Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study

[11] Velasco A(2021)SMR: medical knowledge graph embedding for safe medicine recommendation Big Data Res 16 681-694

[12] Hage FG(2018)The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care Nat Med 14 391-400

[13] Zhao Y(2016)Workflow-driven clinical decision support for personalized oncology BMC Med Inform Decis Mak 100 471-482

[14] Kosorok MR(2019)A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis PLoS ONE 73 1-13

[15] Zeng D(2013)Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions Biometrika 81 480-494

[16] Zhao Y(2017)Greedy outcome weighted tree learning of optimal personalized treatment rules Biometrics 113 640-661

[17] Zeng D(2021)Personalized multimorbidity management for patients with type 2 diabetes using reinforcement learning of electronic health records Drugs 139 1106-1118

[18] Socinski MA(2018)Learning optimal personalized treatment rules in consideration of benefit and risk: with an application to treating type 2 diabetes patients with insulin therapies J Am Stat Assoc 45 895-904

[19] Fang G(2020)Predicting optimal hypertension treatment pathways using recurrent neural networks Int J Med Inform 29 1541-1549

[20] Annis IE(2016)A pilot SMART for developing an adaptive treatment strategy for adolescent depression J Clin Child Adolesc Psychol 107 1887-1902

[11] Velasco A(2021)SMR: medical knowledge graph embedding for safe medicine recommendation Big Data Res 16 681-694

[12] Hage FG(2018)The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care Nat Med 14 391-400

[13] Zhao Y(2016)Workflow-driven clinical decision support for personalized oncology BMC Med Inform Decis Mak 100 471-482

[14] Kosorok MR(2019)A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis PLoS ONE 73 1-13

[15] Zeng D(2013)Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions Biometrika 81 480-494

[16] Zhao Y(2017)Greedy outcome weighted tree learning of optimal personalized treatment rules Biometrics 113 640-661

[17] Zeng D(2021)Personalized multimorbidity management for patients with type 2 diabetes using reinforcement learning of electronic health records Drugs 139 1106-1118

[18] Socinski MA(2018)Learning optimal personalized treatment rules in consideration of benefit and risk: with an application to treating type 2 diabetes patients with insulin therapies J Am Stat Assoc 45 895-904

[19] Fang G(2020)Predicting optimal hypertension treatment pathways using recurrent neural networks Int J Med Inform 29 1541-1549

[20] Annis IE(2016)A pilot SMART for developing an adaptive treatment strategy for adolescent depression J Clin Child Adolesc Psychol 107 1887-1902