Deep learning for healthcare: review, opportunities and challenges

By Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, Joel T. Dudley. 2018

2,642 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management, Molecular Biology, Medical & Health AI

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Citations in the corpus

Split learning for health: Distributed deep learning without sharing raw patient data (2018, background): “Deep learning methods in general have found a pervasive suite of applications in biology, clinical medicine, genomics and public health as surveyed in [24, 23, 25, 29, 30, 31].”
Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (2018, background): “In order to trust the model, it is essential for doctors and domain experts to understand the rationale behind the model predictions [20, 32].”
What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (2019, background): “in many domains, including high-stakes domains such as healthcare, employment, and criminal justice (Chalfin et al., 2016; Miotto et al., 2017; Chouldechova, 2017).”
Deep Reinforcement Learning (2018, background): “tion in Section20.4, computer systems in Section20.5, and, science, engineering and art in Section20.6. 20.1 HEALTHCARE There are many opportunities and challenges in healthcare for machine learning (Miotto et al., 2017;Saria,2014). Personalized medicine is getting popular in healthcare. It systemat”
Capturing Feature-Level Irregularity in Disease Progression Modeling (2017, methodology): “Deep learning methods have also been employed in a wide range of EMR data analytic tasks [21], including ICU mortality prediction [4, 20], diagnosis [19], and interpretable phenotype learning in clinical decision making [5].”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “It is diﬃcult to establish what constitutes relevant information, because various medical ontologies can be used for data interpretation [7].”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, background): “In particular, use of machine learning and especially deep learning to process EHRs is creating a unique opportunity to derive previously unknown clinical insights [7].”
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians (2019, background): “Existing literature in the machine learning field provides relatively successful methods to train such mathematical models that learn useful knowledge from data [9-11].”
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation (2018, background): “However, these simple parametric models do not handle complex datasets well and they do not represent the mainstream machine learning technologies used in biomedical research [12,13].”
Neural Networks and Deep Learning (2018, neutral): “Numerous surveys and overviews of neural networks in different contexts may be found in [27, 28, 198, 277, 345, 431].”
Introducing Artificial Intelligence Training in Medical Education (2019, neutral): “Examples of advances in AI include natural language processing (NLP) [16], speech recognition [17], virtual agents [18], decision management [19], machine learning [20], deep learning [21], and robotic process automation [22].”
Machine learning applications in drug development (2019, background): “Automatic feature selection algorithms can take advantage of the great ﬂexibility and learning power of Deep Neural Networks, that, provided the raw input data and the expected outcome, create the most discriminant features [116,117].”