Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis

RationaleTuberculosis diagnosis in children remains challenging. Microbiological confirmation of tuberculosis disease is often lacking, and standard immunodiagnostic including the tuberculin skin test and interferon-γ release assay for tuberculosis infection has limited sensitivity. Recent research suggests that inclusion of novel Mycobacterium tuberculosis antigens has the potential to improve standard immunodiagnostic tests for tuberculosis.ObjectiveTo identify optimal antigen–cytokine combinations using novel Mycobacterium tuberculosis antigens and cytokine read-outs by machine learning algorithms to improve immunodiagnostic assays for tuberculosis.MethodsA total of 80 children undergoing investigation of tuberculosis were included (15 confirmed tuberculosis disease, five unconfirmed tuberculosis disease, 28 tuberculosis infection and 32 unlikely tuberculosis). Whole blood was stimulated with 10 novel Mycobacterium tuberculosis antigens and a fusion protein of early secretory antigenic target (ESAT)-6 and culture filtrate protein (CFP) 10. Cytokines were measured using xMAP multiplex assays. Machine learning algorithms defined a discriminative classifier with performance measured using area under the receiver operating characteristics.Measurements and main resultsWe found the following four antigen–cytokine pairs had a higher weight in the discriminative classifier compared to the standard ESAT-6/CFP-10-induced interferon-γ: Rv2346/47c- and Rv3614/15c-induced interferon-gamma inducible protein-10; Rv2031c-induced granulocyte-macrophage colony-stimulating factor and ESAT-6/CFP-10-induced tumor necrosis factor-α. A combination of the 10 best antigen–cytokine pairs resulted in area under the curve of 0.92 ± 0.04.ConclusionWe exploited the use of machine learning algorithms as a key tool to evaluate large immunological datasets. This identified several antigen–cytokine pairs with the potential to improve immunodiagnostic tests for tuberculosis in children.

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Using machine learning algorithms to predict response and toxicity to immune checkpoint inhibitors (ICIs) in melanoma patients.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.2581 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 2581-2581 ◽

Cited By ~ 1

Author(s):

Paul Johannet ◽

Nicolas Coudray ◽

George Jour ◽

Douglas MacArthur Donnelly ◽

Shirin Bajaj ◽

...

Keyword(s):

Machine Learning ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitors ◽

Predictive Accuracy ◽

Checkpoint Inhibitors ◽

Learning Algorithms ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Operating Characteristics ◽

Melanoma Patients

2581 Background: There is growing interest in optimizing patient selection for treatment with immune checkpoint inhibitors (ICIs). We postulate that phenotypic features present in metastatic melanoma tissue reflect the biology of tumor cells, immune cells, and stromal tissue, and hence can provide predictive information about tumor behavior. Here, we test the hypothesis that machine learning algorithms can be trained to predict the likelihood of response and/or toxicity to ICIs. Methods: We examined 124 stage III/IV melanoma patients who received anti-CTLA-4 (n = 81), anti-PD-1 (n = 25), or combination (n = 18) therapy as first line. The tissue analyzed was resected before treatment with ICIs. In total, 340 H&E slides were digitized and annotated for three regions of interest: tumor, lymphocytes, and stroma. The slides were then partitioned into training (n = 285), validation (n = 26), and test (n = 29) sets. Slides were tiled (299x299 pixels) at 20X magnification. We trained a deep convolutional neural network (DCNN) to automatically segment the images into each of the three regions and then deconstruct images into their component features to detect non-obvious patterns with objectivity and reproducibility. We then trained the DCNN for two classifications: 1) complete/partial response versus progression of disease (POD), and 2) severe versus no immune-related adverse events (irAEs). Predictive accuracy was estimated by area under the curve (AUC) of receiver operating characteristics (ROC). Results: The DCNN identified tumor within LN with AUC 0.987 and within ST with AUC 0.943. Prediction of POD based on ST-only always performed better than prediction based on LN-only (AUC 0.84 compared to 0.61, respectively). The DCNN had an average AUC 0.69 when analyzing only tumor regions from both LN and ST data sets and AUC 0.68 when analyzing tumor and lymphocyte regions. Severe irAEs were predicted with limited accuracy (AUC 0.53). Conclusions: Our results support the potential application of machine learning on pre-treatment histologic slides to predict response to ICIs. It also revealed their limited value in predicting toxicity. We are currently investigating whether the predictive capability of the algorithm can be further improved by incorporating additional immunologic biomarkers.

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

Information ◽

10.3390/info13010035 ◽

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.

