首页> 外文OA文献 >Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1

【2h】

Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1

机译：从星形* D和Can-Conn-1预测抗抑郁药物治疗结果的机器学习方法的复制预测抗抑郁药物治疗结果

代理获取

本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。

页面导航

著录项
引文网络
相似文献
相关主题

著录项

作者
John-Jose Nunez; Teyden T. Nguyen; Yihan Zhou; Bo Cao; Raymond T. Ng; Jun Chen; Benicio N. Frey; Roumen Milev; Daniel J. Müller; Susan Rotzinger; Claudio N. Soares; Rudolf Uher; Sidney H. Kennedy; Raymond W. Lam;
展开▼
作者单位

展开▼
年度 2021
总页数
原文格式 PDF
正文语种
中图分类

相似文献

外文文献
中文文献
专利

1. Hippocampal tail volume as a predictive biomarker of antidepressant treatment outcomes in patients with major depressive disorder: a CAN-BIND report [J] . Nikita Nogovitsyn, Meghan Muller, Roberto Souza, Neuropsychopharmacology . 2020,第2期

机译：海马尾部体积作为主要抑郁症患者的抗抑郁治疗结果的预测生物标志物：CAN-BIND报告
2. Daily activity level improvement with antidepressant medications predicts long-term clinical outcomes in outpatients with major depressive disorder [J] . Manish K Jha, Raymond B Teer, Abu Minhajuddin, Neuropsychiatric Disease and Treatment . 2017,第1期

机译：抗抑郁药物的每日活动水平提高可预测重度抑郁症门诊患者的长期临床结局
3. Antidepressant medication treatment failure does not predict lower remission with ECT for major depressive disorder: a report from the consortium for research in electroconvulsive therapy. [J] . Rasmussen KG, Mueller M, Knapp RG, The journal of clinical psychiatry . 2007,第11期

机译：抗抑郁药物治疗失败并不能预测ECT对严重抑郁症的缓解率更低：电惊厥疗法研究联盟的一份报告。
4. An EEG and Machine Learning based Method for the Detection of Major Depressive Disorder [C] . Elif İzcı, Mehmet Akif Özdemır, Aydın Akan, Signal Processing and Communications Applications Conference . 2021

机译：基于机器学习的重大抑郁障碍的方法
5. Interactive associations of race and comorbidity in medication treatment and outcomes of Medicaid enrolled patients with major depressive disorder. [D] . Wu, Chung-Hsuen. 2010

机译：药物治疗中种族和合并症之间的互动关联以及纳入Medicaid的重度抑郁症患者的结局。
6. Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1 [O] . John-Jose Nunez, Teyden T. Nguyen, Yihan Zhou, 2021

机译：从星形* D和Can-Conn-1预测抗抑郁药物治疗结果的机器学习方法的复制预测抗抑郁药物治疗结果
7. Daily activity level improvement with antidepressant medications predicts long-term clinical outcomes in outpatients with major depressive disorder [O] . Manish Jha, Raymond Teer, Abu Minhajuddin, 2017

机译：抗抑郁药物的日常活性水平改善预测主要抑郁症的门诊患者的长期临床结果

Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1

著录项

引文网络

相似文献

相关主题

期刊订阅