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C17 Prediction of Critical Pediatric Perioperative Adverse Events using the APRICOT Dataset

Hannah Lonsdale
Clinical Research Associate at The Johns Hopkins University
+ 5 speakers
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Machine Learning for Healthcare
August 8, 2020, Online, Los Angeles, CA, USA
Machine Learning for Healthcare
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C17 Prediction of Critical Pediatric Perioperative Adverse Events using the APRICOT Dataset
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About speakers

Hannah Lonsdale
Clinical Research Associate at The Johns Hopkins University
Ali Jalali
Senior Data Scientist at Biofourmis
Luis Ahumada
ДолжностьDirector Center for Pediatric Data Science and Analytic Methodology at Johns Hopkins All Children's Hospital
Mohamed Rehman
Eric Kobren Professor of Applied Health Informatics at The Johns Hopkins University School of Medicine
Walid Habre
Head Anesthesiological Investigations Unit at Université de Genève
Nicola Disma
Director of Unit for Research & InnovationConsultant paediatric Anaesthetist at IRCCS Ospedale Pediatrico Giannina Gaslini

Senior Data Scientist and Personalized Predictions Engine Team Lead at Biofourmis. Responsible for leading a talented team of data scientists focusing on utilizing data from various sources to predict patient deterioration or state of health. Experienced Doctor of Philosophy (Ph.D.) Data Scientist with deep knowledge and understanding of Data Science, Machine Learning, Mathematical Modeling, Data Visualization, Python, R, Matlab, Amazon Web services. Hands on experience on retrieving data, data pre-processing, and model development. Research professional with a strong track record of peer reviewed publications.

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Health Data Science professional and researcher at the enterprise level with novel and practical experience in biomedical and healthcare informatics, with demonstrated expertise in strategic visioning and effective leadership. Currently leading JHACH enterprise Data Science efforts for clinical care, research & operations. Extensive knowledge and hands‐on experience in the areas of data/text mining, web mining, machine learning, information retrieval, social network analysis and knowledge representation, and extensive expertise in: • Parametric and Nonparametric Bayesian statistics. • Classification (Neural Networks, Naive Bayes Classifier, Bayesian Networks, Decision Trees, SVM, etc.) • Clustering Analysis. • Natural Language Processing NLP. • Information retrieval and Information extraction. • Knowledge organization and representation (Ontology, metadata, and social tags). • Demonstrated ability to develop innovative data science approaches, methods, and algorithms. • Experienced in analyzing large‐scale imperfect real world datasets. Domain Specialties: Healthcare Informatics, Bioinformatics, Advanced Analytics, Artificial Intelligence, Data Mining, Machine Learning, Knowledge Management, Information Visualization, Visual Analytics, Intelligent Systems, Case Based Reasoning (CBR).

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Mohamed Rehman, M.D., is chair of the Department of Anesthesia at Johns Hopkins All Children’s Hospital and professor of anesthesiology and critical care and pediatrics with the Johns Hopkins University School of Medicine. Nationally recognized for his medical and clinical informatics expertise. Previously Dr. Rehman was a professor of clinical anesthesiology and critical care and professor of pediatrics at the University of Pennsylvania School of Medicine. He held numerous leadership roles at the Children’s Hospital of Philadelphia, including director of transplant anesthesia, and was the anesthesia team leader for the world’s first bilateral hand transplant and several conjoint twin separations. Dr. Rehman is board certified in anesthesiology, with subspecialty certification in critical care medicine and pediatric anesthesia. He also holds certifications from the American Board of Pediatrics and the American Board of Preventive Medicine, clinical informatics subspecialty. A graduate of Mysore Medical College, Mysore, India, he completed a pediatric residency at Chicago’s Cook County Hospital, an anesthesia residency at the University of Miami, and a fellowship in pediatric anesthesiology and critical care at Children’s Hospital of Philadelphia. He is the author of more than 50 original research publications and review articles and more than 70 scientific abstracts.

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Graduated in Anesthesia and Intensive Care in 1987, I have been working at the University Hospitals of Geneva since 1989. I started my subspecialty in Pediatric anesthesia in 1992 with one year in neonatology and pediatric intensive care, followed by 18 months as a Postgraduate Research Fellow, at Princess Margaret Hospital for Children, Perth - Western Australia. and the Research Institute for Child Health. I was appointed as consultant at the Pediatric Anesthesia Unit at the Children's hospital (University Hospitals of Geneva) in 1997 and became the head of the unit in 2000. Parallel to this, I was appointed as associate, than full professor at the University of Geneva in 2015 and was appointed as head of the unit for anesthesiological investigations at the Department of Anesthesiology. Currently, I share my time as senior consultant in pediatric anesthesia and head of research at the Department of Anesthesiology.

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I'm Doctor Hannah Lonsdale presenting on behalf of our team Johns Hopkins. All Children's Hospital and this is the prediction of critical Pediatric. Despite the unparalleled safety Magnum a seizure techniques. It is possible for serious perioperative on this event to occur disability. Child. Previous studies on Perryopolis events have been clinically a single institution studies with insufficient data sets size. How to study of rabbits event existing predictive models based on these small study music? Classical

decision tree with a very limited number of him. And this project has the largest pre-existing perioperative pediatric, doctor mistype in the world, the anesthesia practice and children observational trial apricots, to build a machine learning model that can accurately predict the risk of any pediatric therapist about this event for an individual child The apricot dates that contains over 30,000 Reckless Cowboys from over 250 participating census across thirty three European countries and includes information of demographics medical history surgery.

The individuals to Bear Creek School events are listed in the stable and include surgery, cardiovascular and neurological. And truly like to complications the overall address Eventbrite was highly polished and balanced. A drink, reduce the volume to 25% and then split to divide the day into 70% training validation. Bayesian optimization was used for the models type of piranhas. the best performing model, was it your will that work and demonstrated accuracy of .82 Ricola .53 and area under the receiver of racing

characteristic, curve of 0.7 Percocet Individual identifying patients at high risk of Crisco perioperative aggressive and his potential clinical utility through helping Healthcare Providers improve outcomes, by highlighting. The need to consider modifying their school to a communication with patients and their families in between members of the surgical team. We look forward to further developing a tradition to give more detail in predicting. This patient's risk, including the development of

in serviceability for the mobile on. So the deal anything with the geological system is affected for cardiovascular. We could use a calculator and intuitive user interface using visual analytics muscles and a bling Frontline clinical stuff on that patient care. Email time. Thank you.

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Hannah Lonsdale
Ali Jalali
Luis Ahumada
Mohamed Rehman
Walid Habre
Nicola Disma