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Open AccessArticle

HOLMeS: eHealth in the Big Data and Deep Learning Era

Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, via Claudio 21, 80125 Naples, Italy
Author to whom correspondence should be addressed.
Information 2019, 10(2), 34;
Received: 12 November 2018 / Revised: 7 January 2019 / Accepted: 9 January 2019 / Published: 22 January 2019
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms. In particular, in the last few years, eHealth—which usually indicates all the healthcare practices supported by electronic elaboration and remote communications—calls for the availability of a smart environment and big computational resources able to offer more and more advanced analytics and new human–computer interaction paradigms. The aim of this paper is to introduce the HOLMeS (health online medical suggestions) system: A particular big data platform aiming at supporting several eHealth applications. As its main novelty/functionality, HOLMeS exploits a machine learning algorithm, deployed on a cluster-computing environment, in order to provide medical suggestions via both chat-bot and web-app modules, especially for prevention aims. The chat-bot, opportunely trained by leveraging a deep learning approach, helps to overcome the limitations of a cold interaction between users and software, exhibiting a more human-like behavior. The obtained results demonstrate the effectiveness of the machine learning algorithms, showing an area under ROC (receiver operating characteristic) curve (AUC) of 74.65% when some first-level features are used to assess the occurrence of different chronic diseases within specific prevention pathways. When disease-specific features are added, HOLMeS shows an AUC of 86.78%, achieving a greater effectiveness in supporting clinical decisions. View Full-Text
Keywords: eHealth; big data; deep learning; Watson; Spark; decision support system; prevention pathways eHealth; big data; deep learning; Watson; Spark; decision support system; prevention pathways
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Amato, F.; Marrone, S.; Moscato, V.; Piantadosi, G.; Picariello, A.; Sansone, C. HOLMeS: eHealth in the Big Data and Deep Learning Era. Information 2019, 10, 34.

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