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5,178 results on '"WOOD, Richard"'

2. Targeted calibration to adjust stability biases in non-differentiable complex system models

Author

Pals, Daniel, Bathiany, Sebastian, Wood, Richard, and Boers, Niklas

Subjects
Physics - Atmospheric and Oceanic Physics
Abstract

Numerical models of complex systems like the Earth system are expensive to run and involve many uncertain and typically hand-tuned parameters. In the context of anthropogenic climate change, there is particular concern that specific tipping elements, like the Atlantic Meridional Overturning Circulation, might be overly stable in models due to imperfect parameter choices. However, estimates of the critical forcing thresholds are highly uncertain because the parameter spaces can practically not be explored. Here, we introduce a method for efficient, systematic, and objective calibration of process-based models. Our method drives the system toward parameter configurations where it loses or gains stability, and scales much more efficiently than a brute force approach. We successfully apply the method to a simple bistable model and a conceptual but physically plausible model of the global ocean circulation, demonstrating that our method can help find hidden tipping points, and can calibrate complex models under user-defined constraints.

Published
2024

4. Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning

Author

Hu, Annie, Stockman, Samuel, Wu, Xun, Wood, Richard, Zhi, Bangdong, and Chén, Oliver Y.

Subjects
Computer Science - Machine Learning, Statistics - Applications
Abstract

Early and timely prediction of patient care demand not only affects effective resource allocation but also influences clinical decision-making as well as patient experience. Accurately predicting patient care demand, however, is a ubiquitous challenge for hospitals across the world due, in part, to the demand's time-varying temporal variability, and, in part, to the difficulty in modelling trends in advance. To address this issue, here, we develop two methods, a relatively simple time-vary linear model, and a more advanced neural network model. The former forecasts patient arrivals hourly over a week based on factors such as day of the week and previous 7-day arrival patterns. The latter leverages a long short-term memory (LSTM) model, capturing non-linear relationships between past data and a three-day forecasting window. We evaluate the predictive capabilities of the two proposed approaches compared to two na\"ive approaches - a reduced-rank vector autoregressive (VAR) model and the TBATS model. Using patient care demand data from Rambam Medical Center in Israel, our results show that both proposed models effectively capture hourly variations of patient demand. Additionally, the linear model is more explainable thanks to its simple architecture, whereas, by accurately modelling weekly seasonal trends, the LSTM model delivers lower prediction errors. Taken together, our explorations suggest the utility of machine learning in predicting time-varying patient care demand; additionally, it is possible to predict patient care demand with good accuracy (around 4 patients) three days or a week in advance using machine learning.

Published
2024

26. Variation in Practice Surrounding Antegrade Colonic Enema Channel Placement

Author

Kwon, Eustina G., Kreiss, Jenny, Nicassio, Lauren, Austin, Kelly, Avansino, Jeffrey R., Badillo, Andrea, Calkins, Casey M., Crady, Rachel C., Dickie, Belinda, Durham, Megan M., Frischer, Jason, Fuller, Megan K., Speck, K. Elizabeth, Reeder, Ron W., Rentea, Rebecca, Rollins, Michael D., Saadai, Payam, Wood, Richard J., van Leeuwen, Kathleen D., Smith, Caitlin A., and Rice-Townsend, Samuel E.

Published
2024
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38. Uniting Parametric Uncertainty and Tipping Diagrams

Author

Lux, Kerstin, Ashwin, Peter, Wood, Richard, and Kuehn, Christian

Subjects
Physics - Atmospheric and Oceanic Physics, Mathematics - Dynamical Systems, Mathematics - Probability, 34F10, 62F15, 37H10, 60H35, 65C50, 65C20
Abstract

Various subsystems of the Earth system may undergo critical transitions by passing a so-called tipping point, under sustained changes to forcing. For example, the Atlantic Meridional Overturning Circulation (AMOC) is of particular importance for North Atlantic heat transport and is thought to be potentially at risk of tipping. Given a model of such a subsystem that accurately includes the relevant physical processes, whether tipping occurs or not, will depend on model parameters that typically are uncertain. Reducing this parametric uncertainty is important to understand the likelihood of tipping behavior being present in the system and possible tipping locations. In this letter, we develop improved estimates for the parametric uncertainty by inferring probability distributions for the model parameters based on physical constraints and by using a Bayesian inversion technique. To visualize the impact of parametric uncertainty, we extend classical tipping diagrams by visualizing probabilistic bifurcation curves according to the inferred distribution of the model parameter. Furthermore, we highlight the uncertain locations of tipping points along the probabilistic bifurcation curves. We showcase our probabilistic visualizations of the tipping behavior using a simple box-model of the AMOC, the Stommel-Cessi model [5].

Published
2021

41. Does Delayed Diagnosis of Hirschsprung Disease Impact Post-operative and Functional Outcomes? A Multi-Center Review From the Pediatric Colorectal and Pelvic Learning Consortium

Author

Ullrich, Sarah, Austin, Kelly, Avansino, Jeffrey R., Badillo, Andrea, Calkins, Casey M., Crady, Rachel C., Durham, Megan M., Fuller, Megan K., Rana, Ankur, Reeder, Ron W., Rentea, Rebecca M., Rollins, Michael D., Saadai, Payam, Speck, K. Elizabeth, Wood, Richard J., van Leeuwen, Kathleen, and Frischer, Jason S.

Published
2024
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43. Comparison of the PCPLC Database to NSQIP-P: A Patient Matched Comparison of Surgical Complications Following Repair of Anorectal Malformation

Author

McNevin, Kathryn, Nicassio, Lauren, Rice-Townsend, Samuel E., Katz, Cindy B., Goldin, Adam, Avansino, Jeffrey, Calkins, Casey M., Durham, Megan M., Page, Kent, Ralls, Matthew W., Reeder, Ron W., Rentea, Rebecca M., Rollins, Michael D., Saadai, Payam, Wood, Richard J., van Leeuwen, Kathleen D., and Smith, Caitlin A.

Published
2024
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