Adaptive and personalized user behavior modeling in complex event processing platforms for remote health monitoring systems

Taking care of people who need constant care is essential and its cost is rising every day. Many intelligent remote health monitoring systems have been developed from the past till now. Intelligent systems explainability has become a necessity after the worldwide adoption of such systems, especially in the health domain to explain and justify decisions made by intelligent systems. Rule-based techniques are among the best in terms of explainability. However, there are several challenges associated with remote health monitoring systems in general and rule-based techniques, specifically. In this research, an adaptive platform based on Complex Event Processing (CEP) has been proposed for user behavior modeling to provide adaptive and personalized remote health monitoring. This system can manage a massive amount of data in real-time utilizing the CEP engine. It can also avoid human errors in setting rules thresholds by extracting thresholds from previous data using JRip rule-based classifier. Moreover, a feature selection method is proposed to decrease the high number of features while maintaining accuracy. Additionally, a rule adaption method has been proposed to cope with changes over time. Additionally, a personalized rule adaption method is proposed to address the need for responsiveness of the system to the special requirements of each user. The experimental results on both hospital and activity data sets showed that the proposed rule adaption method improves the accuracy by about 15 % compared to non-adaptive systems. Additionally, the proposed personalized rule adaption method has an accuracy improvement of about 3 % to 6 % on both mentioned datasets

Intelligent rule extraction in complex event processing platform for health monitoring systems

Taking care of people with disabilities is essential in any situation and its cost is increasing every day. Many intelligent remote health monitoring systems have been developed from the past till now. These systems face challenges such as managing large volumes of data, real-time processing, and rule setting human errors. In this research, a complex event processing (CEP)-based platform is proposed for remote health monitoring and user behavior modeling in a health system. This platform can manage massive amounts of data in real-time. In addition, it can solve human errors in rule setting by extracting rules from the previous data using a rule-based learning method. Moreover, rule-based learning is an explainable method to get feedback from the domain experts. Experimental results in the hospital dataset showed that the PART rule-based learning has better accuracy (about 98.61%) in classifying the health conditions of the patients in comparison to other rule-based methods. On the other hand, the JRip rule-based learning has produced 16 fewer rules with similar accuracy (about 97.32%) compared to the PART classification method. Therefore, the JRip classifier is a better option for generating a small set of rules for using in the CEP engine

Prevalence of hearing loss among high risk newborns hospitalized in hospitals affiliated to Tehran University of Medical Sciences

"n Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi;} Background: American pediatric Association proposes to screen all neonates with Oto-Acoustic Emission (OAE). In developing countries, because of several limitations, health policy makers recommend to screen only in high risk patients. This study is performed with the aim to screen hearing loss in 950 high risk newborns hospitalized in hospitals affiliated to Tehran University using the OAE test."n"nMethods: A total of 950 neonates hospitalized in the Neonatal and NICU wards of Vali-e-Asr, Shariati, Medical Center and Bahrami Hospitals during the years 2004-2006 who showed at least one risk factor using TEOAE hearing test were enrolled into this cross-sectional descriptive analytical study and were diagnosed with mild deafness and total deafness. Blood exchange due to hyperbillirubinemia, septicemia, congenital heart disease, the fifth minute apgar scores below six, PROM more than six hours, epilepsia, need to NICU more than five hours, pneumonia and Oto-Toxic drugs were considered as risk factors. Data was past medical history, current disease, admission cause, sign & symptoms and complications of disease."n"nResults: Multivariate logistic regression and paired t-test showed that blood exchange, low birth weight and low first minute Apgar scores had the highest independent risk for hearing loss among newborn."n"nConclusion: Despite of the low prevalence of neonatal hearing loss, screening of hearing loss at early stages is important