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On March 17, 2020, French authorities implemented a nationwide lockdown to respond to COVID-19 epidemic and curb the surge of patients requiring critical care, similarly to other countries. Evaluating the impact of lockdown on population mobility is essential to quantify achievable reductions and identify the factors driving the changes in social dynamics that affected viral diffusion. We used temporally resolved travel flows among 1,436 administrative areas of mainland France reconstructed from mobile phone trajectories. We measured mobility changes before and during lockdown at both local and country scales. Lockdown caused a 65% reduction in countrywide number of displacements, and was particularly effective in reducing work-related short-range mobility, especially during rush hours, and recreational trips. Geographical heterogeneities showed anomalous increases in long-range movements even before lockdown announcement that were tightly localized in space. During lockdown, mobility drops were unevenly distributed across regions. They were strongly associated with active population, workers employed in sectors highly impacted by lockdown, and number of hospitalizations per region, and moderately associated with socio-economic level of the region. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting. Lockdown was effective in reducing population mobility across scales. Caution should be taken in the timing of policy announcements and implementation, as anomalous mobility followed policy announcements that may act as seeding events. On the other hand, risk aversion may be beneficial in further decreasing mobility in largely affected regions. Socio-economic and demographic constraints to the efficacy of restrictions were also identified. The unveiled links between geography, demography, and timing of the response to mobility restrictions may help design interventions that minimize invasiveness while contributing to the current epidemic response.FundingANR projects EVALCOVID-19 (ANR-20-COVI-0007) and DATAREDUX (ANR-19-CE46-0008-03); EU H2020 grants RECOVER (H2020-101003589) and MOOD (H2020-874850); REACTing COVID-19 modeling grant.

Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment and mitigation processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a hypothetical infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals’ spatial behaviour and its relationship with the risk of infectious diseases’ contagion. In particular, we show that CDRs-based indicators of individuals’ spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing mitigation strategies to support decision-making during country-level epidemics.

SummaryObjectives: This work aims at building a platform where quality-of-life data, namely utility coefficients, can be elicited not only for immediate use, but also systematically stored together with patient profiles to build a public repository to be further exploited in studies on specific target populations (e.g. cost/utility analyses).Methods: We capitalized on utility theory and previous experience to define a set of desirable features such a tool should show to facilitate sound elicitation of quality of life. A set of visualization tools and algorithms has been developed to this purpose. To make it easily accessible for potential users, the software has been designed as a web application. A pilot validation study has been performed on 20 atrial fibrillation patients.Results: A collaborative platform, UceWeb, has been developed and tested. It implements the standard gamble, time trade-off and rating-scale utility elicitation methods. It allows doctors and patients to choose the mode of interaction to maximize patients’ comfort in answering difficult questions. Every utility elicitation may contribute to the growth of the repository.Conclusion: UceWeb can become a unique source of data allowing researchers both to perform more reliable comparisons among healthcare interventions and build statistical models to gain deeper insight into quality of life data.

In the last twenty years, many different approaches to deal with Computer-Interpretable clinical Guidelines (CIGs) have been developed, each one proposing its own representation formalism (mostly based on the Task-Network Model) execution engine. We propose META-GLARE a shell for easily defining new CIG systems. Using META-GLARE, CIG system designers can easily define their own systems (basically by defining their representation language), with a minimal programming effort. META-GLARE is thus a flexible and powerful vehicle for research about CIGs, since it supports easy and fast prototyping of new CIG systems.

The inclusion of patients’ perspectives in clinical practice has become an important matter for health professionals, in view of the increasing attention to patient-centered care. In this regard, this report illustrates a method for developing a visual aid that supports the physician in the process of informing patients about a critical decisional problem. In particular, we focused on interpretation of the results of decision trees embedding Markov models implemented with the commercial tool TreeAge Pro. Starting from patient-level simulations and exploiting some advanced functionalities of TreeAge Pro, we combined results to produce a novel graphical output that represents the distributions of outcomes over the lifetime for the different decision options, thus becoming a more informative decision support in a context of shared decision making. The training example used to illustrate the method is a decision tree for thromboembolism risk prevention in patients with nonvalvular atrial fibrillation.

