Tele-Health Monitoring of Patient Wellness

2016 ◽  
Vol 25 (4) ◽  
pp. 515-528 ◽  
Author(s):  
Ross Stewart Sparks ◽  
Chris Okugami
Keyword(s):  
Machine Learning ◽  
Vital Sign ◽  
Vital Signs ◽  
Regression Tree ◽  
Chronically Ill ◽  
Well Being ◽  
Machine Learning Techniques ◽  
Care Plans ◽  
Tree Methods ◽  
Chronically Ill Patients

AbstractThe vital signs of chronically ill patients are monitored daily. The record flags when a specific vital sign is stable or when it trends into dangerous territory. Patients also self-assess their current state of well-being, i.e. whether they are feeling worse than usual, neither unwell nor very well compared to usual, or are feeling better than usual. This paper examines whether past vital sign data can be used to forecast how well a patient is going to feel the next day. Reliable forecasting of a chronically sick patient’s likely state of health would be useful in regulating the care provided by a community nurse, scheduling care when the patient needs it most. The hypothesis is that the vital signs indicate a trend before a person feels unwell and, therefore, are lead indicators of a patient going to feel unwell. Time series and classification or regression tree methods are used to simplify the process of observing multiple measurements such as body temperature, heart rate, etc., by selecting the vital sign measures, which best forecast well-being. We use machine learning techniques to automatically find the best combination of these vital sign measurements and their rules that forecast the wellness of individual patients. The machine learning models provide rules that can be used to monitor the future wellness of a patient and regulate their care plans.

Telehealth Monitoring of Patients in the Community

2016 ◽  
Vol 25 (1) ◽  
pp. 37-53 ◽  
Author(s):  
Ross Sparks ◽  
Branko Celler ◽  
Chris Okugami ◽  
Rajiv Jayasena ◽  
Marlien Varnfield
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Vital Signs ◽  
Chronically Ill ◽  
Well Being ◽  
Health Condition ◽  
Depth Information ◽  
Statistical Background ◽  
Chronically Ill Patients

AbstractThis article outlines a decision support system that seeks to help community nurses monitor the well-being of their chronically ill patients. It is designed for nurses to stay in contact with their patients without spending unnecessary time on less productive aspects of community nursing, such as avoidable driving to and from patients’ houses and taking measurements of vital signs to assess their health condition. It therefore allows the nurse to spend more time on managing the factors that could lead to a healthier patient. The decision support system is developed for two levels of mathematical capability. Nurses with a statistical background are provided with in-depth information allowing them to detect changes in mean, mean square error (and hence variation), and correlations using a variation on dynamic principle components. Less mathematically inclined nurses are offered information about trends, change points, and a simpler multivariate view of a patient’s well-being involving parallel coordinate plots.

Behavioral engagement with mHealth for improved well-being among chronically ill patients: the role of human-related and app-related factors (Preprint)

2021 ◽  
Author(s):  
Freek Van Baelen ◽  
Melissa De Regge ◽  
Bart Larivière ◽  
Katrien Verleye ◽  
Sam Schelfout ◽  
...  
Keyword(s):  
Chronic Condition ◽  
Chronically Ill ◽  
Well Being ◽  
Behavioral Engagement ◽  
Related Factors ◽  
Design Factor ◽  
Mobile Health Applications ◽  
Chronically Ill Patients ◽  
The Relationship ◽  
Mhealth Apps

BACKGROUND Last decade has shown a considerable increase in the amount of mobile health applications (mHealth apps) in everyday life. These mHealth apps have the potential to significantly improve well-being for chronically ill patients. However, behavioral engagement with mHealth apps remains low. OBJECTIVE The aim of this study is to provide insight into the behavioral engagement of adults with chronic conditions with mHealth apps by investigating (1) how it is affected by human-related factors (here, physician motivation) and app-related factors (here, app integration) and (2) how it affects their well-being. Supplementary, this study considers the moderating effect of preference for traditional visits to the physician (habit) and experience in app use (app experience) by the patients. METHODS A scenario based experiment among patients with a chronic condition (n= 521) was carried out. A Bayesian SEM model with mediation and moderation analysis was conducted in MPlus. RESULTS Both physician motivation for mHealth app use and mHealth app integration have a positive effect on the behavioral engagement of chronically ill patients towards mHealth apps. Higher behavioral engagement positively influences the hedonic and eudaimonic well-being of chronically ill patients. App experience positively moderates the relationship between app integration and behavioral engagement. A patients’ habit with receiving traditional care does not moderate the relationship between physician motivation and behavioral engagement. CONCLUSIONS The human and design factor play a key role in behavioral engagement and well-being among patients with a chronic condition. During and after the development of a mHealth app, app integration and physician motivation should be a point of attention.

scholarly journals Trends in Heart-Rate Variability Signal Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Syem Ishaque ◽  
Naimul Khan ◽  
Sri Krishnan
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Well Being ◽  
Good Health ◽  
Machine Learning Techniques ◽  
Video Gaming ◽  
Clinical Events ◽  
Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

scholarly journals Binary Class Classification of Software Faults in Software Modules using Popular Machine Learning Techniques

