IoT Based Cloud Agent System for Adult Health Care Monitoring

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring.

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Assessment of appropriate body mass index cut-off points for long-term mortality among ST-elevation myocardial infarction survivors in Asian population using machine learning algorithm

Heart and Vessels ◽

10.1007/s00380-021-01916-w ◽

2021 ◽

Author(s):

Naoki Yoshioka ◽

Kensuke Takagi ◽

Akihito Tanaka ◽

Yasuhiro Morita ◽

Ruka Yoshida ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Body Mass Index ◽

Body Mass ◽

Learning Algorithm ◽

Asian Population ◽

Machine Learning Algorithm ◽

St Elevation ◽

Long Term Mortality

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Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2019.04.027 ◽

2019 ◽

Vol 109 ◽

pp. 79-84 ◽

Cited By ~ 12

Author(s):

Christopher Barton ◽

Uli Chettipally ◽

Yifan Zhou ◽

Zirui Jiang ◽

Anna Lynn-Palevsky ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Vital Signs ◽

Machine Learning Algorithm

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Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients

Sensors ◽

10.3390/s20226413 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6413

Author(s):

Victor A. Convertino ◽

Steven G. Schauer ◽

Erik K. Weitzel ◽

Sylvain Cardin ◽

Mark E. Stackle ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Wearable Sensors ◽

Clinical Status ◽

Vital Signs ◽

Human Model ◽

Machine Learning Algorithm ◽

Medical Monitoring ◽

Arterial Waveform ◽

Compensatory Reserve

Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.

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Inconsistency-Induced Learning for Perpetual Learners

Advances in Abstract Intelligence and Soft Computing ◽

10.4018/978-1-4666-2651-5.ch006 ◽

2013 ◽

pp. 70-87 ◽

Cited By ~ 1

Author(s):

Du Zhang ◽

Meiliu Lu

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Target Function ◽

Data Sets ◽

Revision Process ◽

Learning Agents ◽

Learning Agent ◽

The One ◽

Over Time

One of the long-term research goals in machine learning is how to build never-ending learners. The state-of-the-practice in the field of machine learning thus far is still dominated by the one-time learner paradigm: some learning algorithm is utilized on data sets to produce certain model or target function, and then the learner is put away and the model or function is put to work. Such a learn-once-apply-next (or LOAN) approach may not be adequate in dealing with many real world problems and is in sharp contrast with the human’s lifelong learning process. On the other hand, learning can often be brought on through overcoming some inconsistent circumstances. This paper proposes a framework for perpetual learning agents that are capable of continuously refining or augmenting their knowledge through overcoming inconsistencies encountered during their problem-solving episodes. The never-ending nature of a perpetual learning agent is embodied in the framework as the agent’s continuous inconsistency-induced belief revision process. The framework hinges on the agents recognizing inconsistency in data, information, knowledge, or meta-knowledge, identifying the cause of inconsistency, revising or augmenting beliefs to explain, resolve, or accommodate inconsistency. The authors believe that inconsistency can serve as one of the important learning stimuli toward building perpetual learning agents that incrementally improve their performance over time.

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

Journal of Personalized Medicine ◽

10.3390/jpm11050343 ◽

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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Mapping the risk terrain for crime using machine learning

10.31235/osf.io/xc538 ◽

2020 ◽

Author(s):

Andrew Palmer Wheeler ◽

Wouter Steenbeek

Keyword(s):

Machine Learning ◽

Random Forests ◽

Predictive Accuracy ◽

Learning Algorithm ◽

Kernel Density ◽

Black Box ◽

Terrain Models ◽

Interpretable Model ◽

Spatially Varying

Objectives: We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the ‘black box’ of Random Forests, considerably improving their interpretability.Methods: We generate long-term crime forecasts for robberies in Dallas at 200 by 200 feet grid cells that allow spatially varying associations of crime generators and demographic factors across the study area. We then show how using interpretable model summaries facilitate understanding the model’s inner workings.Results: We find that Random Forests greatly outperform Risk Terrain Models and Kernel Density Estimation in terms of forecasting future crimes using different measures of predictive accuracy, but only slightly outperform using prior counts of crime. We find different factors that predict crime are highly non-linear and vary over space. Conclusions: We show how using black-box machine learning models can provide accurate micro placed based crime predictions, but still be interpreted in a manner that fosters understanding of why a place is predicted to be risky.Data and code to replicate the results can be downloaded from https://www.dropbox.com/sh/b3n9a6z5xw14rd6/AAAjqnoMVKjzNQnWP9eu7M1ra?dl=0

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Inconsistency-Induced Learning for Perpetual Learners

International Journal of Software Science and Computational Intelligence ◽

10.4018/jssci.2011100103 ◽

2011 ◽

Vol 3 (4) ◽

pp. 33-51 ◽

Cited By ~ 9

Author(s):

Du Zhang ◽

Meiliu Lu

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Target Function ◽

Data Sets ◽

Revision Process ◽

Learning Agents ◽

Learning Agent ◽

The One ◽

Real World Problems

One of the long-term research goals in machine learning is how to build never-ending learners. The state-of-the-practice in the field of machine learning thus far is still dominated by the one-time learner paradigm: some learning algorithm is utilized on data sets to produce certain model or target function, and then the learner is put away and the model or function is put to work. Such a learn-once-apply-next (or LOAN) approach may not be adequate in dealing with many real world problems and is in sharp contrast with the human’s lifelong learning process. On the other hand, learning can often be brought on through overcoming some inconsistent circumstances. This paper proposes a framework for perpetual learning agents that are capable of continuously refining or augmenting their knowledge through overcoming inconsistencies encountered during their problem-solving episodes. The never-ending nature of a perpetual learning agent is embodied in the framework as the agent’s continuous inconsistency-induced belief revision process. The framework hinges on the agents recognizing inconsistency in data, information, knowledge, or meta-knowledge, identifying the cause of inconsistency, revising or augmenting beliefs to explain, resolve, or accommodate inconsistency. The authors believe that inconsistency can serve as one of the important learning stimuli toward building perpetual learning agents that incrementally improve their performance over time.

Download Full-text