scholarly journals The prototype device for non-invasive diagnosis of arteriovenous fistula condition using machine learning methods

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marcin Grochowina ◽  
Lucyna Leniowska ◽  
Agnieszka Gala-Błądzińska
Keyword(s):  
Machine Learning ◽  
Arteriovenous Fistula ◽  
Low Cost ◽  
Principal Component ◽  
Special Kind ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Signal Acquisition ◽  
Non Invasive ◽  
Prototype Device

Abstract Pattern recognition and automatic decision support methods provide significant advantages in the area of health protection. The aim of this work is to develop a low-cost tool for monitoring arteriovenous fistula (AVF) with the use of phono-angiography method. This article presents a developed and diagnostic device that implements classification algorithms to identify 38 patients with end stage renal disease, chronically hemodialysed using an AVF, at risk of vascular access stenosis. We report on the design, fabrication, and preliminary testing of a prototype device for non-invasive diagnosis which is very important for hemodialysed patients. The system includes three sub-modules: AVF signal acquisition, information processing and classification and a unit for presenting results. This is a non-invasive and inexpensive procedure for evaluating the sound pattern of bruit produced by AVF. With a special kind of head which has a greater sensitivity than conventional stethoscope, a sound signal from fistula was recorded. The proces of signal acquisition was performed by a dedicated software, written specifically for the purpose of our study. From the obtained phono-angiogram, 23 features were isolated for vectors used in a decision-making algorithm, including 6 features based on the waveform of time domain, and 17 features based on the frequency spectrum. Final definition of the feature vector composition was obtained by using several selection methods: the feature-class correlation, forward search, Principal Component Analysis and Joined-Pairs method. The supervised machine learning technique was then applied to develop the best classification model.

scholarly journals Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6334
Author(s):  
Sigfredo Fuentes ◽  
Claudia Gonzalez Viejo ◽  
Surinder S. Chauhan ◽  
Aleena Joy ◽  
Eden Tongson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Respiration Rate ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Automated Tracking ◽  
Non Invasive ◽  
Cutoff Frequencies ◽  
Thermal Cameras ◽  
High Respiration Rate ◽  
Two Parameter

Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

scholarly journals Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Tomasz Oliwa ◽  
Steven B. Maron ◽  
Leah M. Chase ◽  
Samantha Lomnicki ◽  
Daniel V.T. Catenacci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Named Entity ◽  
Pathology Reports

PURPOSE Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions. PATIENTS AND METHODS Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step. RESULTS We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance. CONCLUSION Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

scholarly journals Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Low Cost ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Supervised Machine Learning ◽  
Melanoma Metastasis ◽  
Immunologic Research

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.

scholarly journals In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7752
Author(s):  
Jose M. Celaya-Padilla ◽  
Jonathan S. Romero-González ◽  
Carlos E. Galvan-Tejada ◽  
Jorge I. Galvan-Tejada ◽  
Huizilopoztli Luna-García ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Low Cost ◽  
Child Death ◽  
Classification Model ◽  
Selection Strategy ◽  
Drinking And Driving ◽  
Forward Selection ◽  
Vehicle Accidents ◽  
Non Invasive ◽  
Invasive Method

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.

scholarly journals Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning

2020 ◽  
Vol 11 ◽  
Author(s):  
Yi Guo ◽  
Yushan Liu ◽  
Wenjie Ming ◽  
Zhongjin Wang ◽  
Junming Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Focal Cortical Dysplasia ◽  
Cortical Dysplasia ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Seizure Onset ◽  
Glioneuronal Tumors ◽  
Patients With Epilepsy

Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy.Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study.Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD.Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.

scholarly journals Generative tensor network classification model for supervised machine learning

2020 ◽  
Vol 101 (7) ◽  
Author(s):  
Zheng-Zhi Sun ◽  
Cheng Peng ◽  
Ding Liu ◽  
Shi-Ju Ran ◽  
Gang Su
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Tensor Network

scholarly journals Use of Paper Microdevices in the Identification and Quantification of Cocaine in Seized Street Samples

2021 ◽  
Author(s):  
Jadson dos Reis ◽  
Wanderson Romão
Keyword(s):  
Response Time ◽  
Illicit Drugs ◽  
Low Cost ◽  
Principal Component ◽  
Spot Size ◽  
Experimental Planning ◽  
Non Invasive ◽  
Factorial Model ◽  
Cocaine Hydrochloride ◽  
Analyte Detection

The growing consumption of illicit drugs in Brazil is becoming increasingly problematic for society. It is therefore critical to develop technologies to combat drug trafficking that allow for rapid, non-invasive evaluation of drug samples. Microfluidics is a technology that manipulates and studies small amounts of fluids, using structures with dimensions from ten to hundreds of micrometers (microdevices). The main advantages of microfluidic approaches are its low cost, speed, and ability to provide results in loco. Here, paper microfluidics were developed to perform the modified Scott test to calculate the cocaine hydrochloride content in seized samples of cocaine (n = 30) and crack (n = 30). A smartphone with the Photometrix® app was used to construct a model for quantifying the samples. A factorial model was developed to optimize microfluidic analytical parameters such as spot size (6, 8 and 10 mm), reagent content (50, 75, and 100% cobalt thiocyanate II), cocaine hydrochloride concentration (4, 6 and 8 mg mL-1) and response time (or analyte detection; t = 0, 0.5 1, 12 and 24 h). After experimental planning, a diameter of ΜPADs = 8 mm - [Co(SCN)2] = 100% and a 1 h response time were identified as the best conditions. We observed that the cocaine hydrochloride concentration did not influence the model. A sample concentration of 15 mg mL-1 was used to quantify cocaine hydrochloride in street samples apprehended by the Forensic Police of Espírito Santo state (with n = 60). The quantification curve constructed to determine the cocaine hydrochloride concentration showed a determination coefficient, R2, of 0.98246 and RMSEC (root mean squares error calibration - mean square error of the calibration) of 0.39480, with a LOD and LOQ of 0.09 and 0.30 mg mL-1, respectively. For the crack samples, the cocaine hydrochloride concentrations ranged from 2.5 to 60.8 wt% with an average purity content of 21.3 ± 13.3 wt%. For the seized cocaine samples, variation in hydrochloride content from 1.2 to 22.6 wt% was observed with a mean percentage of 14.19 ± 6.92 wt%. Finally, chemometric tools such as principal component analysis were used to assess the similarity among the samples.

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