Socialized healthcare service recommendation using deep learning

Yuan, Weiwei; Li, Chenliang; Guan, Donghai; Han, Guangjie; Khattak, Asad Masood

doi:10.1007/s00521-018-3394-4

Cited by 30 publications

(14 citation statements)

References 15 publications

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“…Multilayer perceptron (MLP), auto-encoder (AE), convolutional neural network (CNN), recurrent neural network (RNN), restricted Boltzmann machine (RBM), neural autoregressive distribution estimation and adversarial networks (AN) are the main components of the deep learning method [10,33,[47][48][49].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

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DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

et al. 2019

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In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.

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Section: Deep Learning Methodsmentioning

confidence: 99%

“…Subsequently, ensuring the privacy of a patient's information plays a vital role in clinical research. In the proposed approach, the integrity of this information will be maintained while personal identity is effectively shielded [17,[29][30][31][32][33].…”

Section: Privacy Preservationmentioning

confidence: 99%

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

et al. 2019

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“…They have incorporated ANFIS method that uses adaptive and nonadaptive nodes. Yuan et al [55] give a deep learningbased socialized RS to recommends healthcare services to users based on the trust and distrust relationships with the target user. The system also considers the structure information and the node information of users in the network.…”

Section: Medicinementioning

confidence: 99%

“…Manogaran et al [54] Deep neural network Yuan et al [55] Multilayer perceptron Katzman et al [56] Deep neural network Industrial Covington et al [57] Deep neural network Chen et al [58] Multilayer perceptron Images Lei et al [65] Convolutional neural network and Multilayer perceptron Zhou et al [60] Convolutional neural network…”

Section: Medicinementioning

confidence: 99%

An Application-oriented Review of Deep Learning in Recommender Systems

Shokeen¹,

Rana²

2019

IJISA

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The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data. Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. But recommender systems implementing these algorithms suffer from various challenges. Deep learning is proved successful in speech recognition, image processing and object detection. In recent years, deep learning has been also proved effective in handling information overload and recommending items. This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.

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“…The results of this study show good accuracy of respiration rate monitoring. 20 In the seventh study (E7), we presented a way to apply the DL technique to medical image analysis using bone age estimation as an example. The high accuracy of DL has been identified by allowing the estimation of a subject's age from hand-held x-ray images, which eliminates the need for tedious atlas searches in clinical settings and should improve time and cost of the estimation process.…”

Section: Multimodal Imaging 2019mentioning

confidence: 99%

Análise do deep learning em cuidados de saúde

Machado

Emmerick

Silva

et al. 2019

Rev enferm UFPE on line.

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Objective: to analyze the applicability and benefits of Deep Learning in health care. Method: this is a descriptive study, reflective analysis, with articles from 2014 to 2019, published in English and peer-reviewed, in the CAPES Journal Portal, with the search equation ("Deep Learning "AND (" Health Care "OR Health-care OR Healthcare)). The results were presented in figure form followed by descriptive analysis. Results: it is revealed that 15 articles describe the applicability of Deep Learning in the health care area. This article analyzed the use of Deep Learning in different areas related to health care, highlighting the benefits found by the authors of those selected through the literature review. Conclusion: it is suggested the use of Deep Learning in health care in view of the benefits identified in the articles selected as: the prediction of disease stages; precise identification of pathological mutations and support to doctors and nurses in their daily activities. Descriptors: Benefits; Deep Learning; Health Care; Diseases; Physicians; Nurses. RESUMOObjetivo: analisar a aplicabilidade e os benefícios do Deep Learning na área de cuidados de saúde. Método: trata-se de um estudo descritivo, tipo análise reflexiva, com consulta a artigos entre os anos de 2014 a 2019, publicados em inglês e revisado por pares, no Portal de Periódicos da CAPES, com a equação de busca ("Deep Learning" AND ("Health Care" OR Health-care OR Healthcare)). Apresentaram-se os resultados em forma de figura seguida da análise descritiva. Resultados: revela-se que 15 artigos descrevem a aplicabilidade do Deep Learning na área de cuidados de saúde. Analisou-se, por este artigo, o emprego do Deep Learning em diferentes áreas referentes aos cuidados de saúde, destacando os benefícios encontrados pelos autores dos selecionados por meio da revisão de literatura. Conclusão: sugere-se o emprego do Deep Learning na área de cuidados de saúde diante dos benefícios identificados nos artigos selecionados como: a previsão dos estágios das doenças; a identificação precisa de mutações patológicas e o suporte aos médicos e aos enfermeiros em suas atividades diárias. Descritores: Benefícios; Deep Learning; Cuidados de Saúde; Doenças; Médicos; Enfermeiros. RESUMENObjetivo: analizar la aplicabilidad y los beneficios del Deep Learning en la atención médica. Método: se trata de un estudio descriptivo, tipo análisis reflexivo, con artículos de 2014 a 2019, publicados en inglés y revisados por pares, en el Portal de la revista CAPES, con la ecuación de búsqueda ("Deep Learning" Y ("Health Care" O Health-care O Healthcare)). Los resultados se presentaron en forma de figura seguida de un análisis descriptivo. Resultados: se revela que 15 artículos describen la aplicabilidad del Deep Learning en el área de la atención médica. Este artículo analizó el uso del Deep Learning en diferentes áreas relacionadas con la atención de la salud, destacando los beneficios encontrados por los autores de los seleccionados a través de la revisión de la literatura. Conclusión: s...

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Socialized healthcare service recommendation using deep learning

Cited by 30 publications

References 15 publications

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

An Application-oriented Review of Deep Learning in Recommender Systems

Análise do deep learning em cuidados de saúde

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