Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study

Journals

1. Syed M, Syed S, Sexton K, Syeda H, Garza M, Zozus M, Syed F, Begum S, Syed A, Sanford J, Prior F. Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review. Informatics 2021;8(1):16 View
2. Falconer N, Abdel‐Hafez A, Scott I, Marxen S, Canaris S, Barras M. Systematic review of machine learning models for personalised dosing of heparin. British Journal of Clinical Pharmacology 2021;87(11):4124 View
3. Li D, Gao J, Hong N, Wang H, Su L, Liu C, He J, Jiang H, Wang Q, Long Y, Zhu W. A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study. Journal of Medical Internet Research 2021;23(5):e27118 View
4. Chen H, Ma Y, Hong N, Wang H, Su L, Liu C, He J, Jiang H, Long Y, Zhu W. Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods. BMC Medical Informatics and Decision Making 2021;21(S2) View
5. Cardona M, Craig L, Jones M, Byambasuren O, Obucina M, Hattingh L, Clark J, Glasziou P, Hoffmann T. Guideline Adherence As An Indicator of the Extent of Antithrombotic Overuse and Underuse: A Systematic Review. Global Heart 2022;17(1):55 View
6. Boie S, Engelhardt L, Coenen N, Giesa N, Rubarth K, Menk M, Balzer F. A Recurrent Neural Network Model for Predicting Activated Partial Thromboplastin Time After Treatment With Heparin: Retrospective Study. JMIR Medical Informatics 2022;10(10):e39187 View
7. Abdel-Hafez A, Scott I, Falconer N, Canaris S, Bonilla O, Marxen S, Van Garderen A, Barras M. Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach. Interactive Journal of Medical Research 2022;11(2):e34533 View
8. Hong N, Liu C, Gao J, Han L, Chang F, Gong M, Su L. State of the Art of Machine Learning–Enabled Clinical Decision Support in Intensive Care Units: Literature Review. JMIR Medical Informatics 2022;10(3):e28781 View
9. Austin J, Barras M, Woods L, Sullivan C. The Effect of Digitization on the Safe Management of Anticoagulants. Applied Clinical Informatics 2022;13(04):845 View
10. Austin J, Barras M, Sullivan C. Safe and Effective Digital Anticoagulation: A Continuous Iterative Improvement Approach. ACI Open 2021;05(02):e116 View
11. Iancu A, Leb I, Prokosch H, Rödle W. Machine learning in medication prescription: A systematic review. International Journal of Medical Informatics 2023;180:105241 View
12. Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts’ consensus on the application of intensive care big data. Frontiers in Medicine 2024;10 View
13. Danilatou V, Dimopoulos D, Kostoulas T, Douketis J. Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review. Thrombosis and Haemostasis 2024;124(11):1040 View
14. Rao V, Valdez D, Muralidharan R, Agley J, Eddens K, Dendukuri A, Panth V, Parker M. Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis. Journal of Medical Internet Research 2024;26:e57885 View
15. Liu S, Su L, Jiang H, Wei Z, Yang B, Xie J, Guo A, He H, Zhu W, Long Y. Full connected layer model with self-attention to hourly predict heparin dosage for perioperative cardiac surgery patients. Computers in Biology and Medicine 2024;182:109085 View