajanian S; Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
  • Jafarabady K; Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Abbasi M; Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
  • Mohammadifard F; Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
  • Bakhshali Bakhtiari M; Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
  • Shokouhi N; Department of Obstetrics and Gynecology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Saleh Gargari S; Yas University Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Bakhtiyari M; Department of Obstetrics and Gynecology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Salehgargari2000@gmail.com.
Sci Rep ; 14(1): 13953, 2024 06 17.
Article en En | MEDLINE | ID: mdl-38886458
ABSTRACT
Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care centers on patient delivery were incorporated into the current study. Patients with preexisting coagulopathy, traumatic cases, and allogenic blood transfusion were excluded from all analyses. The association of pre-delivery variables with 24-h post-delivery hemoglobin level was evaluated using feature selection with Elastic Net regression and Random Forest algorithms. A suite of ML algorithms was employed to predict post-delivery Hb levels. Out of 2051 pregnant women, 1974 were included in the final analysis. After data pre-processing and redundant variable removal, the top predictors selected via feature selection for predicting post-delivery Hb were parity (B 0.09 [0.05-0.12]), gestational age, pre-delivery hemoglobin (B0.83 [0.80-0.85]) and fibrinogen levels (B0.01 [0.01-0.01]), and pre-labor platelet count (B*1000 0.77 [0.30-1.23]). Among the trained algorithms, artificial neural network provided the most accurate model (Root mean squared error 0.62), which was subsequently deployed as a web-based calculator https//predictivecalculators.shinyapps.io/ANN-HB . The current study shows that ML models could be utilized as accurate predictors of indirect measures of PPH and can be readily incorporated into healthcare systems. Further studies with heterogenous population-based samples may further improve the generalizability of these models.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Hemoglobinas / Aprendizaje Automático Límite: Adult / Female / Humans / Pregnancy Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Hemoglobinas / Aprendizaje Automático Límite: Adult / Female / Humans / Pregnancy Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido