Abstract Dynamic Treatment Regimes (DTRs) are adaptive treatment strategies that allow clinicians to personalize dynamically the treatment for each patient based on their step-by-step response to their treatment. There are a series of predefined alternative treatments for each disease and any patient may associate with one of these treatments according to his/her demographics. DTRs for a certain disease are studied and evaluated by means of statistical approaches where patients are randomized at each step of the treatment and their responses are observed. Recently, the Reinforcement Learning (RL) paradigm has also been applied to determine DTRs. However, such approaches may be limited by the need to design a true reward function, which may be difficult to formalize when the expert knowledge is not well assessed, as when the DTR is in the design phase. To address this limitation, an extension of the RL paradigm, namely Inverse Reinforcement Learning (IRL), has been adopted to learn the reward function from data, such as those derived from DTR trials. In this paper, we define a Projection Based Inverse Reinforcement Learning (PB-IRL) approach to learn the true underlying reward function for given demonstrations (DTR trials). Such a reward function can be used both to evaluate the set of DTRs determined for a certain disease, as well as to enable an RL-based intelligent agent to self-learn the best way and then act as a decision support system for the clinician.

Anomaly detection in industrial environments aims at detecting anomalies in the monitoring data of industrial machinery or equipment, as soon as possible, preferably presenting real-time alarms, to alert the monitoring staff and start maintenance activities timely. In this paper, the problem of anomaly detection of an industrial furnace is tackled, for the real-time recognition of punctual anomalies on multivariate time series. To this aim, a real-time anomaly detection approach is proposed: first, time series acquired from the real machinery are filtered, to select those of interest for possible anomalies, and pre-processed, to obtain sliding windows for real-time detection, then distinct univariate models are applied, to identify different anomaly types. For the application considered here, data regarding the machinery behaviour were available only for normal functioning, thus an unsupervised approach is chosen. In particular, deep learning models based on autoencoders are used to detect punctual anomalies, by reconstructing each window and evaluating the reconstruction error of its last point. An extensive set of autoencoder models is proposed, with varying architecture in terms of type of model (vanilla/variational autoencoders), type of layers (fully connected/LSTM/BiLSTM), and hyperparameters (number of layers, intermediate sizes, BiLSTM type). Available data are split, and used to train the models, and to test them on the normal signal and on synthetic anomalies injected on it, which are of particular interest and were designed according to domain experts. Performances of the proposed models show differences among them, depending on the model architecture. The most efficient models, in terms of F1 score of detection and number of parameters, are identified by their t-test comparison, and the capability of detecting anomalies online is demonstrated. In particular, the proposed anomaly detection approach, including a selected autoencoder with LSTM layers, is able to correctly recognize normal trends, with very few false positives, and promptly give alarms as different anomalous trends appear.

This paper describes a cloud-based platform that offers evidence-based, personalised interventions powered by Artificial Intelligence to help support efficient remote monitoring and clinician-driven guidance to people over 65 who suffer or are at risk of hearing loss, cardiovascular diseases, cognitive impairments, balance disorders, and mental health issues. This platform has been developed within the SMART-BEAR integrated project to power its large-scale clinical pilots and comprises a standards-based data harmonisation and management layer, a security component, a Big Data Analytics system, a Clinical Decision Support tool, and a dashboard component for efficient data collection across the pilot sites.

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net.

Abstract In the last decade, the demand for readily accessible corpora has touched all areas of natural language processing, including coreference resolution. However, it is one of the least considered sub-fields in recent developments. Moreover, almost all existing resources are only available for the English language. To overcome this lack, this work proposes a methodology to create a corpus for coreference resolution in Italian exploiting knowledge of annotated resources in other languages. Starting from OntonNotes, the methodology translates and refines English utterances to obtain utterances respecting Italian grammar, dealing with language-specific phenomena and preserving coreference and mentions. A quantitative and qualitative evaluation is performed to assess the well-formedness of generated utterances, considering readability, grammaticality, and acceptability indexes. The results have confirmed the effectiveness of the methodology in generating a good dataset for coreference resolution starting from an existing one. The goodness of the dataset is also assessed by training a coreference resolution model based on BERT language model, achieving the promising results. Even if the methodology has been tailored for English and Italian languages, it has a general basis easily extendable to other languages, adapting a small number of language-dependent rules to generalize most of the linguistic phenomena of the language under examination.

