Zobrazeno 1 - 10
of 466
pro vyhledávání: '"GOLDENBERG, ANNA"'
Autor:
Nagaraj, Sujay, Goodwin, Andrew J., Lopushanskyy, Dmytro, Eytan, Danny, Greer, Robert W., Goodfellow, Sebastian D., Assadi, Azadeh, Jayarajan, Anand, Goldenberg, Anna, Mazwi, Mjaye L.
Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is importan
Externí odkaz:
http://arxiv.org/abs/2409.00041
Scheduling laboratory tests for ICU patients presents a significant challenge. Studies show that 20-40% of lab tests ordered in the ICU are redundant and could be eliminated without compromising patient safety. Prior work has leveraged offline reinfo
Externí odkaz:
http://arxiv.org/abs/2402.07344
Autor:
Nagaraj, Sujay, Gerych, Walter, Tonekaboni, Sana, Goldenberg, Anna, Ustun, Berk, Hartvigsen, Thomas
Many sequential classification tasks are affected by label noise that varies over time. Such noise can cause label quality to improve, worsen, or periodically change over time. We first propose and formalize temporal label noise, an unstudied problem
Externí odkaz:
http://arxiv.org/abs/2402.04398
Drug synergy, characterized by the amplified combined effect of multiple drugs, is critically important for optimizing therapeutic outcomes. Limited data on drug synergy, arising from the vast number of possible drug combinations and testing costs, m
Externí odkaz:
http://arxiv.org/abs/2305.14517
Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time. Unfortunately, even when model updates improve aggregate metrics such as accuracy, they can lead
Externí odkaz:
http://arxiv.org/abs/2305.04135
Autor:
Hua, Stanley Bryan Z., Rickard, Mandy, Weaver, John, Xiang, Alice, Alvarez, Daniel, Velear, Kyla N., Sheth, Kunj, Tasian, Gregory E., Lorenzo, Armando J., Goldenberg, Anna, Erdman, Lauren
Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from vide
Externí odkaz:
http://arxiv.org/abs/2212.13535
Identifying change points (CPs) in a time series is crucial to guide better decision making across various fields like finance and healthcare and facilitating timely responses to potential risks or opportunities. Existing Change Point Detection (CPD)
Externí odkaz:
http://arxiv.org/abs/2211.03991
Clinician-facing predictive models are increasingly present in the healthcare setting. Regardless of their success with respect to performance metrics, all models have uncertainty. We investigate how to visually communicate uncertainty in this settin
Externí odkaz:
http://arxiv.org/abs/2210.12220
Real-world time series data are often generated from several sources of variation. Learning representations that capture the factors contributing to this variability enables a better understanding of the data via its underlying generative process and
Externí odkaz:
http://arxiv.org/abs/2202.02262