Cluster-Then-Classify Methodology for the Identification of Pain Episodes in Chronic Diseases.

Chronic diseases benefit of the advances on personalize medicine coming out of the integrative convergence of significant developments in systems biology, the Internet of Things and Artificial Intelligence. 70% to 80% of all healthcare costs in the EU and US are currently spent on chronic diseases, leading to estimated costs of C=700 billion and $3.5 trillion respectively. The management of symptomatic pain crises in chronic diseases is based on general clinical guidelines that do not take into account the singularities of the crises, such as their intensity or duration, so that the pain of those particular crises may cause the medication to be ineffective and lead the patient to overmedication. Knowing in detail the characteristics of the pain would help the physician to objectively prescribe personalized treatments for each patient and crisis. In this manuscript, we make a step further on the prediction of symptomatic crisis from ambulatory collected data in chronic diseases. We propose a categorization of pain types according to subjective symptoms of real patients. Our approach has been evaluated in the migraine disease. The migraine is one of the most disabling neurological diseases that affects over 12% of the population worldwide and leads to high economic costs for private and public health systems. This study aims to classify pain episodes by the characterization of pain curves reported by patients in real time. Pain curves have been described as a set of morphological features. With these features the pain episodes are clustered then classified by unsupervised and supervised machine learning models. It is shown that the evolution of different pain episodes in chronic diseases can be modeled and clustered. Over a population of 51 migraine patients, it has been found that there are 4 clusters of pain types that can be classified using 4 morphological features with an accuracy of 99.0% using a Logistic Model Tree algorithm.

Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises.

Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data.

Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives.

2013 Up-date of the consensus statement of the Spanish Menopause Society on postmenopausal osteoporosis.

Postmenopausal osteoporosis is a major female health problem that increases morbidity, mortality and healthcare system costs. Considering that gynecologists are the primary health practitioners involved in the treatment of women with osteoporosis in our country, a panel of experts from the Spanish Menopause Society met to establish a set of criteria and procedures for the diagnosis and treatment of this disease based on the best available evidence and according to the model proposed by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system to elaborate clinical practice guidelines and to classify the quality of the evidence and the strength of the recommendations. These recommendations should be a reference to gynecologist and other health professionals involved in the treatment of postmenopausal women.

Ubiquitous green computing techniques for high demand applications in Smart environments.

Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

Detecting unknown attacks in wireless sensor networks that contain mobile nodes.

As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

Compiler optimizations as a countermeasure against side-channel analysis in MSP430-based devices.

Ambient Intelligence (AmI) requires devices everywhere, dynamic and massively distributed networks of low-cost nodes that, among other data, manage private information or control restricted operations. MSP430, a 16-bit microcontroller, is used in WSN platforms, as the TelosB. Physical access to devices cannot be restricted, so attackers consider them a target of their malicious attacks in order to obtain access to the network. Side-channel analysis (SCA) easily exploits leakages from the execution of encryption algorithms that are dependent on critical data to guess the key value. In this paper we present an evaluation framework that facilitates the analysis of the effects of compiler and backend optimizations on the resistance against statistical SCA. We propose an optimization-based software countermeasure that can be used in current low-cost devices to radically increase resistance against statistical SCA, analyzed with the new framework.

Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps.

The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.

Using reputation systems and non-deterministic routing to secure wireless sensor networks.

Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios.

Transculturally adapted Spanish SRI questionnaire for home mechanically ventilated patients was viable, valid, and reliable.

OBJECTIVE: To validate the Spanish Severe Respiratory Insufficiency (SRI) questionnaire, the first health-related quality-of-life questionnaire specific for patients receiving home mechanical ventilation (HMV). STUDY DESIGN AND SETTING: This multicenter prospective study enrolled 115 patients (53 males, age 62+/-13 years) receiving HMV, recruited from five hospitals. Patients were scheduled for two visits during which sociodemographic and clinical data were recorded, and both the Spanish SRI and the SF-36 questionnaires were administered. Viability was assessed by recording timing and the response rate in the questionnaire. Reliability was assessed using intraclass correlation coefficient (ICC) and Cronbach alpha coefficient. Validity was studied by factor analysis, by a correlation test between the SRI and SF-36 questionnaires, and by establishing several simple, plausible, ad hoc hypotheses. RESULTS: The SRI was administered in 10+/-5 minutes with >or=96% responses for most items. Cronbach alpha coefficient was >0.7 for all scales except social relationships. ICCs were above 0.8 for all scales. Criterion validity obtained high correlations with SF-36, especially in psychosocial well-being and physical functioning scales. Factor analysis explained 60% of the variability. All ad hoc hypotheses were fulfilled. CONCLUSION: The Spanish version of the SRI questionnaire has good psychometric properties, similar to those of the original questionnaire.

Factors related to quality of life in patients receiving home mechanical ventilation.

OBJECTIVES: The objective of this study was to evaluate factors associated with health-related quality of life (HRQOL) among patients receiving home mechanical ventilation (HMV). METHODS: Observational, cross-sectional study. Patients receiving HMV were recruited from hospital outpatients facilities at five participating centers; a single follow-up visit was scheduled. The Spanish version of the Severe Respiratory Insufficiency (SRI) Questionnaire was used and the following variables were collected: socio-demographic status, previous medical history (Charlson-Age Comorbidity Index), current symptoms, administration of questionnaires, pulmonary function tests (PFT), current ventilatory support, and oxygen therapy. RESULTS: One hundred and fifteen patients (57 males and 58 females, mean age 62+/-13 years) were investigated. The reasons for HMV were as follows: thoracic cage abnormalities (33 patients), obesity hypoventilation syndrome (37 patients), neuromuscular disorders (18 patients), sequelae of tuberculosis (12 patients), and chronic obstructive pulmonary disease (15 patients). In a bivariate approach, dyspnea, the number of hospitalizations, and the number of emergency room admissions in last year were the main predictors of each HRQOL dimension. Multivariate analysis showed that dyspnea, FEV(1)/FVC, and the number of hospitalizations in the previous year were independently associated with HRQOL. CONCLUSIONS: HRQOL of patients receiving HMV is influenced by many factors, especially by dyspnea and the number of admissions. An obstructive pattern in the PFT also influences HRQOL. These findings may have therapeutic implications.

Guidelines for the treatment of community-acquired pneumonia: predictors of adherence and outcome.

RATIONALE: Some studies highlight the association of better clinical responses with adherence to guidelines for empiric treatment of community-acquired pneumonia (CAP), but little is known about factors that influence this adherence. OBJECTIVES: Our objectives were to identify factors influencing adherence to the guidelines for empiric treatment of CAP, and to evaluate the impact of adherence on outcome. METHODS: We studied 1,288 patients with CAP admitted to 13 Spanish hospitals. Collected variables included the patients' clinical and demographic data, initial severity of the disease, antibiotic treatment, and specialty and training status of the prescribing physician. MEASUREMENTS AND MAIN RESULTS: Adherence to guidelines was high (79.7%), with significant differences between hospitals (range, 47-97%) and physicians (pneumologists, 81%; pneumology residents, 84%; nonpneumology residents, 82%; other specialists, 67%). The independent factors related to higher adherence were hospital, physician characteristics, and initial high-risk class of Fine, whereas admission to intensive care unit decreased adherence. Seventy-four patients died (6.1%), and treatment failure was found in 175 patients (14.2%). After adjusting for Fine risk class, adherence to the guidelines was found protective for mortality (odds ratio [OR], 0.55; 95% confidence interval [CI], 0.3-0.9) and for treatment failure (OR, 0.65; 95% CI, 0.5-0.9). Treatment prescribed by pneumologists and residents was associated with lower treatment failure (OR, 0.6; 95% CI, 0.4-0.9). CONCLUSIONS: Adherence to guidelines mainly depends on the hospital and the specialty and training status of prescribing physicians. Nonadherence was higher in nonpneumology specialists, and is an independent risk factor for treatment failure and mortality.

Reaching stability in community-acquired pneumonia: the effects of the severity of disease, treatment, and the characteristics of patients.

BACKGROUND: The natural history of the resolution of infectious parameters in patients with community-acquired pneumonia (CAP) is not completely known. The aim of our study was to identify those factors related to host characteristics, the severity of pneumonia, and treatment that influence clinical stability. METHODS: In a prospective, multicenter, observational study, we observed 1424 patients with CAP who were admitted to 15 Spanish hospitals. The main outcome variable was the number of days needed to reach clinical stability (defined as a temperature of or=90 mm Hg, and oxygen saturation >or=90% or arterial oxygen partial pressure of >or=60 mm Hg). RESULTS: The median time to stability was 4 days. A Cox proportional hazard model identified 6 independent variables recorded during the first 24 h after hospital admission related to the time needed to reach stability: dyspnea (hazard ratio [HR], 0.76), confusion (HR, 0.66), pleural effusion (HR, 0.67), multilobed CAP (HR, 0.72), high pneumonia severity index (HR, 0.73), and adherence to the Spanish guidelines for treatment of CAP (HR, 1.22). A second Cox model was performed that included complications and response to treatment. This model identified the following 10 independent variables: chronic bronchitis (HR, 0.81), dyspnea (HR, 0.79), confusion (HR, 0.61), multilobed CAP (HR, 0.84), initial severity of disease (HR, 0.73), treatment failure (HR, 0.31), cardiac complications (HR, 0.66), respiratory complications (HR, 0.77), empyema (HR, 0.57), and admission to the intensive care unit (HR, 0.57). CONCLUSIONS: Some characteristics of CAP are useful at the time of hospital admission to identify patients who will need a longer hospital stay to reach clinical stability. Empirical treatment that follows guidelines is associated with earlier clinical stability. Complications and treatment failure delay clinical stability.