Diseño e implementación de un software para la fenotipacion y etiquetado de historias clínicas electrónicas de mujeres gestantes en las entidades de salud públicas en Medellín

By dmp27

  • Lina Patricia David Gonzalez

    Lina Patricia David Gonzalez

  • Daniel Muñoz Paredes

    Daniel Muñoz Paredes

  • Esneider Cano Londoño

    Esneider Cano Londoño

  • Laura Velasquez Areiza

    Laura Velasquez Areiza

  • Mateo Gomez Henao

    Mateo Gomez Henao

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    Libro de Ingeniería Biomédica

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  • Diseño de una Clase de Sistemas Borrosos Recurrentes Mediante Algoritmos Genéticos Para la Tarea de Clasificación de Patrones

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  • Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning

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  • Application of neural networks for prediction of fetal outcome

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  • The prediction of fetal outcome by applying neural network for evaluation of CTG records

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  • Detection and Identification of Novel Metabolomic Biomarkers in Preeclampsia

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  • Neural networks to estimate the risk for preeclampsia occurrenceNeural networks to estimate the risk for preeclampsia occurrence

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  • Robust Early Pregnancy Prediction of Later Preeclampsia Using Metabolomic Biomarkers

    Kenny, L. C., Broadhurst, D. I., Dunn, W., Brown, M., North, R. A., McCowan, L., ... & Baker, P. N. (2010). Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers. Hypertension, 56(4), 741-749.

  • Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes

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  • Development of e-health network for in-home pregnancy surveillance based on artificial intelligence

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  • Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

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  • Artificial Neural Networks Application to Predict Type of Pregnancy in Women Equal or Greater Than 35 Years of Age

    Nouhjah, S. y Kalhori, SRN (2014). Aplicación de redes neuronales artificiales para predecir el tipo de embarazo en mujeres mayores de 35 años. Ciudad , 83 , 41-5

  • A comparative study of classification algorithms for predicting gestational risks in pregnant women

    Lakshmi, B. N., Indumathi, T. S., & Ravi, N. (2015, November). A comparative study of classification algorithms for predicting gestational risks in pregnant women. In 2015 International Conference on Computers, Communications, and Systems (ICCCS) (pp. 42-46). IEEE.

  • Recurrent neural networks in medical data analysis and classifications

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  • Expert system for predicting the early pregnancy with disorders using artificial neural network

    Maylawati, D. S. A., Ramdhani, M. A., Zulfikar, W. B., Taufik, I., & Darmalaksana, W. (2017, August). Expert system for predicting the early pregnancy with disorders using artificial neural network. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-6). IEEE.

  • Exploring physiological indicators of pregnancy outcomes using machine learning and longitudinal patient data

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  • Use of Machine Learning Algorithms for Prediction of Fetal Risk using Cardiotocographic Data

    Hoodbhoy, Z., Noman, M., Shafique, A., Nasim, A., Chowdhury, D. y Hasan, B. (2019). Uso de algoritmos de aprendizaje automático para la predicción del riesgo fetal a partir de datos cardiotocográficos. Revista internacional de investigación médica básica y aplicada , 9 (4), 226.

  • Early prediction of preeclampsia via machine learning

    Marić, I., Tsur, A., Aghaeepour, N., Montanari, A., Stevenson, D. K., Shaw, G. M., & Winn, V. D. (2020). Early prediction of preeclampsia via machine learning. American Journal of Obstetrics & Gynecology MFM, 2(2), 100100.

  • Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

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  • A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes

    Schmidt, L. J., Rieger, O., Neznansky, M., Hackelöer, M., Dröge, L. A., Henrich, W., ... & Verlohren, S. (2022). A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes. American Journal of Obstetrics and Gynecology.