scholarly journals Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5711
Author(s):  
Yan Hao Tan ◽  
Yuwen Liao ◽  
Zhijie Tan ◽  
King-Ho Holden Li
Keyword(s):  
Machine Learning ◽  
Goodness Of Fit ◽  
Polynomial Regression ◽  
Machine Learning Algorithms ◽  
High Data ◽  
Hospital Bed ◽  
Patient Health ◽  
Validation Score ◽  
Advanced Robotics ◽  
Tendency Analysis

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

scholarly journals EXPERIMENTAL MACHINE LEARNING OF FINGER PHOTOPLETHYSMOGRAPHY (PPG) FOR AUTONOMOUS HOSPITAL BED PUSHING FRAMEWORK USING POLYNOMIAL REGRESSION

2020 ◽  
Vol 5 (9) ◽  
pp. 108-119
Author(s):  
Yan Hao Tan ◽  
Holden Li King Ho
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Influencing Factor ◽  
Life Safety ◽  
Single Subject ◽  
Single Male ◽  
Skilled Nursing ◽  
Non Invasive ◽  
Hospital Bed ◽  
Validation Score

In community-based healthcare, the nursing workforce requires low-skilled nursing automation in the hospital to accelerate talent development towards high-skilled advance practice nurse for community deployment. As precursor, the hospital bed pushing operation for medium-risk patient was hypothesized as a novice nursing task where artificial intelligence automation is possible. The solution framework was embodied by a concept of operation with non-invasive vitals monitoring as priority to study feasibility in addressing patient life-safety requirements. Polynomial regression machine learning of 65 one-hour sets of finger PPG data from a single subject were collected and studied. Convergence of finger PPG to 8th degree polynomial was observed which suggested process feasibility towards establishing patient safe states during autonomous journey. Process reliability ranged between 2% to 95% with long PPG counts as influencing factor for drops in reliability score. Motivation/Background: A predictable non-invasive vitals monitoring was priority to enable autonomous hospital bed pushing framework to address patient life-safety concerns during autonomous journey. Finger PPG is a non-invasive and easy to use method to monitor heart related activities and used to study for convergence and reliability within the framework. Method:65 one-hour sets of finger PPG was recorded from a single male, age 27 subject. The data was processed by polynomial regression machine learning technique to output the degree of polynomial with highest cross validation score mean. Results: Convergence of regressed PPG data to 8th degree for both pre-journey and journey datasets and degree of polynomial matching reliability of 2% to 95% were observed. Conclusions: Convergence of PPG data facilitates the establishment of safe physical states in vitals monitoring, enabling the autonomous hospital bed pushing framework for further development. Reliability remains an area for improvement via medical grade.

scholarly journals A Review on Linear Regression Comprehensive in Machine Learning

2020 ◽  
Vol 1 (4) ◽  
pp. 140-147
Author(s):  
Dastan Maulud ◽  
Adnan M. Abdulazeez
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Linear Relationship ◽  
Multiple Regression ◽  
Polynomial Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Explanatory Variables ◽  
Almost All ◽  
Simple Regression

Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

scholarly journals Prediction of Damage Accumulation Effect of Wood Structural Members under Long-Term Service: A Machine Learning Approach

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1243
Author(s):  
Zheng Li ◽  
Duo Tao ◽  
Mengwei Li ◽  
Zhan Shu ◽  
Songshi Jing ◽  
...  
Keyword(s):  
Machine Learning ◽  
Damage Accumulation ◽  
Goodness Of Fit ◽  
Machine Learning Algorithms ◽  
Cycle Performance ◽  
Model Parameters ◽  
Timber Structures ◽  
Structural Members ◽  
Accumulation Effect

It is well known that wood structural members can stand a relatively heavy load in the short term but will gradually get weaker if the load is applied for a longer period. This phenomenon is caused by the damage accumulation effect in wood and should be appropriately considered during the design of timber structures. Although various formulation methods (also known as classical models) have been proposed to evaluate the damage accumulation effect in wood, the calibration of model parameters is very time-consuming. Our work proposes a novel method to deal with the damage accumulation effect in wood that involves the application of machine learning algorithms. The proposed algorithm considers a multi-objective optimization process with a combination of goodness-of-fit and complexity. Long-term experimental data of typical wood species are used for developing the machine learning based damage accumulation model. Compared with existing pre-formulated models, our model managed to reduce the complexity of the model structure and give sufficiently accurate and unbiased predictions. This study aims to provide a novel tool for evaluating the damage accumulation in wood structural members, and the proposed model can further support the life-cycle performance assessment of timber structures under long-term service scenarios.

