A comparison of spatial-based targeted disease mitigation strategies using mobile phone data

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

Information extraction from SMS text related to a reminder service for outpatients.

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

A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data

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\u2019 spatial behaviour and its relationship with the risk of infectious diseases\u2019 contagion. In particular, we show that CDRs-based indicators of individuals\u2019 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

Information extraction from SMS text related to a reminder service for outpatients.

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

A system for the extraction and representation of summary of product characteristics content.

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

UceWeb: a Web-based Collaborative Tool for Collecting and Sharing Quality of Life Data.

Objectives: 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