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How do the results of machine learning analysis compare to traditional statistics when applied to the results of the APRICOT (Anesthesia Practice In Children Observational Trial) study?

Posted on Nov 04, 2019 by

Investigate whether machine learning (artificial intelligence) can help to identify the risk factors for severe critical events in children undergoing general anesthesia in a more accurate way than standard statistical methodology for the de-identified APRICOT dataset.

Evaluation of Cerebral and Visceral Perfusion using High Resolution NIRS in Posterior Spinal Fusion Patients

Posted on Nov 04, 2019 by

Severe constipation is a common finding in children who undergo back surgery such as the posterior spinal fusion operation for scoliosis. The cause of this is unknown. We utilize a simple, non-invasive device, the NIRS, to assess changes in the gut and brain perfusion during spine surgery and have noticed incidentally that gut perfusion appears to deteriorate independently of brain perfusion over the course of the procedure.

A Comparison of Provider Intuition to Machine-Learning in Predicting Risk for 7-day Re-Admissions in a Pediatric Acute Care Unit

Posted on Nov 04, 2019 by

Efforts to decrease hospital readmissions have recently intensified due in large part to the Hospital Readmissions Reduction Program as part of the Patient Protection and Affordable Care Act. Pediatric hospital readmissions, although not yet linked to CMS payment metrics, have become a recent focus for hospital systems to prepare for possible future penalties.

Prediction of Non-Invasive Ventilation Non-response in Pediatrics: Assessing Near Realtime Physiologic Data, Machine Learning, and the Development of a Clinical Decision Support System.

Posted on Nov 04, 2019 by

Acute respiratory failure (ARF) is a common reason for intensive care unit (ICU) admission and, if severe, may require invasive endotracheal intubation and mechanical ventilation (MV). Non-invasive ventilation (NIV) respiratory modalities have been developed and used in children with ARF to prevent endotracheal intubation since the late 1990s.

The Use of Machine Learning to Predict Relapse/Progression Among Patients With Newly Diagnosed Childhood Pediatric Acute Lymphoblastic Leukemia

Posted on Nov 04, 2019 by

We are proposing a study to use machine learning to develop a prognostic model that can be used at the time of childhood acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) diagnosis to determine a patient’s risk of relapse/progression.

Improvement of Perioperative Clinical Outcomes for Pediatric Patients: Utilizing machine learning for the development of a real time clinical decision support system

Posted on Nov 04, 2019 by

It is our hypothesis that through the use of machine learning methods we can identify the scenarios leading up to laryngospasm and potentially prevent them altogether.

Machine Learning and Visual Analytics to Improve Outcomes in the Adolescent Idiopathic Patient Population

Posted on Nov 04, 2019 by

Outcomes following scoliosis surgery continue to improve through advances in knowledge and medical practice. However, little is known about rare or long term complications of scoliosis surgery.

Machine Learning for Prediction of Congenital Diaphragmatic Hernia (CDH) Repair Utilization and Outcomes

Posted on Nov 04, 2019 by

The development of machine learning tools for the prediction of patient outcomes undergoing congenital diaphragmatic hernia repair surgery is the aim of our project.