Edge Analytics and Artificial Intelligence are important features of the current smart connected living community. In a society where people, homes, cities, and workplaces are simultaneously connected through various devices, primarily through mobile devices, a considerable amount of data is exchanged, and the processing and storage of these data are laborious and difficult tasks. Edge Analytics allows the collection and analysis of such data on mobile devices, such as smartphones and tablets, without involving any cloud-centred architecture that cannot guarantee real-time responsiveness. Meanwhile, Artificial Intelligence techniques can constitute a valid instrument to process data, limiting the computation time, and optimising decisional processes and predictions in several sectors, such as healthcare. Within this field, in this article, an approach able to evaluate the voice quality condition is proposed. A fully automatic algorithm, based on Deep Learning, classifies a voice as healthy or pathological by analysing spectrogram images extracted by means of the recording of vowel /a/, in compliance with the traditional medical protocol. A light Convolutional Neural Network is embedded in a mobile health application in order to provide an instrument capable of assessing voice disorders in a fast, easy, and portable way. Thus, a straightforward mobile device becomes a screening tool useful for the early diagnosis, monitoring, and treatment of voice disorders. The proposed approach has been tested on a broad set of voice samples, not limited to the most common voice diseases but including all the pathologies present in three different databases achieving F1-scores, over the testing set, equal to 80%, 90%, and 73%. Although the proposed network consists of a reduced number of layers, the results are very competitive compared to those of other “cutting edge” approaches constructed using more complex neural networks, and compared to the classic deep neural networks, for example, VGG-16 and ResNet-50.

The automatic ICD-10 classification of medical documents is actually an unresolved issue, despite its crucial importance. The existence of machine learning approaches de-voted to this task is in contrast with the lack of annotated resources, especially for languages different from English. Recent Transformer-based multilingual neural language models at scale have provided an innovative approach for dealing with cross lingual Natural Language Processing tasks. In this paper, we present a preliminary evaluation of the Cross-lingual Language Model (XLM) architecture, a recent multilingual Transformer-based model presented in literature, tested in the cross lingual ICD-10 multilabel classification of short medical notes. In detail, we analysed the performances obtained by fine tuning the XLM model on English language training data and tested for ICD-10 codes prediction of an Italian test set. The obtained results show that the use of the novel XLM multilingual neural language architecture is very promising and it can be very useful in case of low resource languages.

In this paper we propose an architecture specifically devoted to the analysis of huge natural language biomedical textual collections, with the purpose of searching for semantic similarity in order to obtain useful hints for effective simulation that could help physicians in diagnosis tasks. We leverage Word Embedding models trained with word2vec algorithm and a Big Data architecture for their processing and management. We performed some preliminary analyses using a dataset extracted from the whole PubMed library and we developed a web front-end to show the usability of this methodology in a real context.

In this paper we address the problem of analyzing biomedical data collection with the purpose of searching for semantic similarity among textual documents. In details, we leverage Word Embeddings models obtained by word2vec algorithm and a specific Big Data architecture for their management, defining an approach able to permit the retrieving of semantic similar texts among a huge biomedical text corpus. The proposed architecture has been developed with the purpose of improving a previous implementation, lowering the computational time and allowing in this way the use of the whole PubMed library as dataset, proving also the usability of this methodology in a real context.

Atherosclerosis is a multifactorial disease that affects a significant number of people during their lifetime. It is a pathology clinically silent for years that develops with a gradual thickening of the vessel walls and a consecutive formation of plaque. This is the cause of several dangerous conditions such as ischemic stroke, the most common cause of stroke in middle-aged people. To avoid and reduce these events a continuos and meticulous monitoring of patients with any carotid diseases is necessary. This paper presents an objective measure of the progression of a carotid patology. An index capable of distinguishing between the initial state of thickening of the carotid arterial walls and the successive presence of more serious plaque has been defined. The presence of thickening or plaque is estimated by evaluating Heart Rate Variability. This is a non-invasive approach, able to estimate characteristic parameters in an easy and efficient way, constituting an accurate and optimum instrument for a real-time continuous monitoring. Locally Weighted Learning has been used to automatically find a relationship between these parameters and the presence of a disorder, tested on an available dataset.

In the current Italian eHealth scenario, a national IT platform has been designed and developed with the purpose of ensuring the interoperability between the various Electronic Health Record (EHR) systems that have been adopted in the different regions of the country, according to the requirements provided by Italian Laws. In this way, the healthcare providers and the policy makers can acquire and process the data of a patient despite its initial format and source, allowing an improved quality of patient care and optimizing the management of the financial resources. To further exploit this huge resource of health and social data, it is very important to allow the extraction of the complex information buried under the Big Data source enabled by the EHRs, providing the physicians, the researchers and public health policy makers with innovative instruments. Meeting this need is not a trivial task, due to the difficulties of processing different document formats and processing Natural Language text, alongside to the problems related to the data size. In this paper we propose a Big Data architecture that is able to extract information from the documents acquired by the EHRs, integrate and process them, providing a set of valuable data for both physicians and patients, as well as decision makers.

