More music, more health!

2020 ◽  
Author(s):  
Alfredo Raglio
Keyword(s):  
New Technologies ◽  
Therapeutic Potential ◽  
Vital Signs ◽  
Clinical Work ◽  
Well Being ◽  
Therapeutic Interventions ◽  
Machine Learning Techniques ◽  
Music Listening ◽  
Learning Techniques ◽  
Therapy Interventions

Abstract For several decades, music has been used more and more frequently and consciously as a mean of care to reduce or stabilize symptoms and/or complications arising therefrom. This has been the case with several diseases and conditions. Indeed, music also gives pleasure, promotes well-being, facilitates the expression and regulation of emotions and improves communication and relationships between individuals. The basis underlying the therapeutic potential of music are to be considered in relation to the extensive action that music itself exerts on the brain but also on vital signs and neurochemical systems. Music therapy interventions are based on active/receptive approaches (characterized by a relational or rehabilitative component) but also on music listening. Music-based interventions can be considered activities aimed at increasing the person's well-being. The objectives of making/listening to music are to improve the person's mood and motivation, promote socialization and stimulate sensory, motor and cognitive aspects. In particular, music listening effects concern structured symptoms and general well-being reducing anxiety and stress. New technologies, such as algorithmic music and machine learning techniques, can also help to develop therapeutic interventions with music and to bring art and science closer together, in the service of medicine, in clinical work and in research.

Assessing the Accuracy of Fault Interpretation using Machine Learning Techniques when Risking Faults for CO2 Storage Site Assessment

2021 ◽  
pp. 1-55
Author(s):  
Emma A. H. Michie ◽  
Behzad Alaei ◽  
Alvar Braathen
Keyword(s):  
Deep Learning ◽  
New Technologies ◽  
Co2 Storage ◽  
Fault Reactivation ◽  
Machine Learning Techniques ◽  
Close Similarity ◽  
Storage Site ◽  
Site Assessment ◽  
Learning Techniques ◽  
Fault Interpretation

Generating an accurate model of the subsurface for the purpose of assessing the feasibility of a CO2 storage site is crucial. In particular, how faults are interpreted is likely to influence the predicted capacity and integrity of the reservoir; whether this is through identifying high risk areas along the fault, where fluid is likely to flow across the fault, or by assessing the reactivation potential of the fault with increased pressure, causing fluid to flow up the fault. New technologies allow users to interpret faults effortlessly, and in much quicker time, utilizing methods such as Deep Learning. These Deep Learning techniques use knowledge from Neural Networks to allow end-users to compute areas where faults are likely to occur. Although these new technologies may be attractive due to reduced interpretation time, it is important to understand the inherent uncertainties in their ability to predict accurate fault geometries. Here, we compare Deep Learning fault interpretation versus manual fault interpretation, and can see distinct differences to those faults where significant ambiguity exists due to poor seismic resolution at the fault; we observe an increased irregularity when Deep Learning methods are used over conventional manual interpretation. This can result in significant differences between the resulting analyses, such as fault reactivation potential. Conversely, we observe that well-imaged faults show a close similarity between the resulting fault surfaces when both Deep Learning and manual fault interpretation methods are employed, and hence we also observe a close similarity between any attributes and fault analyses made.

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.

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.

scholarly journals Delivering Precision Medicine to Patients with Spinal Cord Disorders; Insights into Applications of Bioinformatics and Machine Learning from Studies of Degenerative Cervical Myelopathy

2021 ◽  
Author(s):  
Kalum J. Ost ◽  
David W. Anderson ◽  
David W. Cadotte
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
New Technologies ◽  
Machine Learning Techniques ◽  
Massive Datasets ◽  
Learning Framework ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Spinal Cord Disorders ◽  
Degenerative Cervical Myelopathy

With the common adoption of electronic health records and new technologies capable of producing an unprecedented scale of data, a shift must occur in how we practice medicine in order to utilize these resources. We are entering an era in which the capacity of even the most clever human doctor simply is insufficient. As such, realizing “personalized” or “precision” medicine requires new methods that can leverage the massive amounts of data now available. Machine learning techniques provide one important toolkit in this venture, as they are fundamentally designed to deal with (and, in fact, benefit from) massive datasets. The clinical applications for such machine learning systems are still in their infancy, however, and the field of medicine presents a unique set of design considerations. In this chapter, we will walk through how we selected and adjusted the “Progressive Learning framework” to account for these considerations in the case of Degenerative Cervical Myeolopathy. We additionally compare a model designed with these techniques to similar static models run in “perfect world” scenarios (free of the clinical issues address), and we use simulated clinical data acquisition scenarios to demonstrate the advantages of our machine learning approach in providing personalized diagnoses.

scholarly journals COMPARATIVE ANALYSIS AND EVALUATION OF THE APPLICATION OF DEEP LEARNING TECHNIQUES TO CYBERSECURITY DATASETS

10.6036/10007 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 528-533
Author(s):  
XAVIER LARRIVA NOVO ◽  
MARIO VEGA BARBAS ◽  
VICTOR VILLAGRA ◽  
JULIO BERROCAL
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
New Technologies ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory

Cybersecurity has stood out in recent years with the aim of protecting information systems. Different methods, techniques and tools have been used to make the most of the existing vulnerabilities in these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems that allow detecting possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and reduce the large number of false positives that these technologies presents in their results. Therefore, this generates the need to research and develop new high-performance cybersecurity systems that allow efficient analysis and resolution of these results. This research presents the application of machine learning techniques to classify real traffic, in order to identify possible attacks. The study has been carried out using machine learning tools applying deep learning algorithms such as multi-layer perceptron and long-short-term-memory. Additionally, this document presents a comparison between the results obtained by applying the aforementioned algorithms and algorithms that are not deep learning, such as: random forest and decision tree. Finally, the results obtained are presented, showing that the long-short-term-memory algorithm is the one that provides the best results in relation to precision and logarithmic loss.

