In the last years, ICT-based Remote Patients' Monitoring (RPM) programmes are being developed to address the continuously increasing socioeconomic impact of Chronic Obstructive Pulmonary Disease (COPD). ICTbased RPM assures the automatic, regular collection of multivariate time series of patient's data. These can be profitably used to assess patient's health status and detect the onset of disease's exacerbations. This paper presents an approach to suitably represent and analyze the temporal data acquired during COPD patients' tele-monitoring so as to extend usual methods based on e-diary cards. The approach relies on Temporal Abstractions (TA) to extract significant information about disease's trends and progression. In particular, the paper describes the application of TA to identify relevant patterns and episodes that are, then, used to obtain a global picture of patient's conditions. The global picture mainly consists of TA-based qualitative and quantitative features that express: (i) a characterization of disease's course in the most recent period; (ii) a summarization of the global disease evolution based on the most frequent pattern; and (ii) a profiling of the patient, based on anamnesis data combined with a summary of disease progression. The paper focuses on the description of the extracted features and discusses their significance and relevance to the problem at hand. Further work will focus on the development of intelligent applications able to recognize and classify the extracted information.

This paper presents a decision making approach for the remote management of COPD patients based on the early detection of disease exacerbation episodes. An e-diary card is defined to evaluate a number of physiological variables and clinical parameters acquired remotely by means of wearable and environmental sensors deployed in patients' long-stay settings. The automatic evaluation of the card results in a socalled Chronic Status Index (CSI) whose computation is tailored to patients' specific manifestation of the disease (i.e., patient's phenotype). The decision support method relies on a parameterized analysis of CSI variations so as to early detect worsening changes, identify exacerbation severity and track the patterns of recovery. A web-based service has been set up and simulation tests have shown the feasibility of the method. Validity and sensitivity are currently being evaluated in a trail study with 30 COPD patients, monitored at home.

Chronic diseases may cause major limitations in patients' daily living due to acute or deterioration events, which can happen more or less frequently and, often, cannot be totally relieved, causing a worsening of patients' conditions. In the last years, a strong effort is being spent in the development of intelligent ICT applications for patients' telemonitoring, aimed at maximizing the quality of life of chronic patients by means of a regular collection of information about their status and actions in a long-stay setting. In this paper, a knowledge based decision support system is presented, which is aimed at aiding clinical professionals in managing chronic patients on a daily basis, by assessing their current status, helping face their worsening conditions, and preventing their exacerbation events.

Remote Health Monitoring (RHM) programmes are being increasingly developed to face the pervasive diffusion of chronic diseases. RHM strongly relies on ICT intelligent platforms devised to remotely acquire multisource data, process these according to specific domain knowledge and support clinical decision making. However, since RHM domain is continuously evolving and the pertinent knowledge is not yet consolidated, there is a great demand for services and tools that allow the encoded knowledge to be modified and enriched. This paper presents a Knowledge Editing Service (KES), which aims at enabling clinicians to insert novel knowledge, in a controlled fashion, into an ICT intelligent platform. The solution proposed is innovative since it addresses synergistically peculiar issues related to (i) RHM knowledge format; (ii) controlled editing patterns; (iii) knowledge verification and (iv) cooperative knowledge editing. None of the existing methods and systems for knowledge authoring tackles all these aspects at the same time. A prototype of the KES has been implemented and evaluated in real operational conditions.

Due to the current socio-economic impact of chronic diseases, a strong effort is being spent in the development of ICT applications able to support a new care paradigm specialized for chronic patients. Such applications are mainly based on patients' telemonitoring for the collection of a number of relevant physiological parameters aimed at identifying and preventing acute events, while maximizing patients' quality of life and reducing clinical costs. The most advanced and challenging features of these ICT applications are intelligent services devoted to the interpretation of monitored patients' data for supporting clinicians in their routine management of chronic patients. In this paper, a Knowledge-based Clinical Decision Support System (KB-CDSS) is presented, which is aimed at aiding clinical professionals in managing chronic patients on a daily basis, by assessing their current status, helping face their worsening conditions, and preventing disease exacerbation events. The CDSS has been developed by encoding the relevant knowledge elicited from clinicians who have a large experience in patients' monitoring. A formalism based on ontologies and rules was selected to build the Knowledge Base according to a scenario based approach. The system is currently under validation for the management of real clinical cases.

This paper presents an advanced search module for bibliography retrieval developed within the CHRONIOUS European IP project. The developed search module is specifically targeted to clinicians and healthcare practitioners searching for documents related to Chronic Obstructive Pulmonary Disease (COPD) and Chronic Kidney Disease (CKD). To this aim, the presented tool exploits two pathology-specific ontologies that allow focused document indexing and retrieval. Besides the search module, an enrichment tool is provided to maintain and to keep up-to date such as ontologies. In addition link with the terms of the MeSH (Medical Subject Heading) thesaurus has been provided to guarantee the coverage with the general certified medical terms and multilingual capabilities.

The report describes the design and implementation of a clinical decision support system able to aid clinicians in a remote management of chronic patients. The knowledge base of the system is described carefully, as well as the technical choices and the development details.

CHRONIOUS is an highly innovative Information and Communication Technologies (ICT) research Initiative that aspires to implement its vision for ubiquitous health and lifestyle monitoring. The 17 European project partners are strictly working together since February 2008 to realize an open platform to manage and monitor elderly patients with chronic diseases and many difficulties to reach hospital centers for routine controls. The testing activities will be done in Italy and Spain involving COPD (Chronic Obstructive Pulmonary Disease) and CKD (Chronic Kidney Disease) patients, these being widespread and highly expensive in terms of social and economic costs. Patients, equipped by wearable technologies and sensors and interacting with lifestyle interfaces, will be assisted by healthcare personnel able to check the health record and critical conditions through the Chronious platform data analysis and decision support system. Additionally, the new ontology based literature search engine will help the clinicians in the standardization of care delivery process. This paper is to present the main project objectives and its principal components from the intelligent system point of view.

The report summarized the functional specifications of a guidelines formalization framework based on the development of a Clinical Decision Support System for the management of chronic patients.