scholarly journals Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lingling Zheng ◽  
Fangqin Lin ◽  
Changxi Zhu ◽  
Guangjian Liu ◽  
Xiaohui Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bacterial Infections ◽  
Learning Algorithms ◽  
Intestinal Failure ◽  
Diagnostic Value ◽  
Machine Learning Algorithms ◽  
Respiratory Disorder ◽  
Clinical Indicators ◽  
Auc Value ◽  
Sensitivity Specificity

Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis. Metabolomic changes triggered by microbial activity provide us with the possibility of accurately identifying infection. We adopted machine learning methods for training different classifiers with a clinical-metabolomic database from sepsis cases to identify the pathogen of sepsis. Records of clinical indicators and concentration of metabolites were obtained for each patient upon their arrival at the hospital. Machine learning algorithms were used in 100 patients with clear infection and corresponding 29 controls to select specific biosignatures to discriminate microorganism in septic patients. The sensitivity, specificity, and AUC value of clinical and metabolomic characteristics in predicting diagnostic outcomes were determined at admission. Our analyses demonstrate that the biosignatures selected by machine learning algorithms could have diagnostic value on the identification of infected patients and Gram-positive from Gram-negative; related AUC values were 0.94±0.054 and 0.80±0.085, respectively. Pathway and blood disease enrichment analyses of clinical and metabolomic biomarkers among infected patients showed that sepsis disease was accompanied by abnormal nitrogen metabolism, cell respiratory disorder, and renal or intestinal failure. The panel of selected clinical and metabolomic characteristics might be powerful biomarkers to discriminate patients with sepsis.

scholarly journals Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Jibouni Ayoub ◽  
Dounia Lotfi ◽  
Ahmed Hammouch
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Actual State ◽  
The Future ◽  
Auc Value ◽  
The Mean ◽  
Analysis Of Social Networks

The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

scholarly journals Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections

2017 ◽  
Vol 92 (1) ◽  
pp. 179-191 ◽  
Author(s):  
Jingjing Zhang ◽  
Ida M. Friberg ◽  
Ann Kift-Morgan ◽  
Gita Parekh ◽  
Matt P. Morgan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Peritoneal Dialysis ◽  
Bacterial Infections ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Dialysis Patients

Predictive Strength of Ensemble Machine Learning Algorithms for the Diagnosis of Large Scale Medical Datasets

2021 ◽  
pp. 260-281
Author(s):  
Elangovan Ramanujam ◽  
L. Rasikannan ◽  
S. Viswa ◽  
B. Deepan Prashanth
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Weather Forecast ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Ensemble Machine Learning ◽  
Simple Technology ◽  
Sensitivity Specificity

Machine learning is not a simple technology but an amazing field having more and more to explore. It has a number of real-time applications such as weather forecast, price prediction, gaming, medicine, fraud detection, etc. Machine learning has an increased usage in today's technological world as data is growing in volumes and machine learning is capable of producing mathematical and statistical models that can analyze complex data and generate accurate results. To analyze the scalable performance of the learning algorithms, this chapter utilizes various medical datasets from the UCI Machine Learning repository ranges from smaller to large datasets. The performance of learning algorithms such as naïve Bayes, decision tree, k-nearest neighbor, and stacking ensemble learning method are compared in different evaluation models using metrics such as accuracy, sensitivity, specificity, precision, and f-measure.

scholarly journals Prediction of hyperaldosteronism subtypes when adrenal vein sampling is unilaterally successful

2020 ◽  
Vol 183 (6) ◽  
pp. 657-667
Author(s):  
Jacopo Burrello ◽  
Alessio Burrello ◽  
Jacopo Pieroni ◽  
Elisa Sconfienza ◽  
Vittorio Forestiero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Ct Scanning ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Score System ◽  
Contralateral Suppression ◽  
Diagnostic Models ◽  
Sensitivity Specificity

Objective Adrenal venous sampling (AVS) is the gold standard to discriminate patients with unilateral primary aldosteronism (UPA) from bilateral disease (BPA). AVS is technically demanding and in cases of unsuccessful cannulation of adrenal veins, the results may not always be interpreted. The aim of our study was to develop diagnostic models to distinguish UPA from BPA, in cases of unilateral successful AVS and the presence of contralateral suppression of aldosterone secretion. Design Retrospective evaluation of 158 patients referred to a tertiary hypertension unit who underwent AVS. We randomly assigned 110 patients to a training cohort and 48 patients to a validation cohort to develop and test the diagnostic models. Methods Supervised machine learning algorithms and regression models were used to develop and validate two prediction models and a simple 19-point score system to stratify patients according to their subtype diagnosis. Results Aldosterone levels at screening and after confirmatory testing, lowest potassium, ipsilateral and contralateral imaging findings at CT scanning, and contralateral ratio at AVS, were associated with a diagnosis of UPA and were included in the diagnostic models. Machine learning algorithms correctly classified the majority of patients both at training and validation (accuracy: 82.9–95.7%). The score system displayed a sensitivity/specificity of 95.2/96.9%, with an AUC of 0.971. A flow-chart integrating our score correctly managed all patients except 3 (98.1% accuracy), avoiding the potential repetition of 77.2% of AVS procedures. Conclusions Our score could be integrated in clinical practice and guide surgical decision-making in patients with unilateral successful AVS and contralateral suppression.

scholarly journals Potential of machine learning methods to identify patients with nonvalvular atrial fibrillation

2020 ◽  
Vol 16 (1) ◽  
pp. 43-52
Author(s):  
Ryoko Suzuki ◽  
Jun Katada ◽  
Sreeram Ramagopalan ◽  
Laura McDonald
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Nonvalvular Atrial Fibrillation ◽  
New Approach ◽  
Increased Risk ◽  
Sensitivity Specificity ◽  
Retrospective Database

Aim: Nonvalvular atrial fibrillation (NVAF) is associated with an increased risk of stroke however many patients are diagnosed after onset. This study assessed the potential of machine-learning algorithms to detect NVAF. Materials & methods: A retrospective database study using a Japanese claims database. Patients with and without NVAF were selected. 41 variables were included in different classification algorithms. Results: Machine learning algorithms identified NVAF with an area under the curve of >0.86; corresponding sensitivity/specificity was also high. The stacking model which combined multiple algorithms outperformed single-model approaches (area under the curve ≥0.90, sensitivity/specificity ≥0.80/0.82), although differences were small. Conclusion: Machine-learning based algorithms can detect atrial fibrillation with accuracy. Although additional validation is needed, this methodology could encourage a new approach to detect NVAF.

AUTOMATED DIAGNOSIS IN A POPULATION-BASED SCREENING FOR LUNG CANCER

2017 ◽  
Vol 63 (2) ◽  
pp. 215-220
Author(s):  
Anton Barchuk ◽  
A. Atroshchenko ◽  
V. Gaydukov ◽  
P. Vinogradov ◽  
Sergey Tarakanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Learning Algorithms ◽  
Open Data ◽  
Diagnostic Value ◽  
Population Based ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Tissue Samples ◽  
Diagnostic Studies

Oncologists nowadays are faced with big amount of heterogeneous medical data of diagnostic studies. Possible errors in determining the nature and extent of spread the tumor process will inevitably reduce the effectiveness of treatment and increase the unnecessary costs to it. To reduce the burden on clinicians, various computer-aided solutions based on machine learning algorithms are being developed. We made an attempt to evaluate effectiveness of thirteen machine learning algorithms in the tasks of classification of pathologic tissue samples in cancerous thorax based on gene expression levels. For a preliminary study we used open data set of molecular genetics composition of lung adenocarcinoma and pleural mesothelioma. Effectiveness of machine learning algorithms was evaluated by Matthews correlation coefficient and Area Under ROC Curve. Best results were showed by two methods: Bayesian logistic regression and Discriminative Multinomial Naive Bayes classifier. Nevertheless, all of the methods were effective at automatic discrimination of two types of cancer. That proves machine learning algorithms are applicable in lung cancer classification. In the future studies it will be carried out a similar analysis of the diagnostic value of methods for other malignancies with more complex differential morphological diagnosis. Similar methods can be applied to other diagnostic studies including computerized tomography image analysis in the differential diagnosis of lung nodules.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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