Preprint Article Version 1 This version is not peer-reviewed

HOLMeS: eHealth in the Big Data and Deep Learning Era

Version 1 : Received: 12 November 2018 / Approved: 15 November 2018 / Online: 15 November 2018 (04:14:36 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Information 2019, 10, 34
DOI: 10.3390/info10020034

Abstract

Data collection and analysis are becoming more and more important in a variety of application domains as long as the novel technologies advance. At the same time, we are experiencing a growing need for human-machine interaction with expert systems pushing research through new knowledge representation models and interaction paradigms. In particular, in the last years eHealth - that indicates all the health-care practices supported by electronic elaboration and remote communications - calls for the availability of smart environment and big computational resources. The aim of this paper is to introduce the HOLMeS (Health On-Line Medical Suggestions) framework. The introduced system proposes to change the eHealth paradigm where a trained machine learning algorithm, deployed on a cluster-computing environment, provides medical suggestion via both chat-bot and web-app modules. The chat-bot, based on deep learning approaches, is able to overcome the limitation of biased interaction between users and software, exhibiting a human-like behavior. Results demonstrate the effectiveness of the machine learning algorithms showing 74.65% of Area Under ROC Curve (AUC) when first-level features are used to assess the occurrence of different prevention pathways. When disease-specific features are added, HOLMeS shows 86.78% of AUC achieving a more specific prevention pathway evaluation.

Subject Areas

eHealth; big data; deep learning; watson; spark; decision support system; prevention pathways

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