We provide a framework encapsulating patient's preferences elicitation and decision models.We define a shared decision-making framework to be used by physicians and patients together.We use as a case study the prevention of thromboembolism in atrial fibrillation patients.Using personalized utility coefficients allows taking into account the features of the population.We show how to integrate shared decision-making into a guideline-based decision support tool. ObjectiveTaking into account patients' preferences has become an essential requirement in health decision-making. Even in evidence-based settings where directions are summarized into clinical practice guidelines, there might exist situations where it is important for the care provider to involve the patient in the decision. In this paper we propose a unified framework to promote the shift from a traditional, physician-centered, clinical decision process to a more personalized, patient-oriented shared decision-making (SDM) environment. MethodsWe present the theoretical, technological and architectural aspects of a framework that encapsulates decision models and instruments to elicit patients' preferences into a single tool, thus enabling physicians to exploit evidence-based medicine and shared decision-making in the same encounter. ResultsWe show the implementation of the framework in a specific case study related to the prevention and management of the risk of thromboembolism in atrial fibrillation. We describe the underlying decision model and how this can be personalized according to patients' preferences. The application of the framework is tested through a pilot clinical evaluation study carried out on 20 patients at the Rehabilitation Cardiology Unit at the IRCCS Fondazione Salvatore Maugeri hospital (Pavia, Italy). The results point out the importance of running personalized decision models, which can substantially differ from models quantified with population coefficients. ConclusionsThis study shows that the tool is potentially able to overcome some of the main barriers perceived by physicians in the adoption of SDM. In parallel, the development of the framework increases the involvement of patients in the process of care focusing on the centrality of individual patients.

Clinical practice guidelines CPGs play an important role in medical practice, and computerized support to CPGs is now one of the most central areas of research in Artificial Intelligence in medicine. In recent years, many groups have developed different computer-assisted management systems of Computer Interpretable Guidelines CIGs. We propose a generalization: META-GLARE is a "meta"-system or, in other words, a shell to define new CIG systems. It takes as input a representation formalism for CIGs, and automatically provides acquisition, consultation and execution engines for it. Our meta-approach has several advantages, such as generality and, above all, flexibility and extendibility. While the meta-engine for acquisition has been already described, in this paper we focus on the execution meta-engine.

Clinical practice guidelines (CPGs) play an important role in medical practice, and computerized support to CPGs is now one of the most central areas of research in Artificial Intelligence in medicine. In recent years, many groups have developed different computer-assisted management systems of Computer Interpretable Guidelines (CIGs). From one side, there are several commonalities between different approaches; from the other side, each approach has its own peculiarities and is geared towards the treatment of specific phenomena. In our work, we propose a form of generalization: instead of defining “yet another CIG system”, we propose a META-GLARE, a “meta”-system (or, in other words, a shell) to define new CIG systems. From one side, we try to capture the commonalities, by providing (i) a general tool for the acquisition, consultation and execution of hierarchical directed graphs (representing the control flow of actions in CIGs), parameterized over the types of nodes and of arcs constituting it, and (ii) a library of different elementary components of guidelines nodes (actions) and arcs, in which each type definition involves the specification of how objects of this type can be acquired, consulted and executed. From the other side, we provide generality and flexibility, by allowing free aggregations of such elementary components to define new primitive node and arc types. In this paper, we first propose META-GLARE general architecture and then, for the sake of brevity, we will focus only on the acquisition issue.

Computer Interpretable Guidelines (CIG) are an emerging area of research, to support medical decision making through evidence-based recommendations. However, new challenges in the data management field have to be faced, to integrate CIG management with a proper treatment of patient data, and of other forms of medical knowledge. The GINSENG (GuIdeliNe SEcoNd Generation) project aims at setting the ground for the proposal of a four-year European project (in the HORIZON 2020 context) coping with such issues. The "visionary" idea underlying our long-term research proposal is that, to achieve a significant step forward in the state-of-the-art, it is important to provide an homogeneous approach integrating (at least) three different aspects (which have been often considered in isolation by the Medical Informatics literature): (1) Computer Interpretable Guidelines, (2) Treatment of patient data, and (3) Treatment of "basic" medical knowledge (BMK).

In the context of the EU project MobiGuide, the development of a patient-centric decision support system based on clinical guidelines is the main focus. The project is addressed to patients with chronic illnesses, including atrial fibrillation (AF). In this paper we describe a shared-decision model framework to address those situations, described in the guideline, where the lack of hard evidence makes it important for the care provider to share the decision with the patient and/or his relatives. To illustrate this subject we focus on an important subject tackled in the AF guideline: thromboembolic risk prevention. We introduce a utility model and a cost model to collect patient’s preferences. On the basis of these preferences and of literature data, a decision model is implemented to compare different therapeutic options. The development of this framework increases the involvement of patients in the process of care focusing on the centrality of individual subjects.

This work evaluates the users' satisfaction with an SMS-based reminder system that is being used since about six years by an Italian healthcare organization. The system was implemented for reducing dropouts. This goal has been achieved, as dropout decreased from 8% to 4%. During these years, a number of reminded citizens, even not required, sent an SMS message back, with comments about the service, further requirements, etc. We collected some thousands of them. Their analysis may represent a useful feedback to the healthcare organization. We used conditional random fields as the information extraction method for classifying messages into appreciation, critique, inappropriateness, etc. The classification system achieved a very good overall performance (F1-measure of 94%), thus it can be used from here on to monitor the users' satisfaction in time.

