Machine learning has become a powerful tool for analysing medical domains, assessing the importance of clinical parameters, and extracting medical knowledge for outcomes research. In this paper, we present a machine learning method for extracting diagnostic and prognostic thresholds, based on a symbolic classification algorithm called REMED.We evaluated the performance of our method by determining new prognostic thresholds for well-known and potential cardiovascular risk factors that are used to support medical decisions in the prognosis of fatal cardiovascular diseases. Our approach predicted 36% of cardiovascular deaths with 80% specificity and 75% general accuracy. The new method provides an innovative approach that might be useful to support decisions about medical diagnoses and prognoses.

Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.

The ARVmobile v1.0 is a multiplatform mobile personal health monitor (PHM) application for ambulatory blood pressure (ABP) monitoring that has the potential to aid in the acquisition and analysis of detailed profile of ABP and heart rate (HR), improve the early detection and intervention of hypertension, and detect potential abnormal BP and HR levels for timely medical feedback.The PHM systemconsisted of ABP sensor to detect BP and HR signals and smartphone as receiver to collect the transmitted digital data and process them to provide immediate personalized information to the user. Android and Blackberry platforms were developed to detect and alert of potential abnormal values, offer friendly graphical user interface for elderly people, and provide feedback to professional healthcare providers via e-mail. ABP data were obtained from twenty-one healthy individuals (>51 years) to test the utility of the PHM application.The ARVmobile v1.0 was able to reliably receive and process the ABP readings from the volunteers. The preliminary results demonstrate that the ARVmobile 1.0 application could be used to performa detailed profile of ABP and HR in an ordinary daily life environment, bedsides of estimating potential diagnostic thresholds of abnormal BP variability measured as average real variability.

Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.

BACKGROUND Average real variability (ARV) is a recently proposed index for shortterm blood pressure (BP) variability. We aimed to determine the minimum number of BP readings required to compute ARV without loss of prognostic information. METHODS ARV was calculated from a discovery dataset that included 24-hour ambulatory BP measurements for 1,254 residents (mean age!=! 56.6! years; 43.5% women) of Copenhagen, Denmark. Concordance between ARV from full (%80 BP readings) and randomly reduced 24-hour BP recordings was examined, as was prognostic accuracy. A! test dataset that included 5,353 subjects (mean age!=! 54.0!years; 45.6% women) with at least 48 BP measurements from 11 randomly recruited population cohorts was used to validate the results. RESULTS In the discovery dataset, a minimum of 48 BP readings allowed an accurate assessment of the association between cardiovascular risk and ARV. In the test dataset, over 10.2!years (median), 806 participants died (335 cardiovascular deaths, 206 cardiac deaths) and 696 experienced a major fatal or nonfatal cardiovascular event. Standardized multivariable-adjusted hazard ratios (HRs) were computed for associations between outcome and BP variability. Higher diastolic ARV in 24-hour ambulatory BP recordings predicted (P!<!0.01) total (HR!=!1.12), cardiovascular (HR!=!1.19), and cardiac (HR!=!1.19) mortality and fatal combined with nonfatal cerebrovascular events (HR!=!1.16). Higher systolic ARV in 24-hour ambulatory BP recordings predicted (P!<!0.01) total (HR!=!1.12), cardiovascular (HR!=!1.17), and cardiac (HR!=!1.24) mortality. CONCLUSIONS Forty-eight BP readings over 24 hours were observed to be adequate to compute ARV without meaningful loss of prognostic information.

Background-—Although 24-hour blood pressure (BP) variability (BPV) is predictive of cardiovascular outcomes independent of absolute BP levels, it is not regularly assessed in clinical practice. One possible limitation to routine BPV assessment is the lack of standardized methods for accurately estimating 24-hour BPV. We conducted a systematic review to assess the predictive power of reported BPV indexes to address appropriate quantification of 24-hour BPV, including the average real variability (ARV) index. Methods and Results-—Studies chosen for review were those that presented data for 24-hour BPV in adults from meta-analysis, longitudinal or cross-sectional design, and examined BPV in terms of the following issues: (1) methods used to calculate and evaluate ARV; (2) assessment of 24-hour BPV determined using noninvasive ambulatory BP monitoring; (3) multivariate analysis adjusted for covariates, including some measure of BP; (4) association of 24-hour BPV with subclinical organ damage; and (5) the predictive value of 24-hour BPV on target organ damage and rate of cardiovascular events. Of the 19 assessed studies, 17 reported significant associations between high ARV and the presence and progression of subclinical organ damage, as well as the incidence of hard end points, such as cardiovascular events. In all these cases, ARV remained a significant independent predictor (P<0.05) after adjustment for BP and other clinical factors. In addition, increased ARV in systolic BP was associated with risk of all cardiovascular events (hazard ratio, 1.18; 95% confidence interval, 1.09–1.27). Only 2 cross-sectional studies did not find that high ARV was a significant risk factor. Conclusions-—Current evidence suggests that ARV index adds significant prognostic information to 24-hour ambulatory BP monitoring and is a useful approach for studying the clinical value of BPV.