4research
Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City.
Link: https://doi.org/10.1371/journal.pone.0297919 Authors: Takkavatakarn, Kullaya; Dai, Yang; Hsun Wen, Huei; Kauffman, Justin; Charney, Alexander; Coca, Steven G; Nadkarni, Girish N; Chan, Lili Abstract: Area-level social determinants of health (SDOH) based on patients’ ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could…
Read MorePediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.
Link: https://doi.org/10.1161/CIRCULATIONAHA.123.067750 Authors: Mayourian, Joshua; La Cava, William G; Vaid, Akhil; Nadkarni, Girish N; Ghelani, Sunil J; Mannix, Rebekah; Geva, Tal; Dionne, Audrey; Alexander, Mark E; Duong, Son Q; Triedman, John K Abstract: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains…
Read MoreDerivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements.
Link: https://doi.org/10.1101/2024.01.30.24301974 Authors: Gulamali, Faris; Jayaraman, Pushkala; Sawant, Ashwin S; Desman, Jacob; Fox, Benjamin; Chang, Annie; Soong, Brian Y; Arivazaghan, Naveen; Reynolds, Alexandra S; Duong, Son Q; Vaid, Akhil; Kovatch, Patricia; Freeman, Robert; Hofer, Ira S; Sakhuja, Ankit; Dangayach, Neha S; Reich, David S; Charney, Alexander W; Nadkarni, Girish N Abstract: Increased intracranial pressure (ICP)…
Read MoreRemote Monitoring and Artificial Intelligence: Outlook for 2050.
Link: https://doi.org/10.1213/ANE.0000000000006712 Authors: Feinstein, Max; Katz, Daniel; Demaria, Samuel; Hofer, Ira S Abstract: Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively.…
Read MoreQuantitative Prediction of Right Ventricular Size and Function From the ECG.
Link: https://doi.org/10.1161/JAHA.123.031671 Authors: Duong, Son Q; Vaid, Akhil; My, Vy Thi Ha; Butler, Liam R; Lampert, Joshua; Pass, Robert H; Charney, Alexander W; Narula, Jagat; Khera, Rohan; Sakhuja, Ankit; Greenspan, Hayit; Gelb, Bruce D; Do, Ron; Nadkarni, Girish N Abstract: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional…
Read MoreEvaluating the role of ChatGPT in gastroenterology: a comprehensive systematic review of applications, benefits, and limitations.
Link: https://doi.org/10.1177/17562848231218618 Authors: Klang, Eyal; Sourosh, Ali; Nadkarni, Girish N; Sharif, Kassem; Lahat, Adi Abstract: The integration of artificial intelligence (AI) into healthcare has opened new avenues for enhancing patient care and clinical research. In gastroenterology, the potential of AI tools, specifically large language models like ChatGPT, is being explored to understand their utility and…
Read MoreA novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.
Link: https://doi.org/S0933-3657(23)00264-6 Authors: Oh, Wonsuk; Jayaraman, Pushkala; Tandon, Pranai; Chaddha, Udit S; Kovatch, Patricia; Charney, Alexander W; Glicksberg, Benjamin S; Nadkarni, Girish N Abstract: Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However,…
Read MoreMachine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.
Link: https://doi.org/10.1186/s12882-023-03424-7 Authors: Takkavatakarn, Kullaya; Oh, Wonsuk; Cheng, Ella; Nadkarni, Girish N; Chan, Lili Abstract: End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advanced care…
Read MoreJoint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.
Link: https://doi.org/10.2196/51844 Authors: Abbott, Ethan E; Oh, Wonsuk; Dai, Yang; Feuer, Cole; Chan, Lili; Carr, Brendan G; Nadkarni, Girish N Abstract: Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). We conducted a cluster…
Read MoreDeep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.
Link: https://doi.org/10.3390/diagnostics13243613 Authors: Klang, Eyal; Sourosh, Ali; Nadkarni, Girish N; Sharif, Kassem; Lahat, Adi Abstract: Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review…
Read More