scholarly journals A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 866
Author(s):  
Madhav Raj Theeng Tamang ◽  
Mhd Saeed Sharif ◽  
Ali H. Al-Bayatti ◽  
Ahmed S. Alfakeeh ◽  
Alhuseen Omar Alsayed
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Subjective Evaluation ◽  
Heat Rate ◽  
Objective Evaluation ◽  
Learning Approaches ◽  
Large Cities ◽  
Agent Interactions ◽  
Before And After ◽  
Analysis And Synthesis

The daily commute represents a source of chronic stress that is positively correlated with physiological consequences, including increased blood pressure, heart rate, fatigue, and other negative mental and physical health effects. The purpose of this research is to investigate and predict the physiological effects of commuting in Greater London on the human body based on machine-learning approaches. For each participant, the data were collected for five consecutive working days, before and after the commute, using non-invasive wearable biosensor technology. Multimodal behaviour, analysis and synthesis are the subjects of major efforts in computing field to realise the successful human–human and human–agent interactions, especially for developing future intuitive technologies. Current analysis approaches still focus on individuals, while we are considering methodologies addressing groups as a whole. This research paper employs a pool of machine-learning approaches to predict and analyse the effect of commuting objectively. Comprehensive experimentation has been carried out to choose the best algorithmic structure that suit the problem in question. The results from this study suggest that whether the commuting period was short or long, all objective bio-signals (heat rate and blood pressure) were higher post-commute than pre-commute. In addition, the results match both the subjective evaluation obtained from the Positive and Negative Affect Schedule and the proposed objective evaluation of this study in relation to the correlation between the effect of commuting on bio-signals. Our findings provide further support for shorter commutes and using the healthier or active modes of transportation.

scholarly journals Objective evaluation versus subjective evaluation of flexion tests in the pelvic limb of horses

2019 ◽  
Vol 56 (4) ◽  
pp. e157248
Author(s):  
Marcos Da Silva Azevedo ◽  
Flávio Desessards De La Côrte ◽  
Ricardo Pozzobon ◽  
Stefano Leite Dau ◽  
Miguel Gallio
Keyword(s):  
Inertial Sensors ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
Evaluation Methods ◽  
Wireless System ◽  
Moderate Agreement ◽  
Before And After ◽  
Pelvic Limb ◽  
Different Levels ◽  
Low Rate

The agreement between subjective and objective evaluation methods was studied to identify claudication in the pelvic limbs of horses before and after flexion tests were performed. Twenty-nine horses were equipped with a wireless system of inertial sensors and evaluated during seven times while trotting. Videos were recorded to be evaluated by three veterinarians, with different levels of experience, to evaluate the agreement between the two different methods and between the evaluators. The evaluators and the objective evaluation had a low rate of agreement, with the exception of moderate agreement between the objective evaluation and evaluator 1 to identify lameness after the left total flexion and moderate agreement in evaluating the response to the tests, between objective evaluation and evaluator 2, after rightdistal flexion. This shows that there was a low agreement among the evaluators and between them and the objective evaluation for identifying lameness, measuring the degree of lameness and the response to the flexion tests.

scholarly journals Features Derived From Blood Pressure Predict Elevated Intracranial Pressure in Critically Ill Children

2021 ◽  
Author(s):  
Kassi Ackerman ◽  
Akram Mohammed ◽  
Lokesh Chinthala ◽  
Robert L. Davis ◽  
Rishikesan Kamaleswaran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Intracranial Pressure ◽  
Data Streams ◽  
Brain Injuries ◽  
Critically Ill Children ◽  
Learning Approaches ◽  
Elevated Intracranial Pressure ◽  
Pressure Data ◽  
Blood Pressure Data

