Deep Learning for Health Informatics

Ravì, Daniele; Wong, Charence; Deligianni, Fani; Berthelot, Melissa; Andreu-Pérez, Javier; Lo, Benny; Yang, Guang

doi:10.1109/jbhi.2016.2636665

Cited by 1,518 publications

(875 citation statements)

References 114 publications

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“…Life Sciences have long been one of the key drivers behind progress in AI, and the vastly increasing volume and complexity of data in biology is one of the drivers in Data Science as well. Life Sciences are also a prime application area for novel machine learning methods [2,51]. Similarly, Semantic Web technologies such as knowledge graphs and ontologies are widely applied to represent, interpret and integrate data [12,32,61].…”

Section: Data and Knowledge In Research -The Case Of The Life Sciencesmentioning

confidence: 99%

Data Science and symbolic AI: Synergies, challenges and opportunities

Hoehndorf

Queralt-Rosiñach

2017

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Editor: Tobias Kuhn (https://orcid.org/0000-0002-1267-0234) Solicited reviews: anonymous reviewer; Rinke Hoekstra (https://orcid.org/0000-0001-7076-9083); Agnieszka Ławrynowicz (https://orcid.org/0000-0002-2442-345X); Honghan Wu (https://orcid.org/0000-0002-0213-5668) Received 21 February 2017 Accepted 27 April 2017Abstract. Symbolic approaches to Artificial Intelligence (AI) represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions, and manipulate symbols and symbol expressions through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to AI, there is an increasing potential for successfully applying symbolic approaches as well. Symbolic representations and symbolic inference are close to human cognitive representations and therefore comprehensible and interpretable; they are widely used to represent data and metadata, and their specific semantic content must be taken into account for analysis of such information; and human communication largely relies on symbols, making symbolic representations a crucial part in the analysis of natural language. Here we discuss the role symbolic representations and inference can play in Data Science, highlight the research challenges from the perspective of the data scientist, and argue that symbolic methods should become a crucial component of the data scientists' toolbox.

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Section: Data and Knowledge In Research -The Case Of The Life Sciencesmentioning

confidence: 99%

Data Science and symbolic AI: Synergies, challenges and opportunities

Hoehndorf

Queralt-Rosiñach

2017

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“…From the whole dataset, we have extracted 80% for the definition of the training set and the 20% for the testing set. The TD has been used to train both the deep neural network (DNN) [8] and conventional neural network (CNN) [8].…”

Section: Approachmentioning

confidence: 99%

“…The movements are described by means of the 3-axes acceleration values and the stress level is described by the electrodermal activity (EDA). We have used these signals as input for two kinds of deep learning (DL) algorithms: deep neural network (DNN) [8] and conventional neural network (CNN) [8].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Approach for the Recognition of Sleep Disorders in Patients with Cognitive Diseases: A Case Study

Paragliola

Coronato

2017

Annals of Computer Science and Information Systems

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Abstract-Alzheimer's disease is the most common type of dementia. Patients suffer from of this kind of disease could show symptoms such as sleep disturbances, muscle rigidity or other typical Alzheimer's movement irregularities. In our work, we have focused on those types of disturbances related to sleep disorders. Due to their not well-known nature, it is difficult to develop software able to identify sleep disorders. In this work, we have addressed the problem of the automatic recognition of sleep disorders in patients with Alzheimer's disease by using deep learning algorithms.

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“…While support vector machines are still popular techniques within the machine learning community [4] [14], the family of deep learning techniques are gaining considerable attention [15]. Deep learning methods are types of representation learning methods, which can automatically identify the optimal representation of raw data without requiring prior feature selection.…”

Section: Introductionmentioning

confidence: 99%

Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning

Stamate

Alghamdi

Ståhl

et al. 2018

Communications in Computer and Information Science

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Abstract.In recent years, a number of researches started to investigate the existence of links between cannabis use and psychotic disorder. More recently, artificial neural networks and in particular deep learning have set a revolutionary wave in pattern recognition and machine learning. This study proposes a novel machine learning approach based on neural network and deep learning algorithms, to developing highly accurate predictive models for the onset of first-episode psychosis. Our approach is based also on a novel methodology of optimising and post-processing the predictive models in a computationally intensive framework. A study of the trade-off between the volume of the data and the extent of uncertainty due to missing values, both of which influencing the predictive performance, enhanced this approach. Furthermore, we extended our approach by proposing and encapsulating a novel post-processing k-fold cross-testing method in order to further optimise, and test these models. The results show that the average accuracy in predicting first-episode psychosis achieved by our models in intensive Monte Carlo simulation, is about 89%.

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Deep Learning for Health Informatics

Cited by 1,518 publications

References 114 publications

Data Science and symbolic AI: Synergies, challenges and opportunities

Data Science and symbolic AI: Synergies, challenges and opportunities

A Deep Learning-Based Approach for the Recognition of Sleep Disorders in Patients with Cognitive Diseases: A Case Study

Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning

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