Armando J Lorenzo1, Mandy Rickard2, Luis H Braga3, Yanbo Guo4, John-Paul Oliveria5. 1. Division of Pediatric Urology, Department of Surgery, Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada; Clinical Urology Research Enterprise (CURE) Program, McMaster Children's Hospital, Hamilton, Ontario, Canada. Electronic address: armando.lorenzo@sickkids.ca. 2. Division of Pediatric Urology, Department of Surgery, Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada; Clinical Urology Research Enterprise (CURE) Program, McMaster Children's Hospital, Hamilton, Ontario, Canada. 3. Clinical Urology Research Enterprise (CURE) Program, McMaster Children's Hospital, Hamilton, Ontario, Canada; McMaster Children's Hospital, McMaster University, Hamilton, Ontario, Canada; McMaster Pediatric Surgery Research Collaborative, McMaster University, Hamilton, Ontario, Canada. 4. McMaster Children's Hospital, McMaster University, Hamilton, Ontario, Canada. 5. Clinical Urology Research Enterprise (CURE) Program, McMaster Children's Hospital, Hamilton, Ontario, Canada.
Abstract
OBJECTIVE: To explore the potential value of utilizing a commercially available cloud-based machine learning platform to predict surgical intervention in infants with prenatal hydronephrosis (HN). MATERIALS AND METHODS: A prospective prenatal HN database was uploaded into Microsoft Azure Machine Learning Studio. Probabilistic principal component analysis was employed for data imputation. Multiple clinical variables were included in two-class decision jungle and neural network for model training, using surgical intervention as the primary outcome. Models were scored and evaluated after a 70/30 split of the data. RESULTS: A total of 557 entries were included. The optimized model (decision jungle) achieved an area under the curve of 0.9, accuracy of 0.87, and precision of 0.80, employing a threshold of 0.5 to predict surgery. Average time to train, score and evaluate the model was 5 seconds. The predictive model was deployed as a web service in 35 seconds, generating a unique API key for app and webpage development. Individualized prediction based on the included variables was deployed as a web-based and batch execution Excel file in less than one minute. CONCLUSION: This cloud-based ML technology allows easy building, deployment, and sharing of predictive analytics solutions. Using prenatal HN as an example, we propose an opportunity to address contemporary challenges with data analysis, reporting a creative solution that moves beyond the current standard.
OBJECTIVE: To explore the potential value of utilizing a commercially available cloud-based machine learning platform to predict surgical intervention in infants with prenatal hydronephrosis (HN). MATERIALS AND METHODS: A prospective prenatal HN database was uploaded into Microsoft Azure Machine Learning Studio. Probabilistic principal component analysis was employed for data imputation. Multiple clinical variables were included in two-class decision jungle and neural network for model training, using surgical intervention as the primary outcome. Models were scored and evaluated after a 70/30 split of the data. RESULTS: A total of 557 entries were included. The optimized model (decision jungle) achieved an area under the curve of 0.9, accuracy of 0.87, and precision of 0.80, employing a threshold of 0.5 to predict surgery. Average time to train, score and evaluate the model was 5 seconds. The predictive model was deployed as a web service in 35 seconds, generating a unique API key for app and webpage development. Individualized prediction based on the included variables was deployed as a web-based and batch execution Excel file in less than one minute. CONCLUSION: This cloud-based ML technology allows easy building, deployment, and sharing of predictive analytics solutions. Using prenatal HN as an example, we propose an opportunity to address contemporary challenges with data analysis, reporting a creative solution that moves beyond the current standard.
Authors: Jethro Cc Kwong; Adree Khondker; Jin Kyu Kim; Michael Chua; Daniel T Keefe; Joana Dos Santos; Marta Skreta; Lauren Erdman; Neeta D'Souza; Antoine Fermin Selman; John Weaver; Dana A Weiss; Christopher Long; Gregory Tasian; Chia Wei Teoh; Mandy Rickard; Armando J Lorenzo Journal: Pediatr Nephrol Date: 2021-10-22 Impact factor: 3.714
Authors: Andrew B Chen; Taseen Haque; Sidney Roberts; Sirisha Rambhatla; Giovanni Cacciamani; Prokar Dasgupta; Andrew J Hung Journal: Urol Clin North Am Date: 2021-10-23 Impact factor: 2.766