An intelligent recommender system based on short-term disease risk prediction for patients with chronic diseases in a telehealth environment

Clinical decisions are usually made based on the practitioners' experiences with limited support from data-centric analytic processes from medical databases. This often leads to undesirable biases, human errors and high medical costs affecting the quality of services provided to patients. Recently, the use of intelligent technologies in clinical decision making in the telehealth environment has begun to play a vital role in improving the quality of patients' lives and reducing the costs and workload involved in their daily healthcare. In the telehealth environment, patients suffering from chronic diseases such as heart disease or diabetes have to take various medical tests such as measuring blood pressure, blood sugar and blood oxygen, etc. This practice adversely affects the overall convenience and quality of their everyday living. In this PhD thesis, an effective recommender system is proposed utilizing a set of innovative disease risk prediction algorithms and models for short-term disease risk prediction to provide chronic disease patients with appropriate recommendations regarding the need to take a medical test on the coming day. The input sequence of sliding windows based on the patient's time series data, is analyzed in both the time domain and the frequency domain. The time series medical data obtained for each chronicle disease patient is partitioned into consecutive sliding windows for analysis in both the time and the frequency domains. The available time series data are readily available in time domains which can be used for analysis without any further conversion. For data analysis in the frequency domain, Fast Fourier Transformation (FFT) and Dual-Tree Complex Wavelet Transformation (DTCWT) are applied to convert the data into the frequency domain and extract the frequency information. In the time domain, four innovative predictive algorithms, Basic Heuristic Algorithm (BHA), Regression-Based Algorithm (RBA) and Hybrid Algorithm (HA) as well as a structural graph-based method (SG), are proposed to study the time series data for producing recommendations. While, in the frequency domain, three predictive classifiers, Artificial Neural Network, Least Squares-Support Vector Machine, and Naïve Bayes, are used to produce the recommendations. An ensemble machine learning model is utilized to combine all the used predictive models and algorithms in both the time and frequency domains to produce the final recommendation. Two real-life telehealth datasets collected from chronic disease patients (i.e., heart disease and diabetes patients) are utilized for a comprehensive experimental evaluation in this study. The results show that the proposed system is effective in analysing time series medical data and providing accurate and reliable (very low risk) recommendations to patients suffering from chronic diseases such as heart disease and diabetes. This research work will help provide high-quality evidence-based intelligent decision support to clinical disease patients that significantly reduces workload associated with medical checkups would otherwise have to be conducted every day in a telehealth environment

IRS-HD: an intelligent personalized recommender system for heart disease patients in a tele-health environment

The use of intelligent technologies in clinical decision making support may play a promising role in improving the quality of heart disease patients’ life and helping to reduce cost and workload involved in their daily health care in a tele-health environment. The objective of this demo proposal is to demonstrate an intelligent prediction system we developed, called IRS-HD, that accurately advises patients with heart diseases concerning whether they need to take the body test today or not based on the analysis of their medical data during the past a few days. Easy-to-use user friendly interfaces are developed for users to supply necessary inputs to the system and receive recommendations from the system. IRS-HD yields satisfactory recommendation accuracy, offers a promising way for reducing the risk of incorrect recommendations, as well saves the workload for patients to conduct body tests every day

Effects of environmental parameters on diatoms community of the Euphrates River system

A study was designed to (1) establish the taxonomy of diatom species in the Euphrates River, and (2) determine the effect of the main environmental factors on diatom community distribution in the Euphrates River. From 14 sites along part of the Euphrates River, samples of diatoms and water were taken during 2016. Diatom samples were collected from the water by phytoplankton nets at a randomly selected site. A total of 96 diatom species were recorded during the study period. Using correlation factor analysis, patterns of diatom species distributions in connection to environmental variables were discovered. Temperature, total suspended solids, total alkalinity, and phosphate (PO4) were all significantly and strongly linked with diatom species in both habitats (r = 0.85, 0.88, 0.92, and 0.83, respectively). Fragilaria crotonensis Kitton 1869 had a higher total number recorded (881.64 cells/l×103) during the study period, and site 2 had a higher total number compared with other sites (4845 cells/l×103). November had a higher total number recorded compared with other months (13722.64 cells/l×103). As a result, we concluded that in lotic systems, environmental conditions can affect the existence and distribution of diatoms

An intelligent recommender system based on short-term risk prediction for heart disease patients

In this paper, an intelligent recommender system is developed, which uses an innovative time series prediction algorithm to provide recommendations to heart disease patients in the tele-health environment. Based on analytics of each patient’s medical tests in records, the system provides the patient with decision support for necessity of medical tests. The experimental results show that the proposed system yields satisfactory accuracy in recommendations. The system also offers a promising way for saving the workload for patients and healthcare practitioners in conducting daily medical tests. The research will help reduce the workload and cost in healthcare and help the healthcare industry transform from the traditional scenario to more a personalized paradigm in a tele-health environment

An intelligent recommender system based on predictive analysis in telehealthcare environment

The use of intelligent technologies for providing useful recommendations to patients suffering chronic diseases may play a positive role in improving the general life quality of patients and help reduce the workload and cost involved in their daily healthcare. The objective of this study is to develop an intelligent recommender system based on predictive analysis for advising patients in the telehealth environment concerning whether they need to take the body test one day in advance by analyzing medical measurements of a patient for the past k days. The proposed algorithms supporting the recommender system have been validated using a time series telehealth data recorded from heart disease patients which were collected from May to January 2012, from our industry collaborator Tunstall. The experimental results show that the proposed system yields satisfactory recommendation accuracy and offer a promising way for saving the workload for patients to conduct body tests every day. This study highlights the possible usefulness of the computerized analysis of time series telehealth data in providing appropriate recommendations to patients suffering chronic diseases such as heart diseases patients

Geotechnical Properties and Slopes Stability for Banks Soils in North Part of Shatt Al-Hilla at Meandering Sites, Babylon Governorate / Central of Iraq