Health 4.0 can provide effective ways to improve the health status of subjects by taking advantage of cyber-physical systems and Internet of things technologies for the solution of healthcare problems. One of these is represented by suitably estimating blood pressure values of subjects in a continuous, real-time, and noninvasive way. To address it, we propose an approach only requiring a photoplethysmography (PPG) sensor and a mobile/desktop device. The approach avails itself of genetic programming to automatically find an explicit relationship between blood pressure values and PPG ones. This relationship is tested on a set of 11 subjects and compared against other regression methods, and turns out to be better. Namely, the root-mean-square error values are equal to 8.49 and 6.66 for the systolic and the diastolic blood-pressure values, respectively. Those for the relative error, instead, are equal to 5.55% for the systolic and 6.59% for the diastolic values.

The wide availability of sensing devices in the medical domain causes the creation of large and very large data sets. Hence, tasks as the classification in such data sets becomes more and more difficult. Deep Neural Networks (DNNs) are very effective in classification, yet finding the best values for their hyper-parameters is a difficult and time-consuming task. This paper introduces an approach to decrease execution times to automatically find good hyper-parameter values for DNN through Evolutionary Algorithms when classification task is faced. This decrease is obtained through the combination of two mechanisms. The former is constituted by a distributed version for a Differential Evolution algorithm. The latter is based on a procedure aimed at reducing the size of the training set and relying on a decomposition into cubes of the space of the data set attributes. Experiments are carried out on a medical data set about Obstructive Sleep Anpnea. They show that sub-optimal DNN hyper-parameter values are obtained in a much lower time with respect to the case where this reduction is not effected, and that this does not come to the detriment of the accuracy in the classification over the test set items.

Question answering (QA) is a challenging task and has received considerable attention in the last years. Answer selection among candidate answers is one of the main phases for QA and the best answer to be returned is determined in this phase. A common approach consists in considering the selection of the final answer(s) as a ranking problem. So far, different methods have been proposed, mainly oriented to produce a single best ranking model operating in the same way on all the question types. Differently, this paper proposes a fuzzy approach for ranking and selecting the correct answer among a list of candidates in a state-of-the-art QA system operating with factoid and description questions on Italian corpora pertaining a closed domain. Starting from the consideration that this ranking problem can be reduced to a classification one, the proposed approach is based on the Likelihood-Fuzzy Analysis (LFA), applied in this case for mining fuzzy rule-based models able to discern correct (True) from incorrect answers (False). Such fuzzy models are mined as specifically tailored to each question type, and, thus, can be individually applied to produce a more robust and accurate final ranking. An experimental session over a collection of questions pertaining the Cultural Heritage domain, using a manually annotated gold-standard dataset, shows that considering specific fuzzy ranking models for each question type improves the accuracy of the best answer returned back to the user.

Clinical summarization means the collection and synthesis of a patient's significant data, undertaken in order to support health-care providers in the process of patient care. Considering that medical information comes from multiple sources, a system for the automatic generation of problem lists could prove to be very effective in terms of saving time in the analysis of large amounts of medical data. In this paper, we propose a system able to acquire and present relevant references to medical disorders from a patient's history, producing a subject-oriented summary. The implemented system relies on an NLP pipeline, for the extraction of relevant medical entities contained in narrative health records, and on several queries, necessary for the scanning of structured documents. The tool aggregates any medical problems, performed procedures, and prescribed medications, providing the healthcare practitioner with a visual summary of the patient's data.

This paper aims to investigate and compare the accuracy of different data mining classification schemes, employing Ensemble Machine Learning Techniques, for the prediction of heart disease. The Cleveland data set for heart diseases, containing 303 instances, has been used as the main database for the training and testing of the developed system. 10-Fold Cross-Validation has been applied in order to increase the amount of data, which would otherwise have been limited. Different classifiers, namely Decision Tree (DT), Naïve Bayes (NB), Multilayer Perceptron (MLP), K-Nearest Neighbor (K-NN), Single Conjunctive Rule Learner (SCRL), Radial Basis Function (RBF) and Support Vector Machine (SVM), have been employed. Moreover, the ensemble prediction of classifiers, bagging, boosting and stacking, has been applied to the dataset. The results of the experiments indicate that the SVM method using the boosting technique outperforms the other aforementioned methods.

