Big data and machine learning algorithms for health-care delivery

Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinic...

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Vydáno v:The lancet oncology Ročník 20; číslo 5; s. e262 - e273
Hlavní autoři: Ngiam, Kee Yuan, Khor, Ing Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 01.05.2019
Elsevier Limited
Témata:
Algorithms
Artificial intelligence
Biomarkers
Biopsy
Clinical medicine
Clinical trials
Colonoscopy
Colorectal cancer
Datasets
Diagnosis
Electronic health records
Health care delivery
Histology
Learning algorithms
Lung cancer
Medical imaging
Medical screening
Melanoma
Neural networks
Oncology
Pancreatic cancer
Physicians
Population
Prostate cancer
Skin cancer
Vision systems
Canada
United States > US
China
Spain
ISSN:1470-2045, 1474-5488, 1474-5488
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Shrnutí:Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.
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ISSN:1470-2045
1474-5488
1474-5488
DOI:10.1016/S1470-2045(19)30149-4