2020 ◽  
Vol 6 (6) ◽  
pp. 14-18
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Regression Tree ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Customer Requirements ◽  
Software Faults ◽  
Learning Techniques ◽  
Software Modules

Software engineering is an important area that deals with development and maintenance of software. After developing a software, it is always important to track its performance. One has to always see whether the software functions according to customer requirements. To ensure this, faulty and non- faulty modules must be identified. For this purpose, one can make use of a model for binary class classification of faults. Different technique's outputs differ in one or the other way with respect to the following: fault dataset used, complexity, classification algorithm implemented, etc. Various machine learning techniques can be used for this purpose. But this paper deals with the best classification algorithms available till date and they are decision tree, random forest, naive bayes and logistic regression (tree-based techniques and bayesian based techniques). The motive behind developing such a project is to identify the faulty modules within a software before the actual software testing takes place. As a result, the time consumed by testers or the workload of the testers can be reduced to an extent. This work is very well useful to those working in software industry and also to those people carrying out research in software engineering where the lifecycle of development of a software is discussed.

scholarly journals Relationship between Spiritual Well-being and Quality of Life among Chronically Ill Individuals

2015 ◽  
Vol 2 (4) ◽  
Author(s):  
Kanwal Shahbaz ◽  
Dr. Kiran Shahbaz
Keyword(s):  
Quality Of Life ◽  
Economic Status ◽  
Chronically Ill ◽  
Well Being ◽  
Life Questionnaire ◽  
Spiritual Wellbeing ◽  
Socio Economic Status ◽  
Spiritual Well Being ◽  
Chronically Ill Patients

The study was aimed to find the relationship between Spiritual Wellbeing and Quality of Life among chronically ill individuals. Likewise, relationship between demographic variables with Quality of Life and Spiritual Wellbeing were also reconnoitered. Non probability purposive sampling technique was used with chronically ill patients of 15yrs to 80yrs. For measuring spiritual wellbeing Urdu version of “Spiritual Wellness Inventory” (SWI-URDU) (Hanif, 2010) was used. Alternatively, for the measurement of Quality of life WHO Quality of Life Questionnaire (WHO-QOL-BREF) was used. A sample of 200 chronically ill patients were taken from four different hospitals of Rawalpindi and Islamabad. Reliabilities of both the instruments were computed as 0.90 for SWI and 0.74 for WHO-QOL-BREF. Findings show that quality of life and Spiritual wellbeing is positively related among chronically ill individuals. Males found to score high on spiritual wellbeing than females. Individuals with less education are more spiritually inclined as compared to individuals with high education. Quality of life was scored high by individuals with higher education as compared to less education. Married individuals were having better quality of life than unmarried, separated widow and divorced. Patients with middle socio-economic status were having better quality of life than higher and lower. Quality of life was high among individuals with better monthly income than those who have low and middle monthly incomes. Spiritual well being is higher in middle adolescents (15-17) than in late (18-20) adolescents. The current research can be implemented in designing the intervention plans for the betterment of chronically ill patients. It may also help us to develop an insight that each patient with same disease but in different age group and socio-economic status has different needs and plans of treatment and care.

scholarly journals Symptom clusters among cancer survivors: what can machine learning techniques tell us?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Koen I. Neijenhuijs ◽  
Carel F. W. Peeters ◽  
Henk van Weert ◽  
Pim Cuijpers ◽  
Irma Verdonck-de Leeuw
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Cancer Survivors ◽  
Well Being ◽  
Physical Symptoms ◽  
Symptom Clusters ◽  
Machine Learning Techniques ◽  
Risk Scores ◽  
Learning Techniques ◽  
Patient Reported

Abstract Purpose Knowledge regarding symptom clusters may inform targeted interventions. The current study investigated symptom clusters among cancer survivors, using machine learning techniques on a large data set. Methods Data consisted of self-reports of cancer survivors who used a fully automated online application ‘Oncokompas’ that supports them in their self-management. This is done by 1) monitoring their symptoms through patient reported outcome measures (PROMs); and 2) providing a personalized overview of supportive care options tailored to their scores, aiming to reduce symptom burden and improve health-related quality of life. In the present study, data on 26 generic symptoms (physical and psychosocial) were used. Results of the PROM of each symptom are presented to the user as a no well-being risk, moderate well-being risk, or high well-being risk score. Data of 1032 cancer survivors were analysed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on high risk scores and moderate-to-high risk scores separately. Results When analyzing the high risk scores, seven clusters were extracted: one main cluster which contained most frequently occurring physical and psychosocial symptoms, and six subclusters with different combinations of these symptoms. When analyzing moderate-to-high risk scores, three clusters were extracted: two main clusters were identified, which separated physical symptoms (and their consequences) and psycho-social symptoms, and one subcluster with only body weight issues. Conclusion There appears to be an inherent difference on the co-occurrence of symptoms dependent on symptom severity. Among survivors with high risk scores, the data showed a clustering of more connections between physical and psycho-social symptoms in separate subclusters. Among survivors with moderate-to-high risk scores, we observed less connections in the clustering between physical and psycho-social symptoms.