In the last years, the interest about enhancing the interface usability of applications has strongly increased, focusing, in particular, on chatbots, i.e. conversational agent that interacts with users, turn by turn using natural language. However, building chatbots for answering to questions over structured medical knowledge bases is a very thorny task and is still considered an open research challenge. In order to face this issue, this paper proposes a knowledge-based conversational chatbot for medical question answering, aimed at supporting: i) the formulation of factoid questions over medical knowledge bases; ii) the generation of more precise and contextualized dialog responses by analyzing the relations between entities in knowledge bases; iii) the detection of ambiguous user intents, with respect to the current dialog state and the suggestion of some interaction hints aimed at clarifying and/or confirming their meaning. A relevant characteristic of this system is represented by the usage of Knowledge Graphs to formally represent textual inputs given by the user as well as templates of questions and, contextually, efficiently navigate and use the domain knowledge of interest to provide an answer. The proposed chatbot has been implemented as a desktop application named “Medical Assistant” able to conversate with users interested to diagnose and identify the possible diseases causing a symptom, and find the most suitable treatment for a medical problem. It has been proficiently tested with respect to some factoid questions, showing its capability to help user reach the desired information also in the case of initial missing information.

Question Classification (QC) is a very important module, to include into the pipeline usually employed to implement the Question Answering paradigm. Recently, good results have been achieved on the QC task by using Convolutional Neural Networks (CNNs). This approach requires setting a CNN architecture and a huge number of hyperparameters to obtain the desirable achievements, and only little research has been addressed on this activity. Moreover, while the greatest part of research strength focused on English language, very few works dealt with other languages. In this work, an approach based on neural networks is used to classify Italian questions taken from a TREC dataset. In particular, different solutions regarding the CNN architecture are tested, and, according to literature advices, the best settings are searched in the proper ranges, in order to maximize the classification power for the particular case of Italian questions dataset.

Classification systems based on Fuzzy Logic are of particular importance in the ambit of cognitive systems, due to their ability of managing uncertainty and presenting interpretable knowledge bases by emulating human cognition processes. However, the notion of interpretability is not yet exhaustively defined. In this work, the properties assessing the interpretability of a fuzzy classifier are discussed, and on this basis two indexes are proposed, which numerically evaluate readability and semantic interpretability, respectively. These indexes are calculated for a benchmark dataset, showing how they can be used to quantitatively compare systems with different characteristics.

The aim of this study is to identify an explicit relationship between life-style and the risk of falling under the form of a mathematical model. Starting from some personal and behavioral information as, e.g., weight, height, age, data about physical activity habits, and concern about falling, the model would easily estimate the score of the Mini-Balance Evaluation Systems (Mini-BES) test. This would make fall risk assessment less invasive, because subjects would not need to undergo the classical Mini-BES test, rather they could estimate it at home by answering some questionnaires. The mathematical model obtained in this study has been tested over a subset of unseen subjects and the results show an average error of ±2.74.

Information management in healthcare is nowadays experiencing a great revolution. After the impressive progress in digitizing medical data by private organizations, also the federal government and other public stakeholders have also started to make use of healthcare data for data analysis purposes in order to extract actionable knowledge. In this paper, we propose an architecture for supporting interoperability in healthcare systems by exploiting Big Data techniques. In particular, we describe a proposal based on big data techniques to implement a nationwide system able to improve EHR data access efficiency and reduce costs.

Recently, innovative and mobile health services have been developed by embedding knowledge-based systems, with the aim of remotely promoting wellness and healthy lifestyle, monitoring patients' chronic diseases and improving their adherence to therapies. Even if different knowledge-based systems have been proposed for mobile devices, they are typically based on precise production rules built on the top of ontological primitives for describing the domain of interest. Thus, they are not able to handle medical knowledge graded and affected by uncertainty, which often underlies medical decision-making processes. In order to address this topic, this paper presents a hybrid, rule-based reasoning system for mobile devices aimed at enabling the realization of intelligent health services. This system is essentially characterized by two main features: i) a hybrid knowledge representation approach for modelling productions rules involving both precise and vague information by integrating ontological and fuzzy primitives; ii) a lazy reasoning algorithm able to efficiently process this hybrid knowledge and timely produce answers. A case study has been arranged in order to evaluate the effectiveness of the proposed system within a mobile application for detecting heart arrhythmias.

