scholarly journals Early Diagnosis of Necrotizing Enterocolitis in Neonates Using an Electronic Nose

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mira Prabhakar ◽  
Troy Markel
Keyword(s):  
Machine Learning ◽  
Necrotizing Enterocolitis ◽  
Electronic Nose ◽  
Foley Catheter ◽  
Machine Learning Algorithms ◽  
National Institutes Of Health ◽  
Sample Collection ◽  
Data Set ◽  
Number Of Patients ◽  
Significant Difference

Background: Necrotizing enterocolitis (NEC) is a devastating clinical problem that often necessitates resection of necrotic intestine, leaving infants with a suboptimal length of intestine to absorb nutrition. There are no adequate tests or biomarkers to predict NEC onset. We hypothesized that assessment of biological samples with a novel electronic nose can be used with machine learning algorithms to detect aberrancies in stool and urine volatile organic compounds to predict NEC risk.  Methods: 18 infants of gestational age ≤34 weeks in the Riley Hospital NICU were enrolled in the study and underwent stool (38 control, 3 NEC) and urine (42 control, 5 NEC) sample collection. Stool was collected by the bedside nurse via the infant’s diaper or ostomy bag. Urine was collected using a bag around the infant’s perineum or via foley catheter if already present. Stool samples and 250uL urine samples were aliquoted into Eppendorf tubes and covered with Parafilm. Samples were heated to 40°C, 30 minutes for stool, and 10 minutes for urine. Sample headspace was analyzed using the Cyranose 320 electronic nose, creating a “smellprint” comprised of readings from 32 unique sensors. Individual sensors were compared using Mann-Whitney U test.  P<0.05 was significant.  Results: There was no significant difference in urine or stool signals among the 32 sensors between NEC and control groups.   Potential Impact: While no significant difference was found, this study is greatly limited by the number of patients enrolled, with only 2 of the 18 being diagnosed with NEC. Past retrospective studies with this device have found differences between NEC and non-NEC stool. The future goals of this study are to continue enrolling patients to have a more robust data set. Using machine learning, we aim to create a model to predict NEC before its clinical manifestation so that beneficial treatment can be initiated earlier.    Acknowledgment: This project was funded, in part, with support from the NIH NHLBI Short-Term Training Program in Biomedical Sciences Grant funded, in part by T35HL110854 from the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 

Prediction ability of machine learning algorithms in Himalaya region of Pakistan for landslide susceptibility mapping

2020 ◽  
Author(s):  
Naeem Shahzad ◽  
Xiaoli Ding ◽  
Sawaid Abbas
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Information Gain ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Data Set ◽  
Prediction Ability ◽  
Significant Difference

&lt;p&gt;Machine learning has proven most effective in mapping landslide susceptibility. We carry out experiments with two machine learning algorithms, SVM and MaxENT to study their effectiveness for some mountaneous areas in Pakistan. A data set of 112 historic landslides are used in the study with 70% of the landslides are used for training and the rest for validation. 15 landslide casuative factors are used initially and ineffective ones are eliminated based on information Gain Ratio and Multicollinearity test techniques.&amp;#160; The perfromances of the landslides susceptibility maps generated are assessed using receiver operating curves (ROC), confusion matrix (CM) (Kappa, root mean square error, mean absolute error and balanced accuracy), landslide density (LD), R-index and Pearson&amp;#8217;s Chi-squared tests. The result show that both of the models work well in this area. However, the lowest significant value &amp;#8216;p&amp;#8217; (&lt;0.05) during Chi-square test, showed that both the landslide models have statistical significant difference.&lt;/p&gt;

scholarly journals Adverse reactions of targeted therapy in cancer patients: a retrospective study of hospital medical data in China

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ruofei Du ◽  
Xin Wang ◽  
Lixia Ma ◽  
Leon M. Larcher ◽  
Han Tang ◽  
...  
Keyword(s):  
Targeted Therapy ◽  
Cancer Patients ◽  
Adverse Reactions ◽  
Cox Regression ◽  
Target Therapy ◽  
Skin Damage ◽  
Data Set ◽  
Patients With Cancer ◽  
Number Of Patients ◽  
Significant Difference

