Big Data and Machine Learning in Health Care.

By Andrew Beam, Isaac S. Kohane. 2018

1,854 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “However, attaining this sort of algorithmic fairness would imply inequality of treatment across genders (Berk et al., 2018). Fairness could be further hampered by the combined use of this algorithm with others driving decisions on neighbourhood police patrolling. The fact these algorithms may be pro”
Artiﬁcial Intelligence Applications in Dermatology: Where Do We Stand? (neutral): “Beam et al. discussed the relationship of AI compared to more classic statistical models (85).”
Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment (2020, background): “There is no clear line between ML models and traditional statistical models, and a recent article summarizes the relationship between the two [41].”
Machine learning in medicine: a practical introduction (2019, background): “Inter-ested readers are directed to materials which develop the ideas discussed here [11].”
Artificial intelligence in retina. (2018, background): “In addition, questions such as data ownership, rights to intellectual property and big profits created from public funding become more and more virulent (Beam and Kohane, 2018).”
Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, background): “Machine learning pervades an increasing number of fields, from banking and finance [1, 2, 3] to healthcare [4, 5, 6], robotics [7, 8, 9], transportation [10, 11, 12], e-commerce [13, 14, 15], and social networks to mention a few.”
Opportunities in Machine Learning for Healthcare (2018, neutral): “ensional data. Individual patient data has ballooned to include many sources and modalities, making integration more challenging in systems currently struggling with overload (Obermeyerand Lee, 2017; Beam and Kohane, 2018). Using high-dimensional data to make well-calibrated predictions of establish”
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “Some researchers describe the distinction between statistical modeling and ML as a continuum [5].”
A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, background): “success in clinical practice [175], [177], however, their success has been restricted to applications that can provide the”
A Review of Challenges and Opportunities in Machine Learning for Health. (2018, background): “Individual patient data has ballooned to include many sources and modalities, making integration more challenging in systems currently struggling with overload 88,102 .”
Deep Learning for Medical Anomaly Detection – A Survey (2020, background): “data [9] and increased computational resources has empowered deep learning’s hierarchical feature learning process, avoiding the need to explicitly hand-craft and define what constitutes an anomaly.”