Deep learning for healthcare: review, opportunities and challenges

@article{Miotto2018DeepLF,
  title={Deep learning for healthcare: review, opportunities and challenges},
  author={Riccardo Miotto and Fei Wang and Shuang Wang and Xiaoqian Jiang and Joel T. Dudley},
  journal={Briefings in bioinformatics},
  year={2018},
  volume={19 6},
  pages={
          1236-1246
        }
}
Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering… 

Figures and Tables from this paper

Efficient Deep Learning Approaches for Health Informatics

  • T. M. Navamani
  • Computer Science
    Deep Learning and Parallel Computing Environment for Bioengineering Systems
  • 2019

Intelligent Health Care: Applications of Deep Learning in Computational Medicine

The applications of deep learning in medical image, electronic health record, genomics, and drug development are studied, where the suggestion is that deep learning has obvious advantage in making full use of biomedical data and improving medical health level.

DeepHealth: Deep Learning for Health Informatics

This article presents a comprehensive review of research applying deep learning in health informatics with a focus on the last five years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research.

DeepHealth : Deep Learning for Health Informatics reviews , challenges , and opportunities on medical imaging , electronic health records , genomics , sensing , and online communication health

A comprehensive review of research applying deep learning in health informatics with a focus on the last five years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research.

Deep Learning for Electronic Health Records Analytics

The purpose of this study was to offer an intuitive explanation for possible use cases of deep learning with EHR and reflect on techniques that can be applied by health informatics professionals by giving technical intuitions and blue prints on how each clinical task can be approached by a deep learning algorithm.

The Role of Deep Learning in Improving Healthcare

This chapter focuses on Deep Learning, a subfield of ML that relies on deep artificial neural networks to deliver breakthroughs in long-standing AI problems and offers insights into existing applications of DL to healthcare on their suitability for specific types of data and their limitations.

Case Study: Deep Convolutional Networks in Healthcare

The most popular and up-to-date deep learning solutions to biomedical problems are discussed and organ and disease combination are taken into consideration in this chapter.

Opportunities and obstacles for deep learning in biology and medicine

This work examines applications of deep learning to a variety of biomedical problems -- patient classification, fundamental biological processes, and treatment of patients -- to predict whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges.

A Predictive Approach Using Deep Feature Learning for Electronic Medical Records: A Comparative Study

A new predictive approach based on feature representation using deep feature learning and word embedding techniques to obtain effective and more robust features from EMRs, and then build prediction models on the top of them.

Deep Learning Methods and Their Application to Nursing Workflows: Technology and Perspectives.

Deep learning methods have dramatically outperformed many traditional ML approaches in settings both old and new and shows potential to enable more effective and cost-efficient diagnostics, provide rapid care to patients most in need, improve personalization of care, and ease provider fatigue.
...

References

SHOWING 1-10 OF 115 REFERENCES

Risk Prediction with Electronic Health Records: A Deep Learning Approach

A deep learning approach for phenotyping from patient EHRs by building a fourlayer convolutional neural network model for extracting phenotypes and perform prediction and the proposed model is validated on a real world EHR data warehouse under the specific scenario of predictive modeling of chronic diseases.

Applications of Deep Learning in Biomedicine.

Key features of deep learning that may give this approach an edge over other machine learning methods are discussed and a number of applications ofdeep learning in biomedical studies demonstrating proof of concept and practical utility are reviewed.

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

The findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

Deep Computational Phenotyping

This work proposes two novel modifications to standard neural net training that address challenges and exploit properties that are peculiar, if not exclusive, to medical data, and examines a general framework for using prior knowledge to regularize parameters in the topmost layers.

Deep learning for healthcare decision making with EMRs

The experimental results indicate that the proposed deep model is able to reveal previously unknown concepts and performs much better than the conventional shallow models.

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

Abstract Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals, this work produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task.

Big Data Application in Biomedical Research and Health Care: A Literature Review

The recent progress and breakthroughs of big data applications in these health-care domains are reviewed and the challenges, gaps, and opportunities to improve and advance bigData applications in health care are summarized.

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

The efficacy of DeepCare for disease progression modeling and readmission prediction in diabetes, a chronic disease with large economic burden, is demonstrated and the results show improved modeling and risk prediction accuracy.

Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

An introduction to machine learning tasks that address important problems in genomic medicine by focusing on how machine learning can help to model the relationship between DNA and the quantities of key molecules in the cell, with the premise that these quantities may be associated with disease risks.
...