A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning

Objective

Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies.

Methods

Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions.

Results

The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hospitals of the Lazio region which admit about 90% of the region's patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%.

Conclusions

Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.

Design, realization, and user evaluation of the ARCA system for exploring a digital library

This paper presents ARCA, a software system that enables semantic search and exploration over a book catalog. The main purpose of this work is twofold: to propose a general paradigm for a semantic enrichment workflow and to evaluate a visual approach to information retrieval based on extracted information and existing knowledge graphs. ARCA has been designed and implemented following a user-centered design approach. Two different releases of the system have incrementally and iteratively developed and evaluated. The first release has evaluated the quality and usefulness of the extracted data. The second release, whose design was a refinement based on the previous evaluation results, was assessed by several users. Moreover, a comparative test with other information retrieval systems was conducted in order to study the potential added-value of the system. ARCA is employed in a real editorial scenario to visually search and explore the books of a publishing house.

A Geospatial Artificial Intelligence and satellite-based earth observation cognitive system in response to COVID-19

The pandemic emergency caused by the spread of COVID-19 has stressed the importance of promptly identifying new epidemic clusters and patterns, to ensure the implementation of local risk containment measures and provide the needed healthcare to the population. In this framework, artificial intelligence, GIS, geospatial analysis and space assets can play a crucial role. Social media analytics can be used to trigger Earth Observation (EO) satellite acquisitions over potential new areas of human aggregation. Similarly, EO satellites can be used jointly with social media analytics to systematically monitor well-known areas of aggregation (green urban areas, public markets, etc.). The information that can be obtained from the Earth Cognitive System 4 COVID-19 (ECO4CO) are both predictive, aiming to identify possible new clusters of outbreaks, and at the same time supervisorial, by monitoring infrastructures (i.e. traffic jams, parking lots) or specific categories (i.e. teenagers, doctors, teachers, etc.). In this perspective, the technologies described in this paper will allow us to detect critical areas where individuals can be involved in risky aggregation clusters. The ECO4CO data lake will be integrated with ad hoc data obtained by health care structures to understand trends and dynamics, to assess criticalities with respect to medical response and supplies, and to test possibilities useful to tackle potential future emergencies. The System will also provide geographical information on the spread of the infection which will allow an appropriate context-specific public health response to the epidemic. This project has been co-funded by the European Space Agency under its Business Applications programme.

Intelligent Process Adaptation in the SmartPM System

The increasing application of process-oriented approaches in new challenging dynamic domains beyond business computing (e.g., healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex knowledge-intensive processes in such domains. A knowledge-intensive process is influenced by user decision making and coupled with contextual data and knowledge production, and involves performing complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and knowledge-intensive processes must be robust to unexpected conditions and adaptable to unanticipated exceptions, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. To tackle this issue, in this paper we present SmartPM, a model and a prototype Process Management System featuring a set of techniques providing support for automated adaptation of knowledge-intensive processes at runtime. Such techniques are able to automatically adapt process instances when unanticipated exceptions occur, without explicitly defining policies to recover from exceptions and without the intervention of domain experts at runtime, aiming at reducing error-prone and costly manual ad-hoc changes, and thus at relieving users from complex adaptations tasks. To accomplish this, we make use of well-established techniques and frameworks from Artificial Intelligence, such as situation calculus, IndiGolog and classical planning. The approach, which is backed by a formal model, has been implemented and validated with a case study based on real knowledge-intensive processes coming from an emergency management domain.

Assistive device with conventional, alternative, and brain-computer interface inputs to enhance interaction with the environment for people with amyotrophic lateral sclerosis: a feasibility and usability study

OBJECTIVE

To evaluate the feasibility and usability of an assistive technology (AT) prototype designed to be operated with conventional/alternative input channels and a P300-based brain-computer interface (BCI) in order to provide users who have different degrees of muscular impairment resulting from amyotrophic lateral sclerosis (ALS) with communication and environmental control applications.

DESIGN

Proof-of-principle study with a convenience sample.

SETTING

An apartment-like space designed to be fully accessible by people with motor disabilities for occupational therapy, placed in a neurologic rehabilitation hospital.

PARTICIPANTS

End-users with ALS (N=8; 5 men, 3 women; mean age ± SD, 60 ± 12 y) recruited by a clinical team from an ALS center.

INTERVENTIONS

Three experimental conditions based on (1) a widely validated P300-based BCI alone; (2) the AT prototype operated by a conventional/alternative input device tailored to the specific end-user's residual motor abilities; and (3) the AT prototype accessed by a P300-based BCI. These 3 conditions were presented to all participants in 3 different sessions.

MAIN OUTCOME MEASURES

System usability was evaluated in terms of effectiveness (accuracy), efficiency (written symbol rate, time for correct selection, workload), and end-user satisfaction (overall satisfaction) domains. A comparison of the data collected in the 3 conditions was performed.

RESULTS

Effectiveness and end-user satisfaction did not significantly differ among the 3 experimental conditions. Condition III was less efficient than condition II as expressed by the longer time for correct selection.

CONCLUSIONS

A BCI can be used as an input channel to access an AT by persons with ALS, with no significant reduction of usability.

On the Discovery of Declarative Control Flows for Artful Processes

Artful processes are those processes in which the experience, intuition, and knowledge of the actors are the key factors in determining the decision making. They are typically carried out by the “knowledge workers,” such as professors, managers, and researchers. They are often scarcely formalized or completely unknown a priori. Throughout this article, we discuss how we addressed the challenge of discovering declarative control flows in the context of artful processes. To this extent, we devised and implemented a two-phase algorithm, named MINERful. The first phase builds a knowledge base, where statistical information extracted from logs is represented. During the second phase, queries are evaluated on that knowledge base, in order to infer the constraints that constitute the discovered process. After outlining the overall approach and offering insight on the adopted process modeling language, we describe in detail our discovery technique. Thereupon, we analyze its performances, both from a theoretical and an experimental perspective. A user-driven evaluation of the quality of results is also reported on the basis of a real case study. Finally, a study on the fitness of discovered models with respect to synthetic and real logs is presented.

Smart homes to improve the quality of life for all

A home is smart when, being aware of its own state and that of its users, is capable of controlling itself in order to support the user wishes and thus improving their quality of life. This holds both for users with special needs and for those with ordinary domestic needs. In this paper, we overview the Smart Homes for All project which represents the current state of the art with respect to software control and user interfaces in the smart homes arena.

WORKPAD

In complex emergency/disaster scenarios, persons from teams from various emergency-response organizations collaborate to achieve a common goal. In these scenarios, the use of smart mobile devices and applications can improve the collaboration dynamically. The lack of basic interaction principles can be dangerous, as it could increase the level of disaster or can make the efforts ineffective. This paper examines the main results of the project WORKPAD finished in December 2009. WORKPAD worked on a two-level architecture to support rescue operators during emergency management. The use of a user-centered design methodology during the entire development cycle has guaranteed that the architecture and resulting system meet end-user requirements. The feasibility of its use in real emergencies is also proven by a demonstration showcased with real operators. The paper includes qualitative and quantitative results and presents guidelines that can be useful in developing emergency-management systems.