Predicting mental disorder from noisy questionnaires: an anomaly detection approach based on keyword extraction and machine learning techniques

2021 ◽  
pp. 1-13
Author(s):  
Qing Zhou ◽  
Xi Shi ◽  
Liang Ge
Keyword(s):  
Machine Learning ◽  
Mental Disorders ◽  
Anomaly Detection ◽  
Well Being ◽  
Machine Learning Techniques ◽  
Keyword Extraction ◽  
Learning Techniques ◽  
Detection Approach ◽  
Scaling Methods ◽  
Machine Learning Models

The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation of question significance. Experiments demonstrate the effectiveness of our proposed methods.

Adaptation of the Questionnaire of Hahn, Cella, Bode, and Hanrahan in Spanish Patients Affected by Asbestos Poisoning

2013 ◽  
Vol 16 ◽  
Author(s):  
Miguel Clemente ◽  
Adela Reig-Botella
Keyword(s):  
High Reliability ◽  
Chronically Ill ◽  
Well Being ◽  
Chronic Illnesses ◽  
Future Directions ◽  
The Social ◽  
Male Patients ◽  
Chronically Ill Patients ◽  
Social Companionship ◽  
Very High

AbstractThe purpose of this study was to assess whether or not the questionnaire developed by Hahn, Cella, Bode, and Hanharan (2010) for use with cancer patients accurately measures the social well-being of individuals suffering from chronic illnesses associated with asbestos poisoning. One hundred ten male patients with asbestos poisoning were age-matched in blocks to a comparison group of 70 “healthy” controls, all of whom were current or retired employees of the largest naval company in Spain. The results indicate very high reliability of the Hahn et al. (2010) test to assess social well-being in these chronically ill patients, and a high concurrent validity of the measured outcomes with regard to results of the SCL-90 Derogatis questionnaire, especially on the social well-being dimensions of negative emotional support, negative social companionship, and satisfaction. Limitations of the study and possible future directions are discussed.

scholarly journals Multiple Patient Monitoring in High Dependency Units: A Field Study

2019 ◽  
Vol 63 (1) ◽  
pp. 669-669
Author(s):  
Marie-Lys F. A. Deschamps ◽  
Penelope M. Sanderson
Keyword(s):  
Critical Care ◽  
Simulation Study ◽  
Vital Sign ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Well Being ◽  
Alarm Fatigue ◽  
Critical Care Units ◽  
High Dependency ◽  
Australian Hospital

Much of the focus related to alarm fatigue has been directed towards reducing the number of alarms associated with vital sign monitoring. However, recent fieldwork conducted in four high dependency and critical care units of an Australian hospital suggests that the most problematic alarms were often unassociated with vital signs, such as IV pumps and mattress alarms. Many nurses indicated that they like alarms, even when false, because they support awareness of their patients’ well-being. Results of the fieldwork are guiding the design of a simulation study investigating clinical monitoring displays.

scholarly journals Extracting Knowledge from Big Data for Sustainability: A Comparison of Machine Learning Techniques

2019 ◽  
Vol 11 (23) ◽  
pp. 6669 ◽  
Author(s):  
Raghu Garg ◽  
Himanshu Aggarwal ◽  
Piera Centobelli ◽  
Roberto Cerchione
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Soil Quality ◽  
Soil Analysis ◽  
Regression Tree ◽  
Machine Learning Techniques ◽  
Stochastic Gradient Descent ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Operating Characteristics

At present, due to the unavailability of natural resources, society should take the maximum advantage of data, information, and knowledge to achieve sustainability goals. In today’s world condition, the existence of humans is not possible without the essential proliferation of plants. In the photosynthesis procedure, plants use solar energy to convert into chemical energy. This process is responsible for all life on earth, and the main controlling factor for proper plant growth is soil since it holds water, air, and all essential nutrients of plant nourishment. Though, due to overexposure, soil gets despoiled, so fertilizer is an essential component to hold the soil quality. In that regard, soil analysis is a suitable method to determine soil quality. Soil analysis examines the soil in laboratories and generates reports of unorganized and insignificant data. In this study, different big data analysis machine learning methods are used to extracting knowledge from data to find out fertilizer recommendation classes on behalf of present soil nutrition composition. For this experiment, soil analysis reports are collected from the Tata soil and water testing center. In this paper, Mahoot library is used for analysis of stochastic gradient descent (SGD), artificial neural network (ANN) performance on Hadoop environment. For better performance evaluation, we also used single machine experiments for random forest (RF), K-nearest neighbors K-NN, regression tree (RT), support vector machine (SVM) using polynomial function, SVM using radial basis function (RBF) methods. Detailed experimental analysis was carried out using overall accuracy, AUC–ROC (receiver operating characteristics (ROC), and area under the ROC curve (AUC)) curve, mean absolute prediction error (MAE), root mean square error (RMSE), and coefficient of determination (R2) validation measurements on soil reports dataset. The results provide a comparison of solution classes and conclude that the SGD outperforms other approaches. Finally, the proposed results support to select the solution or recommend a class which suggests suitable fertilizer to crops for maximum production.

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