Kim D Nolen

发表

Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record’s Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study

P. Elkin, D. Schlegel, J. Mardekian, 2021, Journal of medical Internet research.

Usability and Perceived Usefulness of the AFib 2gether Mobile App in a Clinical Setting: Single-Arm Intervention Study

David McManus, Alok Kapoor, Anna Hayes, 2021, JMIR cardio.

Usability and Perceived Usefulness of the AFib 2gether™ Mobile App in a Clinical Setting: Single-Arm Intervention Study (Preprint)

D. McManus, K. Mazor, C. Possidente, 2021 .

SCREENING OF PATIENTS AT RISK FOR WILD TYPE ATTR-CM USING A COMPUTATIONAL MACHINE LEARNING ALGORITHM

Kensaku Kawamoto, J. Biskupiak, J. Nativi-Nicolau, 2021, Journal of the American College of Cardiology.

Clinical Integration of a Smartphone App for Patients With Chronic Pain: Retrospective Analysis of Predictors of Benefits and Patient Engagement Between Clinic Visits

R. Jamison, E. Ross, L. Nicholls, 2020, Journal of medical Internet research.

Applying diagnosis support systems in electronic health records to identify wild-type transthyretin amyloid cardiomyopathy risk.

Kensaku Kawamoto, K. Nolen, T. Kannampallil, 2022, Future cardiology.

Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center

T. Kannampallil, D. Lenihan, Joshua D. Mitchell, 2022, Clinical Medicine Insights. Cardiology.

Usability, Perceived Usefulness, and Shared Decision-Making Features of the AFib 2gether Mobile App: Protocol for a Single-Arm Intervention Study

D. McManus, K. Mazor, C. Possidente, 2021, JMIR research protocols.

Performance Evaluation of A Machine Learning Model For Systematic Identification of Wild-type Transthyretin Amyloid Cardiomyopathy At Two Academic Medical Centers

Miriam R. Elman, A. Masri, S. Heitner, 2020 .

Technical Feasibility of Implementing and Commercializing a Machine Learning Model for Rare Disease Prediction

G. Koutitas, S. Attal, Kim D Nolen, 2023, IEEE Access.

Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record’s Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study

P. Elkin, D. Schlegel, J. Mardekian, 2021, Journal of medical Internet research.