Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives

Abstract

With the advent of electronic medical records and the expansion of health care data, researchers, clinicians and patients, are now facing an unprecedented wealth of digital information. In fact, the complexity and volume of the data from basic science to clinical information are beyond the capacity of a human's brain to analyse and comprehend. 1 Furthermore, clinical management from diagnosis through staging and the consideration of treatment options is also becoming more challenging for patients and providers; hence we often reference guidelines informed by disease experts to serve patients. 2 Machine learning (ML) technology can offer tools to support the processing of large volumes of data and can potentially help us in various areas including supporting diagnosis, creating prognostic models and informing treatment decisions.The term 'machine learning' was first used in 1959 by Arthur Samuel when he programmed a digital computer to learn to play a better game of checkers. 3 ML is a subdomain of artificial intelligence and includes deep learning. This technology has been applied extensively in almost every facet of life including marketing, online advertising, global positioning satellite software, fraud detection, engineering, and is now becoming a major research field and method in medicine. Although the vision of computation augmenting or replacing the intellectual function of the physicians was discussed in the 1970s, 4 there is currently little in health care that has been driven by ML. However, research papers using ML algorithms have been exploding, particularly in the last 3 years. With the expansion of databases using electronic medical records, we are in the early days of applying ML methods to a greater extent in medicine. Overviews of ML in medicine or in oncology have been described elsewhere. 2,5,6 In this review, we will focus on summarizing how ML algorithms have been applied in various fields of lymphoma clinical research and discuss limitations and future directions.