Abstract Background The adverse reactions (ADRs) of targeted therapy were closely associated with treatment response, clinical outcome, quality of life (QoL) of patients with cancer. However, few studies presented the correlation between ADRs of targeted therapy and treatment effects among cancer patients. This study was to explore the characteristics of ADRs with targeted therapy and the prognosis of cancer patients based on the clinical data. Methods A retrospective secondary data analysis was conducted within an ADR data set including 2703 patients with targeted therapy from three Henan medical centers of China between January 2018 and December 2019. The significance was evaluated with chi-square test between groups with or without ADRs. Univariate and multivariate logistic regression with backward stepwise method were applied to assess the difference of pathological characteristics in patients with cancer. Using the univariate Cox regression method, the actuarial probability of overall survival was performed to compare the clinical outcomes between these two groups. Results A total of 485 patients were enrolled in this study. Of all patients, 61.0% (n = 296) occurred ADRs including skin damage, fatigue, mucosal damage, hypertension and gastrointestinal discomfort as the top 5 complications during the target therapy. And 62.1% of ADRs were mild to moderate, more than half of the ADRs occurred within one month, 68.6% ADRs lasted more than one month. Older patients (P = 0.022) and patients with lower education level (P = 0.036), more than 2 comorbidities (P = 0.021), longer medication time (P = 0.022), drug combination (P = 0.033) and intravenous administration (P = 0.019) were more likely to have ADRs. Those with ADRs were more likely to stop taking (P = 0.000), change (P = 0.000), adjust (P = 0.000), or not take the medicine on time (P = 0.000). The number of patients with recurrence (P = 0.000) and metastasis (P = 0.006) were statistically significant difference between ADRs and non-ADRs group. And the patients were significantly poor prognosis in ADRs groups compared with non-ADRs group. Conclusion The high incidence of ADRs would affect the treatment and prognosis of patients with cancer. We should pay more attention to these ADRs and develop effective management strategies.

Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology

2021 ◽  
Vol 30 (1) ◽  
pp. 460-469
Author(s):  
Yinying Cai ◽  
Amit Sharma
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Research Result ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Data Set ◽  
Rice Pests ◽  
Machine Leaning ◽  
Smart Agriculture ◽  
Swarm Intelligence Optimization

Abstract In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

scholarly journals Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set

2004 ◽  
Vol 8 (4) ◽  
pp. 403-415 ◽  
Author(s):  
Maheshkumar Sabhnani ◽  
Gursel Serpen
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Misuse Detection ◽  
Data Set

scholarly journals Birds Sound Classification Based on Machine Learning Algorithms

2021 ◽  
pp. 1-11
Author(s):  
Aska E. Mehyadin ◽  
Adnan Mohsin Abdulazeez ◽  
Dathar Abas Hasan ◽  
Jwan N. Saeed
Keyword(s):  
Machine Learning ◽  
Noise Suppression ◽  
Bird Species ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Learning Technology ◽  
Species Classification ◽  
Data Set ◽  
Sound Classification ◽  
Mel Frequency Cepstral Coefficient

The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

scholarly journals Identifying those at risk of reattendance at discharge from emergency departments using explainable machine learning

2020 ◽  
Author(s):  
F. P. Chmiel ◽  
M. Azor ◽  
F. Borca ◽  
M. J. Boniface ◽  
D. K. Burns ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Emergency Departments ◽  
Risk Groups ◽  
Machine Learning Algorithms ◽  
Care Indicator ◽  
Number Of Patients ◽  
Increased Risk ◽  
Single Centre Study ◽  
Patients At Risk

ABSTRACTShort-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance can help reduce the number of patients with missed or undertreated illness or injury, and could support appropriate discharges with focused interventions. In this manuscript we present a retrospective, single-centre study where we create and evaluate a machine-learnt classifier trained to identify patients at risk of reattendance within 72 hours of discharge from an emergency department. On a patient hold-out test set, our highest performing classifier obtained an AUROC of 0.748 and an average precision of 0.250; demonstrating that machine-learning algorithms can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. In parallel to our predictive model we train an explanation model, capable of explaining predictions at an attendance level, which can be used to help inform the design of interventional strategies.

scholarly journals PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE

2020 ◽  
Vol 10 (3) ◽  
pp. 32-35
Author(s):  
Jakub Gęca
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Model Parameters ◽  
Data Set ◽  
Reduction Techniques ◽  
Machine Reliability ◽  
Dimensionality Reduction Techniques

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.

scholarly journals Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 34
Author(s):  
Giovanna Sannino ◽  
Ivanoe De Falco ◽  
Giuseppe De Pietro
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Risk Stratification ◽  
Learning Algorithms ◽  
Circulatory System ◽  
Machine Learning Algorithms ◽  
Learning Mechanisms ◽  
Data Set ◽  
Non Invasive ◽  
Blood Pressure Estimation

