Full Text Journal Articles by
Author Scott M Pappada


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Machine learning in medicine: It has arrived, let's embrace it.

Scott M Pappada,

Machine learning and artificial intelligence (AI) have arrived in medicine and the healthcare community is experiencing significant growth in their adoption across numerous patient care settings. There are countless applications for machine learning and AI in medicine ranging from patient outcome prediction, to clinical decision support, to predicting future patient ... Read more >>

J Card Surg (Journal of cardiac surgery)
[2021, 36(11):4121-4124]

Cited: 0 times

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An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.

Scott M Pappada, Mohammad Hamza Owais, Brent D Cameron, Juan C Jaume, Ana Mavarez-Martinez, Ravi S Tripathi, Thomas J Papadimos,

<b><i>Background:</i></b> Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit (ICU) length of stay, and morbidities such as infection. The myriad of illnesses and patient conditions render maintenance of glycemic control very challenging in this setting. <b><i>Materials ... Read more >>

Diabetes Technol Ther (Diabetes technology & therapeutics)
[2020, 22(5):383-394]

Cited: 2 times

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Presumed antiphospholipid syndrome and thrombotic thrombocytopenic purpura: An infrequent association.

Hallie Hanna Dolin, Mark Dziuba, Scott M Pappada, Thomas John Papadimos,

Antiphospholipid syndrome (APS) is an autoimmune disease that demonstrates antiphospholipid antibodies that cause hypercoagulability and leads to venous and arterial thrombosis. Autoantibodies to a disintegrin-like and metalloprotease with thrombospondin type I motif, member 13 (ADAMTS 13) play a role in the microthrombosis of thrombotic thrombocytopenic purpura in APS patients. ... Read more >>

Clin Case Rep (Clinical case reports)
[2019, 7(10):1984-1988]

Cited: 1 time

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Continuous glucose monitoring identifies relationship between optimized glycemic control and post-discharge acute care facility needs.

Scott M Pappada, Karina Woodling, Mohammad Hamza Owais, Evan M Zink, Layth Dahbour, Ravi S Tripathi, Sadik A Khuder, Thomas J Papadimos,

<h4>Objective</h4>Hyperglycemia is an independent risk factor in hospitalized patients for adverse outcomes, even if patients are not diabetic. We used continuous glucose monitoring to evaluate whether glycemic control (hyperglycemia) in the first 72 h after an intensive care admission was associated with the need for admission to a post discharge long-term ... Read more >>

BMC Res Notes (BMC research notes)
[2018, 11(1):533]

Cited: 0 times

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Increasing patient safety with neonates via handoff communication during delivery: a call for interprofessional health care team training across GME and CME.

Allison A Vanderbilt, Scott M Pappada, Howard Stein, David Harper, Thomas J Papadimos,

Hospitals have struggled for years regarding the handoff process of communicating patient information from one health care professional to another. Ineffective handoff communication is recognized as a serious patient safety risk within the health care community. It is essential to take communication into consideration when examining the safety of neonates ... Read more >>

Adv Med Educ Pract (Advances in medical education and practice)
[2017, 8:365-367]

Cited: 4 times

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Comorbidity polypharmacy score and its clinical utility: A pragmatic practitioner's perspective.

Stanislaw P Stawicki, Sarathi Kalra, Christian Jones, Carla F Justiniano, Thomas J Papadimos, Sagar C Galwankar, Scott M Pappada, John J Feeney, David C Evans,

Modern medical management of comorbid conditions has resulted in escalating use of multiple medications and the emergence of the twin phenomena of multimorbidity and polypharmacy. Current understanding of how the polypharmacy in conjunction with multimorbidity influences trauma outcomes is limited, although it is known that trauma patients are at increased ... Read more >>

J Emerg Trauma Shock (Journal of emergencies, trauma, and shock)
[2015, 8(4):224-231]

Cited: 15 times

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Evaluation of a model for glycemic prediction in critically ill surgical patients.

Scott M Pappada, Brent D Cameron, David B Tulman, Raymond E Bourey, Marilyn J Borst, William Olorunto, Sergio D Bergese, David C Evans, Stanislaw P A Stawicki, Thomas J Papadimos,

We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic ... Read more >>

PLoS One (PloS one)
[2013, 8(7):e69475]

Cited: 4 times

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Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes.

Scott M Pappada, Brent D Cameron, Paul M Rosman, Raymond E Bourey, Thomas J Papadimos, William Olorunto, Marilyn J Borst,

<h4>Background</h4>Continuous glucose monitoring (CGM) technologies report measurements of interstitial glucose concentration every 5 min. CGM technologies have the potential to be utilized for prediction of prospective glucose concentrations with subsequent optimization of glycemic control. This article outlines a feed-forward neural network model (NNM) utilized for real-time prediction of glucose.<h4>Methods</h4>A feed-forward ... Read more >>

Diabetes Technol Ther (Diabetes technology & therapeutics)
[2011, 13(2):135-141]

Cited: 36 times

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Development of a neural network model for predicting glucose levels in a surgical critical care setting.

Scott M Pappada, Marilyn J Borst, Brent D Cameron, Raymond E Bourey, Jason D Lather, Desmond Shipp, Antonio Chiricolo, Thomas J Papadimos,

Development of neural network models for the prediction of glucose levels in critically ill patients through the application of continuous glucose monitoring may provide enhanced patient outcomes. Here we demonstrate the utilization of a predictive model in real-time bedside monitoring. Such modeling may provide intelligent/directed therapy recommendations, guidance, and ultimately ... Read more >>

Patient Saf Surg (Patient safety in surgery)
[2010, 4(1):15]

Cited: 4 times

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Development of a neural network for prediction of glucose concentration in type 1 diabetes patients.

Scott M Pappada, Brent D Cameron, Paul M Rosman,

<h4>Background</h4>A major difficulty in the management of diabetes is the optimization of insulin therapies to avoid occurrences of hypoglycemia and hyperglycemia. Many factors impact glucose fluctuations in diabetes patients, such as insulin dosage, nutritional intake, daily activities and lifestyle (e.g., sleep-wake cycles and exercise), and emotional states (e.g., stress). The ... Read more >>

J Diabetes Sci Technol (Journal of diabetes science and technology)
[2008, 2(5):792-801]

Cited: 24 times

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