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Your search keyword '"ANNETTE CHANG"' showing total 3 results
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3 results on '"ANNETTE CHANG"'

1. Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension

Author

Faris Gulamali, Pushkala Jayaraman, Ashwin S. Sawant, Jacob Desman, Benjamin Fox, Annette Chang, Brian Y. Soong, Naveen Arivazagan, Alexandra S. Reynolds, Son Q. Duong, Akhil Vaid, Patricia Kovatch, Robert Freeman, Ira S. Hofer, Ankit Sakhuja, Neha S. Dangayach, David S. Reich, Alexander W. Charney, and Girish N. Nadkarni

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Increased intracranial pressure (ICP) ≥15 mmHg is associated with adverse neurological outcomes, but needs invasive intracranial monitoring. Using the publicly available MIMIC-III Waveform Database (2000–2013) from Boston, we developed an artificial intelligence-derived biomarker for elevated ICP (aICP) for adult patients. aICP uses routinely collected extracranial waveform data as input, reducing the need for invasive monitoring. We externally validated aICP with an independent dataset from the Mount Sinai Hospital (2020–2022) in New York City. The AUROC, accuracy, sensitivity, and specificity on the external validation dataset were 0.80 (95% CI, 0.80–0.80), 73.8% (95% CI, 72.0–75.6%), 73.5% (95% CI 72.5–74.5%), and 73.0% (95% CI, 72.0–74.0%), respectively. We also present an exploratory analysis showing aICP predictions are associated with clinical phenotypes. A ten-percentile increment was associated with brain malignancy (OR = 1.68; 95% CI, 1.09-2.60), intracerebral hemorrhage (OR = 1.18; 95% CI, 1.07–1.32), and craniotomy (OR = 1.43; 95% CI, 1.12–1.84; P

Published
2024
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2. 1033-P: An Interactive Capacity Planning Dashboard for Algorithm-Enabled Telemedicine-Based Diabetes Care

Author

ANNETTE CHANG, MICHAEL Z. GAO, JOHANNES FERSTAD, DAVID M. MAAHS, PRIYA PRAHALAD, RAMESH JOHARI, and DAVID SCHEINKER

Subjects
Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract

We previously showed that the use of a tool for remote analysis of continuous glucose monitor data, Timely Interventions for Diabetes Excellence (TIDE) , was associated with improved glucose management for patients with type 1 diabetes. To facilitate the deployment of TIDE at two partner institutions, we developed an operational planning tool. We designed an interactive dashboard to match capacity and demand with a variety of operational, population, and workforce parameters. To improve the dashboard’s accessibility, simplicity, and interpretability, we performed iterative design based on feedback from users and two partner institutions. We set parameter values based on the first deployment of TIDE and conducted sensitivity analyses of the capacity-demand match. The dashboard (Figure 1) contains seven modifiable parameters; generates a table illustrating the match between capacity and demand; displays a timeseries plot of capacity and demand; and calculates three alternative capacity plans that satisfy annual patient demand. It is available online (www.bit.ly/surf-tide) . Small absolute reductions in per-patient review time produced the most significant changes in clinic capacity. We developed an interactive dashboard that facilitates data-driven operational planning for clinics seeking to deploy algorithm-enabled telemedicine-based diabetes care. Figure 1. Capacity Planning Dashboard Disclosure A.Chang: None. M.Z.Gao: None. J.Ferstad: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. P.Prahalad: None. R.Johari: None. D.Scheinker: None. Funding NIH R18DK122422

Published
2022
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3. Contributors

Author

Abulafia, Adi, Agarwal, Ashvin, Agarwal, Kanika, Ahmad, Ashraf Freddie, Ahmed, Masih U., Al-Mohtaseb, Zaina, Assia, Ehud I., Bailey, Mark D., Barfell, Andrew D., Barrett, Graham, Basti, Surendra, Bhat, Amar Krishna, Brooks, Cassandra C., Böhm, Myriam, Carballo, Sarah E., Chang, David F., Chee, Soon-Phaik, Chen, Lauren E., Chhadva, Priyanka, Cohen, Kenneth L., Commiskey, Patrick W., Culp, Catherine J., Darvish, Mahshad, Davis, Elizabeth A., Dewey, Steven H., Dhaliwal, Deepinder K., Donaldson, Kendall E., Farid, Marjan, Figueiredo, Gabriel B., Findl, Oliver, Fine, I. Howard, Finn, Avni P., Fram, Nicole R., Garg, Sumit (Sam), Gupta, Preeya K., Hackett, Nicholas, Hemkeppler, Eva, Hill, Warren E., Hoffman, Richard S., Houser, Kourtney, Jacob, Soosan, Jalsingh, Ananya, Kalout, Patricia S.O., Kanesa-thasan, Aditya, Kedhar, Sanjay R., Khandelwal, Sumitra, Khurana, Rahul N., Kim, Terry, Kleinmann, Guy, Knezevic, Alexander, Koch, Douglas D., Kohnen, Thomas, Kwong, Stephen, Lee, Bryan S., Lee, Olivia L., Lim, Janet M., Lin, Ken Y., Lindstrom, Richard L., Mah, Francis S., Mamalis, Nick, Masket, Samuel, Mc, Guffey, Charles Daniel, Miller, Kevin M, Mittal, Aman, Narang, Priya, Noble, Carl W., Nuijts, Rudy M.M.A, Oetting, Thomas A., Ogawa, Gregory S.H., Osher, Robert H., Osher, James M., Palkovits, Stefan, Pantanelli, Seth Michel, Penatti, Raphael, Velasquez, Mauricio A. Perez, Pettey, Jeff, Pham, Don, Pineda, Roberto, Qu, Phillip, Radcliffe, Nathan M., Riemann, Christopher D., Rubenstein, Jonathan B., Rupnik, Zsófia, Seibel, Barry S., Shah, Manjool, Shah, Ravi, Sims, Annette Chang, Sivaraman, Kavitha R., Snyder, Michael E., Sood, Shana, Tan, Nicholas E., Teichman, Joshua C., Thompson, Vance, Ti, Seng-Ei, Venkatesh, Rengaraj, Venkateswaran, Nandini, Ventura, Bruna V., Ventura, Marcelo C., Viriya, Elizabeth T., Wade, Matthew, Wagner, Kirsten, Walter, Keith, Wang, Li, Wang, Yvonne, Waring, George O., IV, Warren, Alexis K., Warren, Keith A., Webers, Valentijn S.C., Weikert, Mitchell P., Werner, Liliana, Yeu-Lin, Elizabeth, and Zhou, Elaine J.

Published
2023
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