Literature DB >> 31425737

Statistical thinking, machine learning.

Jiang Bian1, Iain Buchan2, Yi Guo1, Mattia Prosperi3.   

Abstract

Mesh：

Year:  2019        PMID： 31425737     DOI： 10.1016/j.jclinepi.2019.08.003

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN： 0895-4356            Impact factor:   6.437


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  5 in total

1.  Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression.

Authors:  Qiang Liu; Georgia Salanti; Franco De Crescenzo; Edoardo Giuseppe Ostinelli; Zhenpeng Li; Anneka Tomlinson; Andrea Cipriani; Orestis Efthimiou
Journal:  BMC Psychiatry       Date:  2022-05-16       Impact factor: 4.144

2.  What is the foot strike pattern distribution in children and adolescents during running? A cross-sectional study.

Authors:  Bruno Augusto Giacomini; Tiê Parma Yamato; Alexandre Dias Lopes; Luiz Hespanhol
Journal:  Braz J Phys Ther       Date:  2020-10-11       Impact factor: 3.377

3.  Pre-existing and machine learning-based models for cardiovascular risk prediction.

Authors:  Sang-Yeong Cho; Sun-Hwa Kim; Si-Hyuck Kang; Kyong Joon Lee; Dongjun Choi; Seungjin Kang; Sang Jun Park; Tackeun Kim; Chang-Hwan Yoon; Tae-Jin Youn; In-Ho Chae
Journal:  Sci Rep       Date:  2021-04-26       Impact factor: 4.379

4.  A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.

Authors:  Gaosen Zhang; Yan Shi; Peipei Yin; Feifei Liu; Yi Fang; Xiang Li; Qingyu Zhang; Zhen Zhang
Journal:  Front Oncol       Date:  2022-07-25       Impact factor: 5.738

5.  Modelling hospital outcome: problems with endogeneity.

Authors:  John L Moran; John D Santamaria; Graeme J Duke
Journal:  BMC Med Res Methodol       Date:  2021-06-21       Impact factor: 4.615
  5 in total

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