            تعاني ضفاف نهر الحلة  في المناطق الواقعة في الجزء الشمالي من محافظة بابل من مشاكل هندسية نتيجة لتأثير جريان نهر الحلة الذي يعمل على  تأكل (تعرية) من جانب والترسيب على الجانب الاخر وبالتالي يتشكل الالتواء النهرية. بعد الزيارة الاستطلاعية لمنطقة الدراسة تم تحديد مواقع الالتواء ، حيث اجريت دراسة تفصيلية للمنطقة عن طريق حفر 6 ابار اختبارية تمثل ثلاثة مناطق ( السدة، المحاويل، وسنجار) بواقع بئرين على جانبي التعرج وبعمق 10م لكل بئر ، لتحديد خواص التربة وقدرتها على التحمل واتماسك ومقاومة اجهادات القص الناتجة من طاقة تدفق النهر. طرق العمل:  تم جمع عينات التربة  لأجراء الاختبارات الفيزيائية  التالية ( اختبار المحتوى الرطوبي، الوزن النوعي ، حدود اتربيرك، التحليل المنخلي، والكثافة). كما تم اجراء الفحوصات الهندسية والتي شملت ( اختبار النفاذية، مقاومة الانضغاط الاحادي المحور والثلاثي المحور، فحص القص المباشر، اختبار الانضمام وقابلية التحمل). كما تم اجراء مجموعة من الاختبارات الكيميائية وهي ( PH، الاملاح الذائبة الكلية ، نسبة الكبريتات ، فحص الكلوريد، نسبة الجبس، ونسبة المواد العضوية). كما اجريت دراسة لثبات منحدرات البنوك للمحطات الثلاث ، حيث تم رصد المقطع العرضي بواسطة جهاز M9 ، وكذلك ارتفاع الضفاف من الجانبين بواسطة جهاز (LEVEL)  ، وتم الربط بينهما بواسطة جهاز Geo-Studio 2021 بعد التعرف على خصائص التربة وباستخدام (Bishop method) الاستنتاجات:  تم التعرف على عامل الامان للمحطات الثلاثة للمناطق المعرضة للتعرية فكانت اعلى نتيجة لعامل الامان في محطة سنجار (2.5)  ومن ثم ضفاف السدة بقيمة (2.3) واقل قيمة محطة المحاويل والبالغة (1.4). في الظروف الطبيعية ومتوسط منسوب جريان يصله بالسنة (28.7 م) فوق سطح البحر . حيث كانت جميع المحطات امنة مالم يزداد او ينخفض منسوب المياه كذلك توصل البحث تحديد القابلية القصوى للأحمال التي تتحملها الضفة قبل الانهيار .Background: The northern regions of the Babylon Governorate, located on the Hilla River, suffering  from problems as the collapse and erosion of its banks, as well as the increase in sedimentation in the river, which reduces the River flow efficiency; due to the effect of the velocity river flow, which works on eroding one side of the river and sedimentation on the other side, forming meanders in the river. Materials and Methods: After the reconnaissance visit to determine the meandering sites.  A detailed study of the area was carried out by drilling (6) test boreholes representing three areas( Al-Sadda, Al-Mahaweel ,and Sinjar),  two wells on both sides of the meander and at a depth of (10 m), for each borehole to observe the effect of the river flow velocity on the stability of slopes and to know the variation in soil properties, its bearing capacity and the consolidation on both sides of the river, and thus its effect on the engineering construction . Soil samples have been taken  to  carry out physical tests including: (moisture content test , specific gravity test , Atterberg limits tests , sieve analysis and hydrometer tests) ,also carried out engineering tests including: (permeability test , unconfined  & triaxial compressive strength test , direct shear test , consolidation test and bearing capacity) , soil chemical tests have been perform also which are (pH , total soluble salt , sulfate test , chloride test , gypsum test and organic matter test) . A study was also conducted for the stability of the banks slopes for the three stations, where the cross-section was monitored by the M9 device, as well as the height of the banks from both sides by the (LEVEL) device, Using the Geo-Studio 2021 program with soil properties and by using (Bishop), method the safety factor was extracted for the three stations For erosion-prone areas. Conclusion:  The highest result of the safety factor was in Sinjar Station (2.5), then the banks of Al-Sadda Station, with a value of (2.3), and the lowest value of Al-Mahaweel Station, which amounted to (1.4). Under natural conditions, the average river flow level reaches it in year is (28.7 m) above sea level. Where all the stations were (safe), unless the water level increased or decreased. Also, the research reached a determination of the allowable bearing capacity  The soil of the banks reaches it before the landslide.     &nbsp

Sleep EEG signal analysis based on correlation graph similarity coupled with an ensemble extreme machine learning algorithm

Background: Sleep plays an essential role in repairing and healing human mental and physical health. Developing an efficient method for scoring electroencephalogram (EEG) sleep stages is expected to help medical specialists in the early diagnosis of sleep disorders. Method: In this paper, a novel technique is proposed for classifying sleep stages EEG signals using correlation graphs. First, each 30 seconds EEG segment is divided into a set of sub-segments. The dimensionality of each sub-segment is reduced by using a statistical model. Second, each EEG segment is transferred into a graph considering each sub-segment as a node in a graph, and a link between each pair of nodes is calculated based on their correlation coefficient. Graph’s modularity is used as input features into an ensemble classifier. Results: Different community detection algorithm based correlation graph are investigated to discern the most effective features to reveal the differences between EEG sleep stages. A combination of various classification techniques: a least square vector machine (LS-SVM), k-means, Naïve Bayes, Fuzzy C-means, k-nearest, and logistic regression are tested using multi criteria decision making (MCDM) to design an ensemble classifier. Based on the results of the MCDM, the best four: LS-SVM, Naïve Bayes, logistic regression and k-nearest are integrated, to finally utilise as an ensemble classifier to categorise the graph’s characteristics. The results obtained from the ensemble classifier are compared with those from the individual classifiers. The performance of the proposed method is compared with state of the art of sleep stages classification. The experimental results showed that the EEG sleep classification based on correlation graphs are able to achieve better recognition results than the existing state of the art techniques

