Risk Stratification for Acute Gastrointestinal Bleeding
Shung DL, Au B, Taylor RA, Tay JK, Laursen SB, Stanley AJ, Dalton HR, Ngu J, Schultz M, and Laine L. Validation of a machine learning model that outperforms clinical risk scoring systems for upper gastrointestinal bleeding. Gastroenterology. 2020 Jan;158(1):160-167. doi: 10.1053/j.gastro.2019.09.009. Epub 2019 Sep 25.
Shung D, Simonov M, Tsay C, Kawamura Y, Partridge C, Thomas P, Zheng N, Tay K, Hsiao A, Laine, L. External Validation of an External Validation of an Electronic Health Record-Based Deep Learning Model for Automated Rapid Risk Stratification of Patients Presenting with Acute Gastrointestinal Bleeding. Digestive Diseases Week, San Diego, May, 2022. (Oral presentation)
Dynamic Risk Prediction
Shung D, Huang J, Castro E, Tay JK, Simonov M, Laine L, Batra R, Krishnaswamy S. Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit. Sci Rep 2021; 11, 8827.
Finding Patients via EHR Phenotyping or Signal Processing on Knowledge Graphs
Shung D, Tsay C, Laine L, Chang D, Li F, Thomas P, Partridge C, Simonov M, Hsiao A, Tay JK, Taylor A. Early Identification of Patients with Acute Gastrointestinal Bleeding in the Emergency Department using Electronic Health Record Phenotyping. J Gastroenterol Hepatol. 2020; 36 (6), 1590-1597
Tong A*, Huguet G*, Shung D*, Natik A, Kuchroo M, Lajoie G, Wolf G, Krishnaswamy S. Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance. 47th International Conference on Acoustics, Speech, & Signal Processing (ICASSP), Singapore, May 2022. (Oral Presentation)
Gerasimiuk M*, Shung D*, Tong A, Stanley A, Schultz M, Ngu J, Laine L, Wolf G, Krishnaswamy S. MURAL: An unsupervised random forest-based embedding for electronic health record data. Institute of Electrical and Electronics Engineers International Conference on Big Data: Healthcare Special Session (virtual) 2021. (Oral Presentation)
Epidemiological Trends of Acute Gastrointestinal Bleeding and Economic Analysis of Risk Stratification
Zheng, NS, Tsay, C, Laine, L, Shung, DL. Trends in characteristics, management, and outcomes of patients presenting with gastrointestinal bleeding to emergency departments in the United States from 2006 to 2019. Aliment Pharmacol Ther. 2022; 00: 1– 13. https://doi.org/10.1111/apt.17238
Li DK, Laine L, Shung DL. Trends in Upper Gastrointestinal Bleeding in Patients on Primary Prevention Aspirin: A Nationwide Emergency Department Sample Analysis, 2016-2020. Am J Med. 2023 Sep 9:S0002-9343(23)00542-9.
Shung DL, Lin JK, Laine L. Achieving Value by Risk Stratification with Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis. Am J Gastroenterol. 2023 Sep 27.
Disparities in Acute Gastrointestinal Bleeding
Rodriguez NJ, Zheng N, Mezzacappa C, Canavan M, Laine L, Shung D. Disparities in Access to Endoscopy for Patients with Upper Gastrointestinal Bleeding Presenting to Emergency Departments. Gastroenterology. 2022 Oct 10:S0016-5085(22)01157-X.
Systematic Reviews
Plana D*, Shung DL*, Grimshaw AA, Saraf A, Sung JJY, Kann BH. Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review. JAMA Netw Open. 2022;5(9):e2233946. doi:10.1001/jamanetworkopen.2022.33946
Shung D, Simonov M, Gentry M, Au B, Laine L. Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review. Dig Dis Sci. 2019 May; 64(8):2078–2087
Tsay C, Shung DL, Stemmer Frumento K, Laine L. Early Colonoscopy Does Not Improve Outcome in Lower Gastrointestinal Bleeding: Systematic Review of Randomized Trials. Clin Gastroenterol Hepatol. 2019 Dec 13.
*co-first authorshp