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.
Highlights
Data collection and analysis are becoming more and more important in a large variety of application domains, as long as novel technologies advance
We introduce a general big data architecture which is useful to several eHealth applications; on the other hand, we present a particular instance/implementation of such an architecture—the
Starting from the very beginning, our idea was to develop a system able to be effectively used in a real clinical scenario, with the aim of assisting both physicians and patients
Summary
Data collection and analysis are becoming more and more important in a large variety of application domains, as long as novel technologies advance. We are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms [1]. This phenomenon can be observed in many fields, from e-commerce to medical diagnostics. A traditional data warehouse able to contain such an amount of information should be more than three petabytes. This implies that suitable data models and cluster-computing facilities are mandatory in order to use machine learning algorithms tailored to this kind and volume of data. As is well known, such approaches aim to extract patterns indicating the effectiveness of the business strategies, improving the inventory and supply chain management [2]
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