Technological innovations coupled with the rapidly expanding amount of medical data digitally collected and stored have made possible to develop advanced Decision Support Systems (DSSs) able to aid physicians in medical diagnosis, by helping them in classifying among different diseases. Building this typology of DSSs preliminarily requires the extraction from huge sets of clinical data of hidden relationships between possible classes and known features. This task is very thorny due to information uncertainties affecting features as well as biases regarding prior probabilities of classes, depending on the environment and conditions of data collection. To address these problems, this paper presents an approach for building an interval type-2 fuzzy DSS able to handle information uncertainties and perform unbiased medical diagnoses, by adapting interval type-2 fuzzy sets to a medical dataset. A proof of concept is given by applying the proposed approach on a benchmark medical dataset. A comparison is performed between i) a type-1 fuzzy system with prior probabilities extracted from the dataset; ii) an interval type-2 fuzzy DSSs modelling non-biased prior probabilities; and iii) the best known classification methods. This comparison is made both when prior probabilities are fixed, i.e. when the system is evaluated by a stratified cross-validation technique, or while no assumption is made about prior probabilities. Results show that the proposed approach reach good performance in most of the cases, thus evidencing the usefulness of extracting an interval type-2 fuzzy system not based on biased prior probabilities, for obtaining more reliable results.

Continuous blood pressure (BP) measurement is an important issue in the medical field. The hypothesis of existence of a nonlinear relationship between plethysmography (PPG) and BP values has been investigated in this paper. If this hypothesis is true, then it is possible to indirectly measure patient's BP in a non-invasive way through the application of a wearable wireless PPG sensor to patient's finger and through the use of the results of a regression analysis aimed at linking PPG and BP values. To find the relationship between these two biomedical characteristics we have used here Genetic Programming (GP), because in a regression task it can evolve in an automatic way the structure of the most suitable explicit mathematical model. An analysis of the related scientific literature shows that this is the first attempt to mathematically relate PPG and BP values through GP. In this paper some preliminary experiments on the use of GP in facing this regression task have been carried out. As a result, for both systolic and diastolic BP values explicit mathematical models providing nonlinear relationship between PPG and BP values have been achieved, involving an approximation error of around 2 mmHg in both cases. A prototypal mobile-based system has been realized which is able to continuously estimate in real time the two BP values for any given patient by using only a plethysmography signal and the obtained mathematical models.

PeopleN EURODEGENERATIVE diseases are progressive and difficult to diagnose in their early stages.Progressive impairment in the activities of daily living, as well as cognitive deterioration, leads to an increase in patient's dependence.Neuropsychiatric symptoms are common features and include psychosis (delusions and hallucinations), depressive mood, anxiety, irritability/lability, apathy, euphoria, disinhibition, agitation/aggression, aberrant motor activities, sleep disturbance, and eating disorders.Additionally, patients have a tendency to wander and misplace objects in their environment.Therefore, cognitive impaired patients need constant monitoring to ensure their wellbeing, and that they do not harm themselves.Ambient intelligence (AmI) is the computing paradigm that can provide support both for cognitive impaired people within their domestic environments and for clinicians to collect data needed to assess the disease.A novel class of applications can potentially emerge from the specialization of AmI technologies to the special domain of cognitive impaired users.However, such applications must be able to deal with peculiarities due to irrational and abnormal behaviors of cognitive impaired people within their environments, and their general attitude to not accepting wearable technologies.Several technical challenges remain to be solved before effective, intelligent, secure, and reliable AmI applications for the support of cognitive impaired people can be deployed trustfully into real environments.In this special section, the paper "The role of Virtual Motor Rehabilitation.A quantitative analysis between Acute and Chronic Patients with acquired brain injury," concerning acquired brain injury, compares the evolution of chronic patients with acute patients in a virtual rehabilitation program.For this study authors have developed a "Vestibular Virtual Rehabilitation" system.Results have shown a similar recovery for chronic and acute patients during the period of intervention.It has also been shown that chronic patients stop their improvement when they finish the training encouraging the migration of such rehabilitation technologies to home.In the contribution "An Adaptable and Flexible Framework for Assistive Living of Cognitively Impaired People" describes guidelines and an adaptable framework for the dynamic integration of assistive services with their related sensing technologies and interaction devices.The paper on "Statistical Anomaly Detection for Individuals with Cognitive Impairments" focuses on the identification of anomalies for users' routes, which may be useful to assist the impaired in case of losing one's way or to alert caregivers in case of wandering.The proposed approach relies on the exploitation Digital Object