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.

scholarly journals Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants

2021 ◽  
Vol 118 (43) ◽  
pp. e2104925118
Author(s):  
Hyoyoung Jeong ◽  
Sung Soo Kwak ◽  
Seokwoo Sohn ◽  
Jong Yoon Lee ◽  
Young Joong Lee ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Body Movement ◽  
Vital Signs ◽  
The Body ◽  
Therapeutic Interventions ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Integrated Sensor ◽  
Full Body

Early identification of atypical infant movement behaviors consistent with underlying neuromotor pathologies can expedite timely enrollment in therapeutic interventions that exploit inherent neuroplasticity to promote recovery. Traditional neuromotor assessments rely on qualitative evaluations performed by specially trained personnel, mostly available in tertiary medical centers or specialized facilities. Such approaches are high in cost, require geographic proximity to advanced healthcare resources, and yield mostly qualitative insight. This paper introduces a simple, low-cost alternative in the form of a technology customized for quantitatively capturing continuous, full-body kinematics of infants during free living conditions at home or in clinical settings while simultaneously recording essential vital signs data. The system consists of a wireless network of small, flexible inertial sensors placed at strategic locations across the body and operated in a wide-bandwidth and time-synchronized fashion. The data serve as the basis for reconstructing three-dimensional motions in avatar form without the need for video recordings and associated privacy concerns, for remote visual assessments by experts. These quantitative measurements can also be presented in graphical format and analyzed with machine-learning techniques, with potential to automate and systematize traditional motor assessments. Clinical implementations with infants at low and at elevated risks for atypical neuromotor development illustrates application of this system in quantitative and semiquantitative assessments of patterns of gross motor skills, along with body temperature, heart rate, and respiratory rate, from long-term and follow-up measurements over a 3-mo period following birth. The engineering aspects are compatible for scaled deployment, with the potential to improve health outcomes for children worldwide via early, pragmatic detection methods.

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.

Analysis of Crime Report by Data Analytics Using Python

2021 ◽  
pp. 54-79
Author(s):  
G. Maria Jones ◽  
S. Godfrey Winster
Keyword(s):  
Machine Learning ◽  
New Technologies ◽  
Rapid Development ◽  
Performance Measure ◽  
Machine Learning Techniques ◽  
Complex Data ◽  
Time Data ◽  
Related Data ◽  
Learning Techniques ◽  
And Performance

The ever-rapid development of technology in today's world tends to provide us with a dramatic explosion of data, leading to its accumulation and thus data computation has amplified in comparison to the recent past. To manage such complex data, emerging new technologies are enabled specially to identify crime patterns, as crime-related data is escalating. These digital technologies have the potential to manipulate and also alter the pattern. To combat this, machine learning techniques are introduced which have the ability to analyse such voluminous data. In this work, the authors intend to understand and implement machine learning techniques in real time data analysis by means of Python. The detailed explanation in preparing the dataset, understanding, visualizing the data using pandas, and performance measure of algorithm is evaluated.

scholarly journals Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques

2021 ◽  
Vol 11 (5) ◽  
pp. 343
Author(s):  
Fabiana Tezza ◽  
Giulia Lorenzoni ◽  
Danila Azzolina ◽  
Sofia Barbar ◽  
Lucia Anna Carmela Leone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hospital Mortality ◽  
Learning Algorithm ◽  
Vital Signs ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Techniques

The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome of interest. The model with the best performance will be used to identify in-hospital mortality predictors and to build an in-hospital mortality prediction tool. The study involved patients with COVID-19, proved by PCR test, admitted to the “Ospedali Riuniti Padova Sud” COVID-19 referral center in the Veneto region, Italy. The algorithms considered were the Recursive Partition Tree (RPART), the Support Vector Machine (SVM), the Gradient Boosting Machine (GBM), and Random Forest. The resampled performances were reported for each MLT, considering the sensitivity, specificity, and the Receiving Operative Characteristic (ROC) curve measures. The study enrolled 341 patients. The median age was 74 years, and the male gender was the most prevalent. The Random Forest algorithm outperformed the other MLTs in predicting in-hospital mortality, with a ROC of 0.84 (95% C.I. 0.78–0.9). Age, together with vital signs (oxygen saturation and the quick SOFA) and lab parameters (creatinine, AST, lymphocytes, platelets, and hemoglobin), were found to be the strongest predictors of in-hospital mortality. The present work provides insights for the prediction of in-hospital mortality of COVID-19 patients using a machine-learning algorithm.

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