scholarly journals Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3777 ◽  
Author(s):  
Ataollah Shirzadi ◽  
Karim Soliamani ◽  
Mahmood Habibnejhad ◽  
Ataollah Kavian ◽  
Kamran Chapi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Prediction Accuracy ◽  
Goodness Of Fit ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Sample Sizes ◽  
Promising Alternative ◽  
Statistical Measures

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.

scholarly journals Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 33 ◽  
Author(s):  
Joshua Gawlitza ◽  
Timo Sturm ◽  
Kai Spohrer ◽  
Thomas Henzler ◽  
Ibrahim Akin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Function ◽  
Pulmonary Function ◽  
Polynomial Regression ◽  
Pulmonary Function Tests ◽  
Quantitative Computed Tomography ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Function Tests

Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.

scholarly journals A Daily Covid-19 Cases Prediction System using Data Mining and Machine Learning Algorithm

2021 ◽  
Author(s):  
Yiqi Jack Gao ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hospital Admissions ◽  
Polynomial Regression ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Policy Makers ◽  
Diverse Range ◽  
Using Data

The start of 2020 marked the beginning of the deadly COVID-19 pandemic caused by the novel SARS-COV-2 from Wuhan, China. As of the time of writing, the virus had infected over 150 million people worldwide and resulted in more than 3.5 million global deaths. Accurate future predictions made through machine learning algorithms can be very useful as a guide for hospitals and policy makers to make adequate preparations and enact effective policies to combat the pandemic. This paper carries out a two pronged approach to analyzing COVID-19. First, the model utilizes the feature significance of random forest regressor to select eight of the most significant predictors (date, new tests, weekly hospital admissions, population density, total tests, total deaths, location, and total cases) for predicting daily increases of Covid-19 cases, highlighting potential target areas in order to achieve efficient pandemic responses. Then it utilizes machine learning algorithms such as linear regression, polynomial regression, and random forest regression to make accurate predictions of daily COVID-19 cases using a combination of this diverse range of predictors and proved to be competent at generating predictions with reasonable accuracy.

Trend and Predictive Analytics of Dengue Prevalence in Administrative Region

2020 ◽  
pp. 236-262
Author(s):  
Kannimuthu Subramanian ◽  
Swathypriyadharsini P. ◽  
Gunavathi C. ◽  
Premalatha K.
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Goodness Of Fit ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Public Health Problem ◽  
Viral Disease ◽  
Machine Learning Algorithms ◽  
Severe Dengue ◽  
Major Public Health Problem

Dengue is fast emerging pandemic-prone viral disease in many parts of the world. Dengue flourishes in urban areas, suburbs, and the countryside, but also affects more affluent neighborhoods in tropical and subtropical countries. Dengue is a mosquito-borne viral infection causing a severe flu-like illness and sometimes causing a potentially deadly complication called severe dengue. It is a major public health problem in India. Accurate and timely forecasts of dengue incidence in India are still lacking. In this chapter, the state-of-the-art machine learning algorithms are used to develop an accurate predictive model of dengue. Several machine learning algorithms are used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed, and it is found that the optimized SVR gives minimal RMSE 0.25. The classifiers are applied, and experiment results show that the extreme boost and random forest gives 93.65% accuracy.

scholarly journals Application of Machine Learning to Mortality Modeling and Forecasting

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 26 ◽  
Author(s):  
Susanna Levantesi ◽  
Virginia Pizzorusso
Keyword(s):  
Machine Learning ◽  
Stochastic Models ◽  
Goodness Of Fit ◽  
Mortality Rates ◽  
Machine Learning Algorithms ◽  
Stochastic Mortality ◽  
Life Insurers ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Out Of Sample

Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy.

scholarly journals Predicting and Analysing the Behaviour of COVID-19

2021 ◽  
pp. 40-46
Author(s):  
Gaurav Singh ◽  
Shivam Rai ◽  
Himanshu Mishra ◽  
Manoj Kumar
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Systems Science ◽  
Data Repository ◽  
Support Vector ◽  
Squared Error

The prime objective of this work is to predicting and analysing the Covid-19 pandemic around the world using Machine Learning algorithms like Polynomial Regression, Support Vector Machine and Ridge Regression. And furthermore, assess and compare the performance of the varied regression algorithms as far as parameters like R squared, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In this work, we have used the dataset available on Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University. We have analyzed the covid19 cases from 22/1/2020 till now. We applied a supervised machine learning prediction model to forecast the possible confirmed cases for the next ten days.

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