Detection and real time monitoring of obstructive sleep apnea (OSA) episodes are very important tasks in healthcare. To suitably face them, this paper proposes an easy-to-use, cheap mobile-based approach relying on three steps. First, single-channel ECG data from a patient are collected by a wearable sensor and are recorded on a mobile device. Second, the automatic extraction of knowledge about that patient takes place offline, and a set of IF…THEN rules containing heart-rate variability (HRV) parameters is achieved. Third, these rules are used in our real-time mobile monitoring system: the same wearable sensor collects the single-channel ECG data and sends them to the same mobile device, which now processes those data online to compute HRV-related parameter values. If these values activate one of the rules found for that patient, an alarm is immediately produced. This approach has been tested on a literature database with 35 OSA patients. A comparison against five well-known classifiers has been carried out.

Automatic fall detection is a major issue in taking care of the health of elderly people. In this task the capability of telling in real time falls from normal daily activities is crucial. To this aim, this paper proposes an approach based on the automatic extraction of knowledge expressed as a set of IF...THEN rules from a database of fall recordings. This set of rules, generated offline, can then be exploited in a real-time mobile monitoring system: data gathered by wearable sensors are processed in real time and, if their values activate some of the rules describing falls, an alarm message is automatically produced. The approach has been compared against other classifiers on a real-world fall database, and its discrimination ability is shown to be higher. Moreover, a test phase for the real-time mobile monitoring system is being carried out over real cases.

Performing detection and real-time monitoring of Obstructive Sleep Apnea (OSA) is a significant healthcare task. An easy, cheap, and mobile approach to monitor patients with OSA is proposed here. It gathers data from a patient by a single-channel ECG, and offline automatically extracts knowledge about that patient as a set of IF...THEN rules containing Heart Rate Variability (HRV) parameters. These rules are then used in the real-time mobile monitoring system: ECG data is collected by a wearable sensor, sent to a mobile device, and processed online to compute HRV-related parameter values. If a rule is activated by those values, the system produces an alarm. A literature database of OSA patients has been used to test the approach.

Clinical Decision Support Systems (CDSSs) are typically based on clinical guidelines explicitly formalized in the form of rules for reproducing the physician's decisionmaking process and, also, improving the efficiency of medical practices. With the aim of building CDSSs able to represent uncertainty existing in clinical guidelines and efficiently reason on a huge number of inter-connected rules, this paper presents a multi-level fuzzy inference system offering the following set of specifically-devised functionalities: (i) fuzzy rules can be organized in one or more groups of positive evidence rules, where each group is able to interact with other ones by properly chaining their conclusions; (ii) rules inside a group are independently processed and evaluated; (iii) each group of rules can be customized by means of a peculiar configuration for the inference; (iv) a fuzzy ELSE rule can be associated to a group for assembling all the negative evidence for a specific situation. A proof of concept scenario is finally given to describe how the proposed solution can be applied.

BACKGROUND: The diagnosis of many diseases can be often formulated as a decision problem; uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of the whole process. Fuzzy logic, a well established attempt at the formalization and mechanization of human capabilities in reasoning and deciding with noisy information, can be profitably used. Recently, we informally proposed a general methodology to automatically build DDSSs on the top of fuzzy knowledge extracted from data. METHODS: We carefully refine and formalize our methodology that includes six stages, where the first three stages work with crisp rules, whereas the last three ones are employed on fuzzy models. Its strength relies on its generality and modularity since it supports the integration of alternative techniques in each of its stages. RESULTS: The methodology is designed and implemented in the form of a modular and portable software architecture according to a component-based approach. The architecture is deeply described and a summary inspection of the main components in terms of UML diagrams is outlined as well. A first implementation of the architecture has been then realized in Java following the object-oriented paradigm and used to instantiate a DDSS example aimed at accurately diagnosing breast masses as a proof of concept. CONCLUSIONS: The results prove the feasibility of the whole methodology implemented in terms of the architecture proposed.

The formalization and manipulation of complex and not yet assessed rules by clinicians are critical for Decision Support Systems (DSSs) performance in supporting remote monitoring of chronic patients. Sometimes, structural anomalies, such as inconsistency and redundancy, can occur. This work presents a novel system, named Consistency Checker, aimed at verifying the reliability of conditionaction clinical rules in knowledge-based DSSs. This system allows the verification of very complex rules having in their antecedent parts not only simple logical conditions, but also arithmetical expressions. Moreover, the Consistency Checker provides a new classification of the detected anomalies, aimed at fully describing the rule verification results, as well as a suitable knowledge representation formalism to encode condition-action rules in a general way. The system has been designed according to the Service Oriented Architecture and implemented as a Web Service within the CHRONIOUS Project.

