HOLMeS: eHealth in the Big Data and Deep Learning Era

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.

Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey

“…In addition, all physicians practiced within the United States, which may be associated with a different level of enthusiasm toward digital medicine technology compared with other countries and cultures. Some research and viewpoints on health care chatbots have been published from international researchers around the world [13-27]; however, to the best of our knowledge, this was the first study to examine physicians’ perspectives on the direct use of chatbots in their practice. Future research should examine how different samples of HCPs, in different environments, perceive health care chatbots for use with their patients.…”

“…Chatbots in health care may have the potential to provide patients with access to immediate medical information, recommend diagnoses at the first sign of illness, or connect patients with suitable health care providers (HCPs) across their community [13,14]. Theoretically, in some instances, chatbots may be better suited to help patient needs than a human physician because they have no biological gender, age, or race and elicit no bias toward patient demographics.…”

Web-based chatbot for Frequently Asked Queries (FAQ) in Hospitals

“…Hospitals or healthcare organisations manage patient communications through rudimentary web operations. 2 Web-based chatbots overcome this by allowing conversations between hospital staff and patients to collect insights on facilities available in the hospital. 3 Chatbots have been receiving close attention in healthcare because these conversational agents provide easy access to information and function as an interactive platform offering personal engagement.…”

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

“…The challenges of big data healthcare analytics are classified into data and process-related, manpower-related, domain-related, and organizational challenges [66] . Significant multidimensional data handling, including image diagnostics, generate tremendous volumes of information; hence, scalability of storage is required [70] . The overall quality of health data is influenced by the size, speed, and formats of the generated and processed data.…”