Evaluation of factors effect on infection control and safety measures among health care providers at hemodialysis unit in government and private: Comparative study

Background:- Illness control policies and practices are employed in hospitals and other human and animal health care facilities to limit the risk of infection spread. Infection control is a term that is commonly used in the health-care profession and is an important aspect of it. It is responsible for all aspects of health, welfare, and staff safety. In the early 1950s, infection control became a formal institution in the United States. A small number of hospitals had begun to detect healthcare-associated infections (HAIs) and implement infection-control techniques by the late 1950s and early 1960s. Objective:- To investigate of Factors effect on infection control and safety measures among health care providers and to compare Factors effect with related to infection control and safety measures among health care providers at hemodialysis unit in government and private, and to discover the relationship between characetristics of demographic with infection control and safety measures among health care providers. Method of study:ــ The research was conducted out using a comparative study design. These study to investigate of Factors effect on infection control and safety measures among health care providers

A general extensible learning approach for multi-disease recommendations in a telehealth environment

In a telehealth environment, intelligent technologies are rapidly evolving toward improving the quality of patients’ lives and providing better clinical decision-making especially those who suffer from chronic diseases and require continuous monitoring and chronic-related medical measurements. A short-term disease risk prediction is a challenging task but is a great importance for teleheath care systems to provide accurate and reliable recommendations to patients. In this work, a general extensible learning approach for multi-disease recommendations is proposed to provide accurate recommendations for patients with chronic diseases in a telehealth environment. This approach generates appropriate recommendations for patients suffering from chronic diseases such as heart failure and diabetes about the need to take a medical test or not on the coming day based on the analysis of their medical data. The statistical features extracted from the sub-bands obtained after a four-level decomposition of the patient's time series data are classified using a machine learning ensemble model. A combination of three classifiers – Least Squares-Support Vector Machine, Artificial Neural Network, and Naive Bayes – are utilized to construct the bagging-based ensemble model used to produce the final recommendations for patients. Two real-life datasets collected from chronic heart and diabetes disease patients are used for experimentations and evaluation. The experimental results show that the proposed approach yields a very good recommendation accuracy and offers an effective way to reduce the risk of incorrect recommendations as well as reduces the workload for chronic diseases patients who undergo body tests most days. Thus, the proposed approach is considered one of a promising tool for analyzing time series medical data of multi diseases and providing accurate and reliable recommendations to patients suffering from different types of chronic diseases

Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment

Recently, the use of intelligent technologies in clinical decision making in the telehealth environment has begun to play a vital role in improving the quality of patients' lives and helping reduce the costs and workload involved in their daily healthcare. In this paper, an effective medical recommendation system that uses a fast Fourier transformation-coupled machine learning ensemble model is proposed for short-term disease risk prediction to provide chronic heart disease patients with appropriate recommendations about the need to take a medical test or not on the coming day based on analysing their medical data. The input sequence of sliding windows based on the patient's time series data are decomposed by using the fast Fourier transformation in order to extract the frequency information. A bagging-based ensemble model is utilized to predict the patient's condition one day in advance for producing the final recommendation. A combination of three classifiers -- Artificial Neural Network, Least Squares-Support Vector Machine, and Naive Bayes -- are used to construct an ensemble framework. A real-life time series telehealth data collected from chronic heart disease patients is utilized for experimental evaluation. The experimental results show that the proposed system yields a very good recommendation accuracy and offers an effective way to reduce the risk of incorrect recommendations as well as reduce the workload for heart disease patients in conducting body tests every day. The results conclusively ascertain that the proposed system is a promising tool for analyzing time series medical data and providing accurate and reliable recommendations to patients suffering from chronic heart diseases