Fuzzy-based Decision Support Systems (DSSs) have gained increasing importance in medicine, since they rely on a transparent and interpretable rule base. A very attractive feature for these systems is to present their results as a set of plausible conclusions, each of them associated with a degree of possibility. In order to face this need, this work proposes a novel approach consisting in hybridization of possibility theory and a classical fuzzy clustering method, based on a distance metric interpretable in a probabilistic framework with the final aim of determining both fuzzy rules and partitions. As a proof of concept, the method has been applied to a real-life application pertaining the classification of Multiple Sclerosis Lesions. Finally, some sophistications are proposed for a future refinement, in order to improve the quality of results and the generality of applications.

Ubiquitous and pervasive applications may present critical requirements from the point of view of functional correctness, reliability, availability, security, and safety. Unlike traditional safety-critical applications, the behavior of ubiquitous and pervasive applications is affected by the movements and location of users and resources. In this article, we first present emerging formal methods for the description of both entities and their behavior in pervasive computing environments; then, we introduce this special issue. Despite many previous works that have focused on modeling the entities, relatively few have concentrated on modeling or verifying behaviors; and almost none has dealt with combining techniques proposed in these two aspects. The articles accepted in this special issue cover some of the topics aforementioned and constitute a representative sample of the latest development of formal methods in pervasive computing environments.

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.

Ambient Intelligence technologies have not yet been widely adopted in safety critical scenarios. This principally has been due to fact that acceptable degrees of dependability have not been reached for the applications that rely on such technologies. However, the new critical application domains, like Ambient Assisted Living and Smart Hospitals, which are currently emerging, are increasing the need for methodologies and tools that can improve the reliability of the final systems. This paper presents a middleware architecture for safety critical Ambient Intelligence applications which provides the developer with services for runtime verification. It is now possible to continuously monitor and check the running system against correctness properties defined at design time. Moreover, a visual tool which allows the formal design of several of the characteristics of an Ambient Intelligence application and the automatic generation of setting up parameters and code for the middleware infrastructure is also presented.

Recent research agrees on the utility of fuzzy reasoning for the development of Decision Support Systems, which help to classify clinical data. In this context, methods or techniques for representing fuzzy terms in the form of interpretable fuzzy sets obtained from numerical data are strongly required. Typically, in medical settings, statistical data are available or can be obtained from rough data, in the form of probability distributions or likelihood functions. Until now, no theoretical approach was proposed for transforming a probability distribution into a likelihood view fuzzy set. In this paper, a method is developed which generalizes some existing approaches by giving them a theoretical justification. The method enables the construction of normal fuzzy sets, which can be chosen to have a triangular or trapezoidal shape where lateral edges are adapted depending on the input probability distribution. The method was assessed through its application to a simulated normal probability distribution and to real case study pertaining the classification of Multiple Sclerosis lesions.

This article presents a methodology to formally express requirements in safety-critical ubiquitous and pervasive applications in order to achieve a higher degree of dependability. In particular, it will be shown how it is possible to formalize and constrict mobility characteristics by combining and extending several formal methods. The article also discusses some issues concerning both static and dynamic verification.

Technological advances in wireless networks and mobile devices are driving towards a new concept of grid computing, in which mobile users play an active role. For this reason, classical wired grid infrastructures are being extended in order to provide facilities which enable mobile users to exploit services available in the environment. Since grid computing borrows such facilities from pervasive computing, the new computing paradigm is named pervasive grid computing. In this paper, we identify a number of characteristics, like context-awareness and handling of mobile sessions, that all pervasive grid environments should provide. Accordingly, we define an architectural model based on a set of services. A running implementation of such an architecture and an application scenario are presented as well.

Wireless and pervasive healthcare applications typically present critical requirements from the point of view of functional correctness, reliability, availability, security, and safety. In contrast to the case of classic safety critical applications, the behavior of wireless and pervasive applications is affected by the movements and location of users and resources. This article presents a methodology to formally express requirements in safety critical wireless and pervasive healthcare applications in order to achieve a higher degree of dependability. In particular, it will be shown how it is possible to formalize and constrict mobility characteristics by combining, and in some cases extending, several formal methods. The article also describes a rigorous specification process. Finally, it concludes with a case study of a real safety critical pervasive healthcare application that is going to be deployed in a city hospital.