One of the most important physiological parameters of the cardiovascular circulatory system is Blood Pressure. Several diseases are related to long-term abnormal blood pressure, i.e., hypertension; therefore, the early detection and assessment of this condition are crucial. The identification of hypertension, and, even more the evaluation of its risk stratification, by using wearable monitoring devices are now more realistic thanks to the advancements in Internet of Things, the improvements of digital sensors that are becoming more and more miniaturized, and the development of new signal processing and machine learning algorithms. In this scenario, a suitable biomedical signal is represented by the PhotoPlethysmoGraphy (PPG) signal. It can be acquired by using a simple, cheap, and wearable device, and can be used to evaluate several aspects of the cardiovascular system, e.g., the detection of abnormal heart rate, respiration rate, blood pressure, oxygen saturation, and so on. In this paper, we take into account the Cuff-Less Blood Pressure Estimation Data Set that contains, among others, PPG signals coming from a set of subjects, as well as the Blood Pressure values of the latter that is the hypertension level. Our aim is to investigate whether or not machine learning methods applied to these PPG signals can provide better results for the non-invasive classification and evaluation of subjects’ hypertension levels. To this aim, we have availed ourselves of a wide set of machine learning algorithms, based on different learning mechanisms, and have compared their results in terms of the effectiveness of the classification obtained.

scholarly journals Spoken words as biomarkers: using machine learning to gain insight into communication as a predictor of anxiety

2020 ◽  
Vol 27 (6) ◽  
pp. 929-933
Author(s):  
George Demiris ◽  
Kristin L Corey Magan ◽  
Debra Parker Oliver ◽  
Karla T Washington ◽  
Chad Chadwick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Secondary Data ◽  
Health Indicators ◽  
Machine Learning Algorithms ◽  
Standardized Assessments ◽  
Learning Tools ◽  
Data Set ◽  
Problem Solving Therapy ◽  
Audio Communication ◽  
The Impact

Abstract Objective The goal of this study was to explore whether features of recorded and transcribed audio communication data extracted by machine learning algorithms can be used to train a classifier for anxiety. Materials and Methods We used a secondary data set generated by a clinical trial examining problem-solving therapy for hospice caregivers consisting of 140 transcripts of multiple, sequential conversations between an interviewer and a family caregiver along with standardized assessments of anxiety prior to each session; 98 of these transcripts (70%) served as the training set, holding the remaining 30% of the data for evaluation. Results A classifier for anxiety was developed relying on language-based features. An 86% precision, 78% recall, 81% accuracy, and 84% specificity were achieved with the use of the trained classifiers. High anxiety inflections were found among recently bereaved caregivers and were usually connected to issues related to transitioning out of the caregiving role. This analysis highlighted the impact of lowering anxiety by increasing reciprocity between interviewers and caregivers. Conclusion Verbal communication can provide a platform for machine learning tools to highlight and predict behavioral health indicators and trends.

A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA41-WA52 ◽  
Author(s):  
Dario Grana ◽  
Leonardo Azevedo ◽  
Mingliang Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Seismic Data ◽  
Transition Probabilities ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Facies Classification ◽  
The Neural Network

Among the large variety of mathematical and computational methods for estimating reservoir properties such as facies and petrophysical variables from geophysical data, deep machine-learning algorithms have gained significant popularity for their ability to obtain accurate solutions for geophysical inverse problems in which the physical models are partially unknown. Solutions of classification and inversion problems are generally not unique, and uncertainty quantification studies are required to quantify the uncertainty in the model predictions and determine the precision of the results. Probabilistic methods, such as Monte Carlo approaches, provide a reliable approach for capturing the variability of the set of possible models that match the measured data. Here, we focused on the classification of facies from seismic data and benchmarked the performance of three different algorithms: recurrent neural network, Monte Carlo acceptance/rejection sampling, and Markov chain Monte Carlo. We tested and validated these approaches at the well locations by comparing classification predictions to the reference facies profile. The accuracy of the classification results is defined as the mismatch between the predictions and the log facies profile. Our study found that when the training data set of the neural network is large enough and the prior information about the transition probabilities of the facies in the Monte Carlo approach is not informative, machine-learning methods lead to more accurate solutions; however, the uncertainty of the solution might be underestimated. When some prior knowledge of the facies model is available, for example, from nearby wells, Monte Carlo methods provide solutions with similar accuracy to the neural network and allow a more robust quantification of the uncertainty, of the solution.

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