How artificial intelligence, robotics and Big Data can support pandemic situations

Daniel Calvo

Head of Artificial Intelligence and Robotics. Atos Research and Innovation

Orlando Ávila

Artificial Intelligence and Robotics Associated Head. Atos Research and Innovation

Carlos Cavero

Head of Health Unit. Atos Research and Innovation

Posted on: 17 April 2020

Governments, healthcare authorities and staff are working hard to fight and overcome the Covid-19 situation worldwide. Beyond that, innovative technologies can be a powerful additional resource to support the current efforts  – let us explore some of these opportunities:

Predictive models for the spread of infectious diseases

The development of models that provide short and medium term forecasting of the number of new cases is a powerful tool to estimate the need for clinical resources and personnel, especially when healthcare systems are overloaded.

In today’s situation, daily growth rate models are being published, relying on statistical models or time-series forecasting using Long Short-Term Memory (LSTM) neural networks. Further, at the beginning of the crisis, Big Data and AI techniques like Natural Language Processing have been used to identify and alert early about the new virus.

We have observed that most of the current models rely on the number of daily cases, but do not take into account the specific characteristics of each region or other relevant variables that may influence the evolution of the disease. For example, clustering regions can help create more accurate and specific predictive models. AI and Big Data can also be used to create accurate and detailed geospatial spread models, complementing the capabilities of traditional epidemiological compartment approaches.

Risk stratification and personalized treatments

The publicly available datasets for Covid-19 do not include all the needed clinical information regarding patients’ preexisting conditions, such as age, sex, region etc. While worldwide healthcare organizations state that the disease affects mainly elderly people or those with underlying health problems, there is a notable number of deaths outside of these higher-risk groups.

Electronic health records would help find hidden correlations and more finely-tuned risk stratification, resulting in a map of clinical and non-clinical factors that can enable implementation of appropriate mitigation actions while protecting the most vulnerable groups. However, most hospital information systems worldwide do not share clinical data among each other, therefore becoming silos of independent data. This barrier can be overcome with the use of eHealth standards and medical terminologies with a two-fold objective:

  1. Provide semantic interoperability (that is, common descriptive terminology)
  2. Build a global dataset with information from different countries, and with much more details on the effectiveness of experimental treatments. This can enable medical staff to prescribe personalized treatments according to the specific conditions of the patient, and also avoid conflicts and controversies with respect to the adverse side-effects of some drugs.

Robotic platforms to assist isolated patients

Hospitalized patients in isolation do not have the possibility to receive visits or be accompanied, due to the strict restrictions to stop the spreading of the virus. Moreover, in many cases, the overload of healthcare systems reduces the time that clinical staff can dedicate to each patient. Therefore, they spend most of their time alone which may further cause physical and psychological deterioration.

Many reports and scientific studies already warn of the negative impact effect that social distancing and isolation measures have on mental health. Cases of anxiety, depression and stress may also result in a post-traumatic condition after the end of quarantine periods.

The usage of autonomous robotic platforms can be helpful to extend the capabilities of hospitals in crises, in order to offer continuous interaction and personalized attention to isolated patients. Those robots could incorporate capabilities to recognize and adapt their behavior to human emotions and moods, and to naturally interact with patients. While performing non-critical routine tasks (e.g. food delivery), they could offer an alternative source of entertainment and distraction to the patient, monitor non-clinical parameters such as emotional state and level of activity, and report unsafe or at-risk situations or behaviors.

Robotic platforms for disinfection

As recently seen in China, autonomous robotic platforms for disinfection of healthcare facilities can play a fundamental role, by preventing and reducing the spread of infectious diseases, virus, bacteria and other types of harmful organic microorganisms in the environment. They are particularly effective when equipped with ultraViolet (UV) light emitters which break down the DNA-structure of viruses. In other cases, robots and drones have been used to spray sanitizers in public spaces to mitigate the possible expansion of the infection.

Although most of these robots have some autonomous capabilities they must generally be triggered or even controlled by human operators, which strongly reduces the benefit of automation. For instance, in the case of UV disinfecting robots, cleaning staff must use an application to order an actuation, and drones must be typically piloted by humans. To deliver the full potential of robotic platforms, semi-autonomous and distributed decision-making must be implemented in the robots, so that they can operate in a standalone way with minimum supervision, following the high-level goals defined by human authorities.

Global pandemic situations suppose a challenge that affects all aspects of modern societies. The technological progress of the last decades in areas like Big Data, artificial intelligence or robotics systems must be used to support the ongoing efforts to control and overcome the spread of the disease, to mitigate its economic consequences and to prevent the appearance of new outbreaks.

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About Daniel Calvo

Head of Artificial Intelligence and Robotics. Atos Research and Innovation
Daniel Calvo obtained his MSc in Electronics and Telecommunications Engineering by the University of Cantabria in 2009. He has worked for more than ten years in research and innovation projects in the area of heterogeneous hardware/software embedded systems, Internet of Things, connected vehicles and artificial intelligence. Currently, he is the Head of the Artificial Intelligence and Robotics unit in Atos Research and Innovation.

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About Orlando Ávila

Associate Head of Artificial Intelligence and Robotics Unit. Atos Research and Innovation
Orlando Avila García, PhD, has a degree in Computer/Informatics Engineering (Universidad de La Laguna, Spain) and a Ph.D. in Computer Science – Artificial Intelligence (University of Hertfordshire, UK). He joined Atos in 2017 as a senior software architect. Currently, he is the Associate Head of Artificial Intelligence and Robotics unit in ARI (Atos Research and Innovation), working on topics such as adaptive systems, autonomous systems, cognitive robotics, development and epigenetic robotics, robotic systems development and Artificial Intelligence (AI) & robot safety. He has more than 10 years’ experience as a project manager (CMMI-DEV) and enterprise architect (TOGAF 9.1 Certified) in industrial R&D and innovation projects. His focus has been on intelligent automation, automated decision-making and adaptive behaviour in domains such as core banking systems engineering, online advertising, online fraud prevention, intelligent agents and robotics. He has published in Artificial Intelligence (IBERAMIA, ECAL, IROS, SAB) and Model-Driven Engineering (JISBD, ICMT) conferences. He co-founded and organizes WAISE (International Workshop on Artificial Intelligence Safety Engineering) since 2018, and currently belongs to the programme committee of related AI safety workshops SafeAI and AISafety.

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About Carlos Cavero

Head of Health Unit. Atos Research and Innovation
Carlos Cavero is head of the Health Sector in Atos Research and Innovation. He has a Degree in Computer Science Engineering from the Autonomous University of Madrid (Spain). He also has a Degree in Philosophy from the National University of Distance Education (Spain). He joined Atos in October 1999 and in 2006 he moved to the Health Sector in ARI group working on several projects for the European Commission. He devoted the past four years implementing openEHR/EN13606 set of standards as the core reference model and terminologies such as SNOMED-CT. He also has experience with hl7 FHIR, CDA and CCD, ASTM CCR and HL7 vMR. Currently he is analysing the application of IHE profiles. His main interests are the application of continuous integration and automation deployment using open source cloud-based environments, big data technologies and NoSQL databases to the health environment complying with health standards.

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