HOLMeS: eHealth in the Big Data and Deep Learning Era

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.
Authors: Flora Amato, Stefano Marrone, Vincenzo Moscato, Gabriele Piantadosi, Antonio Picariello and Carlo Sansone
 
Journal Title: Information
 
ISSN: 2078-2489 (Online)
 
Publisher: MDPI AG
 
Abstract
 
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.
 

Picture: HOLMeS (health online medical suggestions) system main modules with interaction paradigm: At the bottom-centre, the HOLMeS application core; On the left, the HOLMeS chat-bot (bottom) and the patient (top) interacting with HOLMeS; At the top-centre the, IBM Watson conversation logic adopted by HOLMeS through its API; On the right, the computational cluster providing storage, computing, and machine learning services used by HOLMeS. (credits: Flora Amato, Stefano Marrone, Vincenzo Moscato, Gabriele Piantadosi, Antonio Picariello and Carlo Sansone)

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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