A group of Australian researchers are predicting big improvements in how computers diagnose and treat disease.
A new article published in Nature Machine Intelligence reviews the potential of a new hybrid machine learning technique, ensemble deep learning, to improve the performance of computer models used in medical research.
Machine learning, when computers learn and find novel patterns from big data, already has many applications in medical research - diagnosis and prognosis of disease, drug discovery and development, and formulating precision medicine treatments.
However, the researchers have identified the shortcomings of the two current major machine learning techniques and have reviewed the ways that medical researchers can combine these two styles of machine learning into a more accurate and functional tool.
Ensemble deep learning, a hybrid of deep learning and ensemble learning, means precision medicine and drug development research can proceed more quickly and accurately, leading to treatments and cures faster than currently possible.
Dr Pengyi Yang is group leader of Computational Systems Biology at Children’s Medical Research Institute in Sydney.
“Just like ‘many heads are better than one’, ensemble deep learning that combines multiple ‘computer brains’ with complementary knowledge has achieved high levels of performance unattainable by traditional methods. This review is timely given the explosion of data seen in the biological and biomedical field.”
Dr Yang said the two machine learning components in this hybrid technique, deep learning and ensemble learning, have independently made a substantial impact on medical research.
Deep learning uses a model inspired by neural networks. This allows computers to spot patterns in data (such as images) and learn how to classify new information (such as a previously unseen image with a possible tumour) based on what the computer has learned previously.
“Perhaps in the future we’ll see ensemble deep learning implemented in hospital computers for diagnosing patients based on their genomic information and medical records (such as CT scans) with expert level of accuracy. These technology advances will reduce the current burden and waiting time in clinics and compliment the expertise of doctors,” added Dr Yang.