Deep learning for healthcare: review, opportunities and challenges

Abstract

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 to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

“…To understand class-specific spectral characteristics in the EEG recordings, we analyzed band powers in five frequency ranges: delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14), low beta (14)(15)(16)(17)(18)(19)(20), high beta (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and low gamma .…”

“…Due to its size and rich annotation, this data set has a lot of potential to contribute to the progress of automated EEG diagnosis. Baseline results on this dataset have already been reported by TUH using a convolutional neural network (ConvNet) with multiple fully connected layers that uses precomputed EEG bandpower-based features as input and reached 78.8% accuracy [7].Deep learning approaches recently receive increasing attention in many types of machine learning problems in healthcare [8]. Deep ConvNets trained end-to end from the raw signals are a promising deep learning technique.…”

Deep Learning and Medical Diagnosis: A Review of Literature

“…Back in 2015, it was noted that deep learning has a clear path towards operating with large data sets, and thus, the applications of deep learning are likely to be broader in the future [3]. A large number of newer studies have highlighted the capabilities of advanced deep learning technologies, including learning from complex data [5,6], image recognition [7], text categorization [8] and others. One of the main applications of deep learning is for medical diagnosis [9,10].…”

“…More specific uses of deep learning in the medical field are segmentation, diagnosis, classification, prediction, and detection of various anatomical regions of interest (ROI). Compared to traditional machine learning, deep learning is far superior as it can learn from raw data, and has multiple hidden layers which allow it to learn abstractions based on inputs [5]. The key to deep learning capabilities lies in the capability of the neural networks to learn from data through general purpose learning procedure [5].…”

Deep learning aided decision support for pulmonary nodules diagnosing: a review

“…Deep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction (3,5,6,(11)(12)(13).…”