eDoctor: machine learning and the future of medicine

Abstract

Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.

Orthodontics in the era of big data analytics

“…The machine‐learning approach, in contrast to traditional statistical approaches, is not based on a study hypothesis nor is it dependent upon strict data parameters and data assumptions . Instead, the machine‐learning approach examines patterns in data and can simultaneously account for multiple variables and interactions between variables and assumes that endogeneity is ubiquitous . Consequently, machine learning is the analytical approach of choice when we are inundated with big data drawn from multiple sources and in a wide range of formats.…”

“…Consequently, machine learning is the analytical approach of choice when we are inundated with big data drawn from multiple sources and in a wide range of formats. Machine learning has become increasingly utilized in health care in recent years and has been successful in identifying disease subtypes as well as providing insights into rare diseases and associated outcomes, predictive modelling, identification of never events and targeted therapies . Broadly, machine learning can be either supervised or unsupervised.…”

“…In an unsupervised machine‐learning approach, the algorithms are programmed to identify patterns within a data set. Depending on hierarchical representation of data, clustering and dimensionality of data, frequently used approaches include neural networks, deep learning, tensor factorization and topological data analysis …”

“…In recent years, one of the big data analytic tools that has gained popularity in the applied health sciences is machine-learning algorithms. 16 When the PubMed search engine was queried using the term "machine learning," 24 132 articles were published as of 10/09/2018 ( Figure 2). The number of articles on machine learning increased substantially since 2010.…”

“…10,11,14,[16][17][18] Instead, the machine-learning approach examines patterns in data and can simultaneously account for multiple variables and interactions between variables and assumes that endogeneity is ubiquitous. 10,11,14,[16][17][18] Consequently, machine learning is the analytical approach of choice when we are inundated with big data drawn from multiple sources and in a wide range of formats. Machine learning has become increasingly utilized in health care in recent years and has been successful in identifying disease subtypes as well as providing insights into rare diseases and associated outcomes, predictive modelling, identification of never events and targeted therapies.…”