基本信息
浏览量:1
职业迁徙
个人简介
Dr. Shumake's primary research interest is building models from longitudinal data sets to classify and predict behavior and responses to interventions. He is excited by the advancement of personalized medicine through data science, which he believes is critical for translating prediction research into individualized treatment recommendations. He is particularly interested in using statistical and machine learning algorithms to search for novel combinations of genetic, neural, and behavioral features that predict treatment response. His current projects involve mining biometric and psychometric data from depressed individuals to predict longitudinal mood changes that occur both naturalistically and in response to antidepressant medication and cognitive therapy.
研究兴趣
论文共 79 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
Biological Psychiatry Global Open Scienceno. 3 (2024): 100310-100310
Rachel L. Weisenburger,Michael C. Mullarkey,Jocelyn Labrada, Daniel Labrousse, Michelle Y. Yang, Allison Huff Macpherson,Kean J. Hsu,Hassan Ugail,Jason Shumake,Christopher G. Beevers
Physiology & behavior (2023): 114183-114183
COGNITIVE THERAPY AND RESEARCHno. 5 (2023): 772-787
crossref(2022)
crossref(2022)
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn