Literature DB >> 31000806

How to develop machine learning models for healthcare.

Po-Hsuan Cameron Chen1, Yun Liu2, Lily Peng2.   

Abstract

Mesh:

Year:  2019        PMID: 31000806     DOI: 10.1038/s41563-019-0345-0

Source DB:  PubMed          Journal:  Nat Mater        ISSN: 1476-1122            Impact factor:   43.841


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  33 in total

1.  Mapping MacNew Heart Disease Quality of Life Questionnaire onto country-specific EQ-5D-5L utility scores: a comparison of traditional regression models with a machine learning technique.

Authors:  Lan Gao; Wei Luo; Utsana Tonmukayakul; Marj Moodie; Gang Chen
Journal:  Eur J Health Econ       Date:  2021-01-13

2.  Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges.

Authors:  Danton S Char; Alyssa Burgart
Journal:  Anesth Analg       Date:  2020-06       Impact factor: 5.108

Review 3.  Machine Learning in Pituitary Surgery.

Authors:  Vittorio Stumpo; Victor E Staartjes; Luca Regli; Carlo Serra
Journal:  Acta Neurochir Suppl       Date:  2022

4.  Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models.

Authors:  Jingmei Yang; Xinglong Ju; Feng Liu; Onur Asan; Timothy Church; Jeff Smith
Journal:  IEEE Open J Eng Med Biol       Date:  2021-10-06

Review 5.  How Machine Learning Will Transform Biomedicine.

Authors:  Jeremy Goecks; Vahid Jalili; Laura M Heiser; Joe W Gray
Journal:  Cell       Date:  2020-04-02       Impact factor: 41.582

6.  Using machine learning to construct nomograms for patients with metastatic colon cancer.

Authors:  B Zhao; R A Gabriel; F Vaida; S Eisenstein; G T Schnickel; J K Sicklick; B M Clary
Journal:  Colorectal Dis       Date:  2020-02-16       Impact factor: 3.788

7.  Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.

Authors:  Christopher Nielson; Martin G Seneviratne; Joseph R Ledsam; Shakir Mohamed; Nenad Tomašev; Natalie Harris; Sebastien Baur; Anne Mottram; Xavier Glorot; Jack W Rae; Michal Zielinski; Harry Askham; Andre Saraiva; Valerio Magliulo; Clemens Meyer; Suman Ravuri; Ivan Protsyuk; Alistair Connell; Cían O Hughes; Alan Karthikesalingam; Julien Cornebise; Hugh Montgomery; Geraint Rees; Chris Laing; Clifton R Baker; Thomas F Osborne; Ruth Reeves; Demis Hassabis; Dominic King; Mustafa Suleyman; Trevor Back
Journal:  Nat Protoc       Date:  2021-05-05       Impact factor: 13.491

8.  Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology.

Authors:  Ryan W Stidham
Journal:  Gastroenterol Hepatol (N Y)       Date:  2020-07

Review 9.  Requirements and reliability of AI in the medical context.

Authors:  Yoganand Balagurunathan; Ross Mitchell; Issam El Naqa
Journal:  Phys Med       Date:  2021-03-13       Impact factor: 2.685

10.  Neonatal mortality prediction with routinely collected data: a machine learning approach.

Authors:  André F M Batista; Carmen S G Diniz; Eliana A Bonilha; Ichiro Kawachi; Alexandre D P Chiavegatto Filho
Journal:  BMC Pediatr       Date:  2021-07-21       Impact factor: 2.125

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