Wireless and pervasive applications typically present critical requirements from the point of view of functional correctness, reliability, availability, security and safety. In contrast to the case of classic safety critical applications, the behavior of such applications is affected by the movements and location of users and resources. This paper presents some formal tools that enable designers to specify the software requirements of a pervasive application in an unambiguous and verifiable way. A real safety critical pervasive application for the department of nuclear medicine of a city hospital is also presented as a case study.

Immersive Virtual Reality environments are suitable to support activities related to medicine and medical practice. The immersive visualization of information-rich 3D objects, coming from patient scanned data, provides clinicians with a clear perception of depth and shapes. However, to benefit from immersive visualization in medical imaging, where inspection and manipulation of volumetric data are fundamental tasks, medical experts have to be able to act in the virtual environment by exploiting their real life abilities. In order to reach this goal, it is necessary to take into account user skills and needs so as to design and implement usable and accessible human-computer interaction interfaces. In this paper we present a natural interface for a semi-immersive virtual environment. Such interface is based on an off-the-shelf handheld wireless device and a speech recognition component, and provides clinicians with intuitive interaction modes for inspecting volumetric medical data.

In many fields of medicine interactive virtual environments can offer enhanced visualization and manipulation of three-dimensional objects, reconstructed from high-quality scans of human organs. Stereoscopic systems provide users with a natural depth perception about the spatial nature of the structures of interest; moreover advanced user-friendly interfaces, by allowing a natural and intuitive interaction, can strengthen the feeling of being immersed, so to offer clinicians the possibility to act how they do in the real life. In order to enhance the sense of realism specially in medical computer-assisted education, training and diagnostic fields, it is necessary to have a system in which every action can be executed directly into the 3D world without switching to a 2D visualization mode. In this paper we present new interaction techniques to select and extract a volume-of-interest (VOI) in a semi-immersive interactive environment, by using a user-friendly wireless interface, suitable to implement pointing and manipulation features with 6 DOF.

Smart and intelligent environments can proficiently benefit from a new generation of multimodal dialog systems, which enable users to interact in natural ways like voice, gesture, facial motions, and so on. However, in order to make this possible, it is necessary to integrate such systems and services, possibly in a transparent way. In the present paper, we propose (i) an architectural model for multimodal smart environments; (ii) an agent-based middleware infrastructure that provides basic components for the integration of multimodal interaction systems; and, (iii) a tool for rapid-prototyping, which automatically customizes some middleware components to implement multimodal interaction mechanisms for the environment's application services.

Grid computing and pervasive computing have rapidly emerged and affirmed respectively as the paradigm for high performance computing and the paradigm for user-friendly computing. The conjunction of such paradigms are now generating a new one, the pervasive grid computing, which aims at extending classic grids with characteristics of pervasive computing like spontaneous and transparent integration of mobile devices, context-awareness, pro-activity, and so on. In this paper, we present a software infrastructure for integrating mobile devices as active resources in a grid. In particular, it has been developed a service able to dynamically distribute and execute user's tasks on a grid of mobile and fixed devices. Mobile devices participate to the grid in a way completely transparent to their owners, hence the computational power is given without any configuration operation and in a reliable way. As well, users willing of executing their applications directly submit their code without caring of choosing and allocating resources of the grid

This paper presents some redundancy-based transmission strategies, suitable for MPEG (Moving Picture Experts Group) coded multimedia streams. We focus on version 2 of the standard, which is a major compression scheme for distributed VoD (Video on Demand) systems. In such systems, multimedia data transmitted from a server to a client, is to be delivered and rendered within pre-defined time constraints. Typical values range between 100 to 200 ms. Due to these constraints, developing effective redundancy and transmission mechanisms is not a simple task. The effectiveness of the suggested schemes is evaluated, under varying fault rates and network load conditions. The evaluation is done via simulated fault-injection, i.e. by simulating the behavior of the system while faults are injected to its individual components. In particular, we concentrated on the impact-on the quality of the delivered service-of transient faults in the communication infrastructure.

Abstract The paper deals with the implementation of global time in multicomputer systems. After a formalization of the synchronization problem, techniques to estimate the synchronization delay and to compensate the drift error are proposed. Then SYNC_WAVE, a clock synchronization algorithm where the values of a reference clock are diffused in a wave‐like manner, is described. SYNC_WAVE has no provision for fault‐tolerance and is specially designed to introduce low CPU and communication overhead, in order to support performance analysis applications efficiently. An implementation of the devised algorithm in a transputer‐based system is presented, showing the accuracy results obtained. Finally SYNC_WAVE is compared to other synchronization algorithms and several of its possible applications are suggested.