Choosing better lung cancer treatments with machine learning

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Аvera Allen, Deputy Editor In Chief

Date: 26.02.2021

Researchers say that machine learning could help guide healthcare workers’ treatment decisions for lung cancer patients after developing a model that is 71% more accurate at predicting survival expectancy of patients.

A team of Penn State Great Valley researchers conducted a study in which they developed a deep learning model that is more than 71% accurate in predicting survival expectancy of lung cancer patients, which is significantly better than traditional machine learning models that the team tested which have around a 61% accuracy rate.

Deep learning is a type of machine learning that is based on artificial neural networks, which are generally modelled on how the human brain’s own neural network functions.

The team say that the information on a patient’s survival expectancy could help guide doctors and caregivers in making better decisions on using medicines, allocating resources, and determining the intensity of care for patients. The machine learning model is able to analyse vast amounts of data and can include information such as types of cancer, tumour size, the speed of tumour growth, and demographic data.

The article based on the information: HealthEuropa

 

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