Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

Paul, Robert; Cho, Kyu; Mellins, Claude A.; Malee, Kathleen; Robbins, Reuben N; Kerr, Stephen J.; Sophonphan, Jiratchaya; Jahanshad, Neda; Aurpibul, Linda; Thongpibul, Kulvadee; Kosalaraksa, Pope; Kanjanavanit, Suparat; Ngampiyaskul, Chaiwat; Luesomboon, Wicharn; Wongsawat, Jurai; Saphonn, Vonthanak; Chettra, Kea; Valcour, Victor; Balla, Lila; García-Egan, P; Preston, Rebecca; Ananworanich, Jintanat; Puthanakit, Thanyawee

doi:10.1101/632273

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Regarding specific metrics, a study utilizing a novel machine learning algorithm [ 13 ] aimed to identify clinically significant predictors of neurocognitive development in newborns with perinatal human immunodeficiency virus (HIV). Through multifactor regression with gradient boosting and fivefold cross-validation, the study successfully identified the predictors that have the greatest impact on the neurocognitive stability of newborns.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods for Pregnancy and Childbirth Risk Management

Kopanitsa

Metsker

Kovalchuk

2023

JPM

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Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusing on pregnancy and childbirth risks. The timely identification of risk factors during early pregnancy, along with risk management, mitigation, prevention, and adherence management, can significantly reduce adverse perinatal outcomes and complications for both mother and child. Given the existing burden on medical professionals, clinical decision support systems (CDSSs) can play a role in risk management. However, these systems require high-quality decision support models based on validated medical data that are also clinically interpretable. To develop models for predicting childbirth risks and due dates, we conducted a retrospective analysis of electronic health records from the perinatal Center of the Almazov Specialized Medical Center in Saint-Petersburg, Russia. The dataset, which was exported from the medical information system, consisted of structured and semi-structured data, encompassing a total of 73,115 lines for 12,989 female patients. Our proposed approach, which includes a detailed analysis of predictive model performance and interpretability, offers numerous opportunities for decision support in perinatal care provision. The high predictive performance achieved by our models ensures precise support for both individual patient care and overall health organization management.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Methods for Pregnancy and Childbirth Risk Management

Kopanitsa

Metsker

Kovalchuk

2023

JPM

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Deep Phenotyping of HIV Neurocognitive Complications Among Individuals Residing in High-Income Countries

Paul

García-Egan

Bolzenius

et al. 2020

Neurocognitive Complications of HIV-Infection

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Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care

Variane

Camargo²,

Rodrigues

et al. 2022

Front. Pediatr.

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Neonatology has experienced a significant reduction in mortality rates of the preterm population and critically ill infants over the last few decades. Now, the emphasis is directed toward improving long-term neurodevelopmental outcomes and quality of life. Brain-focused care has emerged as a necessity. The creation of neonatal neurocritical care units, or Neuro-NICUs, provides strategies to reduce brain injury using standardized clinical protocols, methodologies, and provider education and training. Bedside neuromonitoring has dramatically improved our ability to provide assessment of newborns at high risk. Non-invasive tools, such as continuous electroencephalography (cEEG), amplitude-integrated electroencephalography (aEEG), and near-infrared spectroscopy (NIRS), allow screening for seizures and continuous evaluation of brain function and cerebral oxygenation at the bedside. Extended and combined uses of these techniques, also described as multimodal monitoring, may allow practitioners to better understand the physiology of critically ill neonates. Furthermore, the rapid growth of technology in the Neuro-NICU, along with the increasing use of telemedicine and artificial intelligence with improved data mining techniques and machine learning (ML), has the potential to vastly improve decision-making processes and positively impact outcomes. This article will cover the current applications of neuromonitoring in the Neuro-NICU, recent advances, potential pitfalls, and future perspectives in this field.

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Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

Cited by 3 publications

References 42 publications

Machine Learning Methods for Pregnancy and Childbirth Risk Management

Machine Learning Methods for Pregnancy and Childbirth Risk Management

Deep Phenotyping of HIV Neurocognitive Complications Among Individuals Residing in High-Income Countries

Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care

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