A Deep-Big Data Approach to Health Care in the AI Age

Neves, José; Vicente, Henrique; Esteves, Marisa; Ferraz, Filipa; Abelha, António; Machado, José; Machado, Joana; Neves, José; Ribeiro, Jorge; Sampaio, Lúzia

doi:10.1007/s11036-018-1071-6

Cited by 26 publications

(12 citation statements)

References 13 publications

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“…Therefore, this article intends to elaborate on a theory and its algorithm on stock market forecasting, determining the future value of a given company's shares. Nevertheless, several studies aim to accept the challenge, and while some statistical and Machine Learning algorithms achieve significant results, the search for closer to ideal results is underway [1,6,7].…”

Section: State Of the Artmentioning

confidence: 99%

Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

et al. 2021

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Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets.

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Section: State Of the Artmentioning

confidence: 99%

Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

et al. 2021

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“…ANNs are increasingly applied in data mining because of their good performance in prediction [ 13 ]. In last decades, several studies have been published showing the usefulness of ANNs to apprehend complex relationships between variables, in various areas of application (e.g., environment [ 14 , 15 ], health [ 16 , 17 ], and law [ [18] , [19] , [20] ] just to name a few.…”

Section: State Of Artmentioning

confidence: 99%

Psychosocial Risks Assessment in Cryopreservation Laboratories

Fernandes

Figueiredo

Ribeiro

et al. 2020

Safety and Health at Work

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Background Psychosocial risks are increasingly a type of risk analyzed in organizations beyond chemical, physical, and biological risks. To this type of risk, a greater attention has been given following the update of ISO 9001: 2015, more precisely the requirement 7.1.4 for the process operation environment. The update of this normative reference was intended to approximate OHSAS 18001: 2007 reference updated in 2018 with the publication of ISO 45001. Thus, the organizations are increasingly committed to achieving and demonstrating good occupational health and safety performance. Methods The aim of this study was to characterize the psychosocial risks in a cryopreservation laboratory and to develop a predictive model for psychosocial risk management. The methodology followed to collect the information was the inquiry by questionnaire that was applied to a sample comprising 200 employees. Results The results show that most of the respondents are aware of the psychosocial risks, identifying interpersonal relationships and emotional feelings as the main factors that lead to this type of risks. Furthermore, terms such as lack of resources, working hours, lab equipment, stress, and precariousness show strong correlation with psychosocial risks. The model presented in this study, based on artificial neural networks, exhibited good performance in the prediction of the psychosocial risks. Conclusion This work presents the development of an intelligent system that allows identifying the weaknesses of the organization and contributing to the enhancement of the psychosocial risks management.

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“…Recommender Systems are defined as "(...) software tools and techniques that provide suggestions for items that are most likely of interest to a particular user." [13,1].…”

Section: B Food Recommender Systemsmentioning

confidence: 99%

“…Its main ability is to provide personalised and relevant recommendations to the users. Recommender systems focus on a specific domain of items (for example, movies, books or music), the user's graphical interface, and the recommendation techniques used to generate useful suggestions for that type of item [13,1].…”

Section: B Food Recommender Systemsmentioning

confidence: 99%

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Intelligent Nutrition in Healthcare and Continuous Care

Miranda

Ferreira

Abelha

et al. 2019

2019 International Conference in Engineering Applications (ICEA)

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In the healthcare industry, the patient's nutrition is a key factor in their treatment process. Every user has their own specific nutritional needs and requirements. An appropriate nutrition policy can therefore help the patient's recovery process and alleviate possible symptoms. Food recommender systems are platforms that offer personalised suggestions of recipes to users. However, there is a lack of usage of recipe recommendation systems in the healthcare sector. Multiple challenges in representing the domain of food and the patient's needs make it complicated to implement these systems. The present project aims to develop a platform for an intelligent planning of the user's meals, based on their clinical conditions. The application of machine learning algorithms on nutrition, in healthcare services and continuous care is thus a key topic of research. This platform will be tested and deployed at the Social Canteen of Vila Verde (Cantina Social da Santa Casa da Misericórdia de Vila Verde). The development of this project will use the Design Science Research (DSR) investigation methodology, ensuring that the solution to the problem accomplishes all needs and requirements of the professionals, while elucidating new knowledge both for the institution and the scientific community.

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A Deep-Big Data Approach to Health Care in the AI Age

Cited by 26 publications

References 13 publications

Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

Psychosocial Risks Assessment in Cryopreservation Laboratories

Intelligent Nutrition in Healthcare and Continuous Care

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