Abstract Clinicians frequently observe hemodynamic changes preceding elevated intracranial pressure (ICP) events. We employed a machine learning approach to identify novel and differentially expressed features associated with elevated ICP (eICP) events in children with severe brain injuries. Statistical features from physiologic data streams were derived from non-overlapping 30-minute analysis windows prior to 21 eICP events; 200 records without eICP events were used as controls. Ten Monte Carlo simulations with training/testing splits provided performance benchmarks for 4 machine learning approaches. XGB yielded the best performing predictive models. SHAP analyses demonstrated that a majority of the top 20 contributing features from each simulation consistently derived from blood pressure data streams up to 240 minutes prior to eICP events, rivaling ICP-derived features at 0-60 minutes. Our AUROC benchmark at the 30-60 minutes analysis window using the XGB model bundle was 0.82 (95% CI 0.81-0.83); the AUPRC was 0.24 (95% CI 0.23-0.25), well-above the expected baseline. We conclude that physiomarkers discernable by machine learning are concentrated within blood pressure data up to 4 hours prior to eICP events and demonstrate robust benchmark performance. Future predictive modeling of elevated ICP events should leverage features contained within hemodynamic signals.

scholarly journals Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning

Molecules ◽  
2019 ◽  
Vol 24 (6) ◽  
pp. 1075 ◽  
Author(s):  
Radosław Chaber ◽  
Christopher Arthur ◽  
Kornelia Łach ◽  
Anna Raciborska ◽  
Elżbieta Michalak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Ewing Sarcoma ◽  
Bone Tumour ◽  
Ftir Spectra ◽  
Support Vector ◽  
Learning Approaches ◽  
Linear Discriminant ◽  
Before And After ◽  
Leave One Out

Background: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing sarcoma—the second most common paediatric malignant bone tumour. Fourier transform infrared (FTIR) spectroscopy of tissues allows the bulk biochemical content of a biological sample to be probed and makes possible the study and diagnosis of disease. Methods: In this retrospective study, FTIR spectra of sections of biopsy-obtained bone tissue were recorded. Twenty-seven patients (between 5 and 20 years of age) with newly diagnosed Ewing sarcoma of bone were included in this study. The prognostic value of FTIR spectra obtained from Ewing sarcoma (ES) tumours before and after neoadjuvant chemotherapy were analysed in combination with various data-reduction and machine learning approaches. Results: Random forest and linear discriminant analysis supervised learning models were able to correctly predict patient mortality in 92% of cases using leave-one-out cross-validation. The best performing model for predicting patient relapse was a linear Support Vector Machine trained on the observed spectral changes as a result of chemotherapy treatment, which achieved 92% accuracy. Conclusion: FTIR spectra of tumour biopsy samples may predict treatment outcome in paediatric Ewing sarcoma patients with greater than 92% accuracy.

PENGARUH TERAPI TERTAWA TERHADAP PENURUNAN TEKANAN DARAH LANSIA DI UPT PANTI WREDHA BUDHI DHARMA YOGYAKARTA

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Niken Setyaningrum ◽  
Andri Setyorini ◽  
Fachruddin Tri Fitrianta
Keyword(s):  
Blood Pressure ◽  
Data Analysis ◽  
The Elderly ◽  
Control Group ◽  
Group Comparison ◽  
Control Blood Pressure ◽  
Men And Women ◽  
Treatment Of Hypertension ◽  
Before And After ◽  
The Difference

ABSTRACTBackground: Hypertension is one of the most common diseases, because this disease is suffered byboth men and women, as well as adults and young people. Treatment of hypertension does not onlyrely on medications from the doctor or regulate diet alone, but it is also important to make our bodyalways relaxed. Laughter can help to control blood pressure by reducing endocrine stress andcreating a relaxed condition to deal with relaxation.Objective: The general objective of the study was to determine the effect of laughter therapy ondecreasing elderly blood pressure in UPT Panti Wredha Budhi Dharma Yogyakarta.Methods: The design used in this study is a pre-experimental design study with one group pre-posttestresearch design where there is no control group (comparison). The population in this study wereelderly aged over> 60 years at 55 UPT Panti Wredha Budhi Dharma Yogyakarta. The method oftaking in this study uses total sampling. The sample in this study were 55 elderly. Data analysis wasused to determine the difference in blood pressure before and after laughing therapy with a ratio datascale that was using Pairs T-TestResult: There is an effect of laughing therapy on blood pressure in the elderly at UPT Panti WredhaBudhi Dharma Yogyakarta marked with a significant value of 0.000 (P <0.05)

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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