Can I read A Machine Learning Tool for Identifying Non-metastatic Colorectal Cancer in Primary Care on EtoBox?

A Machine Learning Tool for Identifying Non-metastatic Colorectal Cancer in Primary Care by Elinor Nemlander; Marcela Ewing; Eliya Abedi; Jan Hasselström; Annika Sjövall; Axel C. Carlsson; Andreas Rosenblad is a Medicine article available to read on EtoBox.

What is A Machine Learning Tool for Identifying Non-metastatic Colorectal Cancer in Primary Care about?

Background Primary health care (PHC) is often the first point of contact when diagnosing colorectal cancer (CRC). Human limitations in processing large amounts of information warrant the use of machine learning as a diagnostic prediction tool for CRC. Aim To develop a predictive model for identifying non-metastatic CRC (NMCRC) among PHC patients using diagnostic data analysed with machine learning. Design and setting A case–control study containing data on PHC visits for 542 patients >18 years old diagnosed with NMCRC in the Västra Götaland Region, Sweden, during 2011, and 2,139 matched controls. Method Stochastic gradient boosting (SGB) was used to construct a model for predicting the presence of NMCRC based on diagnostic codes from PHC consultations during the year before the date of cancer diagnosis and the total number of consultations. Variables with a normalised relative influence (NRI) >1% were considered having an important contribution to the model. Risks of having NMCRC were calculated using odds ratios of marginal effects. Results Of the 361 variables used as predictors in the stochastic gradient boosting model, 184 had non-zero influence, with 16 variables having NRI >1

Who reads A Machine Learning Tool for Identifying Non-metastatic Colorectal Cancer in Primary Care?

Author

Elinor Nemlander; Marcela Ewing; Eliya Abedi; Jan Hasselström; Annika Sjövall; Axel C. Carlsson; Andreas Rosenblad

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Health Sciences)