Background Adding docetaxel (Taxotere, T) to induction chemotherapy with platinum/infusional 5-FU (PF) has been shown to improve overall survival of patients with head and neck cancer. The aim of the study was to analyze the cost–utility of TPF in patients with unresectable disease. Design We developed a Markov model to represent patient's weekly transitions among different health states, related to treatment or disease status. Transition probabilities were obtained from the TAX 324 clinical trial report and from the European Organization for Research and Treatment of Cancer (EORTC) 24971/TAX 323 raw data. Costs were estimated in Italy from a Regional Healthcare System perspective. A 5-year temporal horizon was adopted and a 3.5% yearly discount rate was applied. Results When compared with PF, TPF treatment increases life expectancy by 0.33 quality-adjusted life-years (QALYs) in TAX 323 and 0.41 QALYs in TAX 324. The benefit was achieved at a cost of €11 822/QALY for TAX 323 and €6757/QALY for TAX 324. Monte Carlo sensitivity analysis showed that 69% (TAX 323) and 99% (TAX 324) of the results lie below the threshold of €50 000/QALY saved. Conclusions In our analysis, TPF induction chemotherapy proved to be cost-effective when compared with PF, having a cost–utility ratio comparable to other widely accepted healthcare interventions.

International audience; Objective : Information about medications is critical in supporting decision-making during the prescription process and thus in improving the safety and quality of care. In this work, we propose a methodology for the automatic recognition of drug-related entities (active ingredient, interaction effects, etc.) in textual drug descriptions, and their further location in a previously developed domain ontology.Methods and material: The summary of product characteristics (SPC) represents the basis of information for health professionals on how to use medicines. However, this information is locked in free-text and, as such, cannot be actively accessed and elaborated by computerized applications. Our approach exploits a combination of machine learning and rule-based methods. It consists of two stages. Initially it learns to classify this information in a structured prediction framework, relying on conditional random fields. The classifier is trained and evaluated using a corpus of about a hundred SPCs. They have been hand-annotated with different semantic labels that have been derived from the domain ontology. At a second stage the extracted entities are added in the domain ontology corresponding concepts as new instances, using a set of rules manually-constructed from the corpus.Results: Our evaluations show that the extraction module exhibits high overall performance, with an average F1-measure of 88% for contraindications and 90% for interactions.Conclusion: SPCs can be exploited to provide structured information for computer-based decision support systems.

The use of medications has a central role in health care provision, yet on occasion, it may injure the person taking them as result of adverse drug events. A correct drug choice must be modulated to acknowledge both patients’ status and drug-specific information. However, this information is locked in free-text and, as such, cannot be actively accessed and elaborated by computerized applications. The goal of this work lies in extracting content (active ingredient, interaction effects, etc.) from the Summary of Product Characteristics, focusing mainly on drug-related interactions, following a machine learning based approach. We compare two state of the art classifiers: conditional random fields with support vector machines. To this end, we introduce a corpus of 100 interaction sections, hand annotated with 13 labels that have been derived from a previously developed conceptual model. The results of our empirical analysis demonstrate that the two models perform well. They exhibit similar overall performance, with an overall accuracy of about 91%.

6075 Background: Adding docetaxel to induction therapy with PF has been shown to improve overall survival of patients with head and neck cancer. On the basis of the raw data from the EORTC trial 24971/TAX 323 a pharmacoeconomic analysis has been performed. A National Healthcare System perspective and a 5-years temporal horizon have been adopted. Methods: A decision-analytic approach has been adopted, and a Markov model has been used to represent patient's weekly transitions among different health states, related to treatment (chemotherapy, radiotherapy, surgeries, palliative care) and disease status (complete/partial/no response, progression, recurrence). Constant or time-variant costs and quality of life adjustment factors have been assigned to each health state, together with transition probabilities to other states. A 3% discount factor has been used for costs sustained after the first year. The model allowed calculating expected life years (LYs), quality adjusted life years (QALYs) and costs. From these figures, incremental cost/effectiveness (ICER) and cost/utility (ICUR) ratios have been calculated. Robustness of results obtained with baseline values has been tested with probabilistic multivariate sensitivity analysis. Results: In the base case analysis, treatment with TPF yields an expectancy of 2.38 LYs and 1.53 QALYs at a cost of Euro (E) 23,018, which for PF were 1.93 LYs, 1.11 QALYs, and E 19,567, respectively. ICER and ICUR were E 7,669/LY and E 8,217/QALY, respectively. Sensitivity analyses were performed varying transition probabilities of 10%, costs of 30% and utility scores of 20% of their basal values. The analyses showed that 86% and 84% of the results lie below the threshold of E 50,000/LY and E 50,000/QALY, respectively. Conclusions: In our analysis TPF induction chemotherapy proved to be cost-effective with respect to PF, having an ICER and ICUR comparable to other widely accepted healthcare interventions. [Table: see text]

International audience; No abstract available

International audience