Deep Learning for Health Informatics
Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Pérez, Benny Lo, Guang‐Zhong Yang
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
Citation edge evidence
- A survey on deep learning in medical image analysis→Deep Learning for Health InformaticsLegacy edge — source detail unavailable
- Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health R…→Deep Learning for Health InformaticsLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Long Short-Term MemoryLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→An overview of statistical learning theoryLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Intriguing properties of neural networksLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→A few useful things to know about machine learningLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Parallel Distributed ProcessingLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Practical Recommendations for Gradient-Based Training of Deep ArchitecturesLegacy edge — source detail unavailable
- Deep Learning for Health Informatics→Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the E…Legacy edge — source detail unavailable
- Deep Learning for Health Informatics→Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Excit…Legacy edge — source detail unavailable
- Practical Recommendations for Gradient-Based Training of Deep Architectures→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
- Practical Recommendations for Gradient-Based Training of Deep Architectures→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
- Long Short-Term Memory→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
- Dropout: a simple way to prevent neural networks from overfitting→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
- Dropout: a simple way to prevent neural networks from overfitting→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
- Reducing the Dimensionality of Data with Neural Networks→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
- Learning long-term dependencies with gradient descent is difficult→A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksLegacy edge — source detail unavailable
- A Fast Learning Algorithm for Deep Belief Nets→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
- Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Excit…→Long Short-Term MemoryLegacy edge — source detail unavailable
- Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Excit…→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
- Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the E…→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
- A survey on deep learning in medical image analysis→Practical Recommendations for Gradient-Based Training of Deep ArchitecturesLegacy edge — source detail unavailable
- A survey on deep learning in medical image analysis→Long Short-Term MemoryLegacy edge — source detail unavailable
- A survey on deep learning in medical image analysis→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
- A survey on deep learning in medical image analysis→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
- A survey on deep learning in medical image analysis→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
- A survey on deep learning in medical image analysis→Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Excit…Legacy edge — source detail unavailable
- Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health R…→A few useful things to know about machine learningLegacy edge — source detail unavailable
- Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health R…→Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the E…Legacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
Links
Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record
Topics
| Machine Learning in Healthcare | Computer Science |
| Artificial Intelligence in Healthcare | Health Professions |
| AI in cancer detection | Computer Science |
Is this record sound?
complete
Nothing in this record contradicts itself and no field we check is missing.
- supports7 author record(s) attached.
- supports159 reference(s) recorded.
- supportsThe DOI's year agrees with the publication year.
- supportsA title is present.
Provenance
sha256 6e314d9c6052d1ed…