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COMPUTATIONAL STUDY ON THE RUPTURE RISK IN REAL CEREBRAL ANEURYSMS WITH GEOMETRICAL AND FLUID-MECHANICAL PARAMETERS USING FSI SIMULATIONS AND MACHINE LEARNING ALGORITHMS

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519419500143 ◽

2019 ◽

Vol 19 (03) ◽

pp. 1950014

Author(s):

ALFREDO ARANDA ◽

ALVARO VALENCIA

Keyword(s):

Machine Learning ◽

Computational Study ◽

Learning Algorithms ◽

Area Under The Curve ◽

Cerebral Aneurysms ◽

Machine Learning Algorithms ◽

Maximum Height ◽

Rupture Risk ◽

Relative Residence Time ◽

Von Mises

Fluid-mechanical and morphological parameters are recognized as major factors in the rupture risk of human aneurysms. On the other hand, it is well known that a lot of machine learning tools are available to study a variety of problems in many fields. In this work, fluid–structure interaction (FSI) simulations were carried out to examine a database of 60 real saccular cerebral aneurysms (30 ruptured and 30 unruptured) using reconstructions by angiography images. With the results of the simulations and geometric analyses, we studied the analysis of variance (ANOVA) statistic test in many variables and we obtained that aspect ratio (AR), bottleneck factor (BNF), maximum height of the aneurysms (MH), relative residence time (RRT), Womersley number (WN) and Von-Mises strain (VMS) are statically significant and good predictors for the models. In consequence, these ones were used in five machine learning algorithms to determine the rupture risk predictions of the aneurysms, where the adaptative boosting (AdaBoost) was calculated with the highest area under the curve (AUC) in the receiver operating characteristic (ROC) curve (AUC 0.944).

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Radiomic Features and Machine Learning for the Discrimination of Renal Tumor Histological Subtypes: A Pragmatic Study Using Clinical-Routine Computed Tomography

Cancers ◽

10.3390/cancers12103010 ◽

2020 ◽

Vol 12 (10) ◽

pp. 3010

Author(s):

Johannes Uhlig ◽

Andreas Leha ◽

Laura M. Delonge ◽

Anna-Maria Haack ◽

Brian Shuch ◽

...

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Renal Tumor ◽

Learning Algorithms ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Clinical Routine ◽

Tumor Subtypes ◽

Chromophobe Rcc ◽

Venous Phase

This study evaluates the diagnostic performance of radiomic features and machine learning algorithms for renal tumor subtype assessment in venous computed tomography (CT) studies from clinical routine. Patients undergoing surgical resection and histopathological assessment of renal tumors at a tertiary referral center between 2012 and 2019 were included. Preoperative venous-phase CTs from multiple referring imaging centers were segmented, and standardized radiomic features extracted. After preprocessing, class imbalance handling, and feature selection, machine learning algorithms were used to predict renal tumor subtypes using 10-fold cross validation, assessed as multiclass area under the curve (AUC). In total, n = 201 patients were included (73.7% male; mean age 66 ± 11 years), with n = 131 clear cell renal cell carcinomas (ccRCC), n = 29 papillary RCC, n = 11 chromophobe RCC, n = 16 oncocytomas, and n = 14 angiomyolipomas (AML). An extreme gradient boosting algorithm demonstrated the highest accuracy (multiclass area under the curve (AUC) = 0.72). The worst discrimination was evident for oncocytomas vs. AML and oncocytomas vs. chromophobe RCC (AUC = 0.55 and AUC = 0.45, respectively). In sensitivity analyses excluding oncocytomas, a random forest algorithm showed the highest accuracy, with multiclass AUC = 0.78. Radiomic feature analyses from venous-phase CT acquired in clinical practice with subsequent machine learning can discriminate renal tumor subtypes with moderate accuracy. The classification of oncocytomas seems to be the most complex with the lowest accuracy.

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PREDICTING SOFTWARE CHANGE IN AN OPEN SOURCE SOFTWARE USING MACHINE LEARNING ALGORITHMS

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539313500253 ◽

2013 ◽

Vol 20 (06) ◽

pp. 1350025

Author(s):

RUCHIKA MALHOTRA ◽

ANKITA JAIN BANSAL

Keyword(s):

Machine Learning ◽

Open Source ◽

Open Source Software ◽

Roc Analysis ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Operating Characteristics ◽

Preventive Actions ◽

Change Proneness ◽

The Relationship

Due to various reasons such as ever increasing demands of the customer or change in the environment or detection of a bug, changes are incorporated in a software. This results in multiple versions or evolving nature of a software. Identification of parts of a software that are more prone to changes than others is one of the important activities. Identifying change prone classes will help developers to take focused and timely preventive actions on the classes of the software with similar characteristics in the future releases. In this paper, we have studied the relationship between various object oriented (OO) metrics and change proneness. We collected a set of OO metrics and change data of each class that appeared in two versions of an open source dataset, 'Java TreeView', i.e., version 1.1.6 and version 1.0.3. Besides this, we have also predicted various models that can be used to identify change prone classes, using machine learning and statistical techniques and then compared their performance. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the models predicted using both machine learning and statistical methods demonstrate good performance in terms of predicting change prone classes. Based on the results, it is reasonable to claim that quality models have a significant relevance with OO metrics and hence can be used by researchers for early prediction of change prone classes.

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Machine Learning Algorithms Using Logistic Regression for Predicting Neurosurgical Outcomes

10.21203/rs.3.rs-37934/v1 ◽

2020 ◽

Author(s):

Nida Fatima

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Predictive Model ◽

Web Application ◽

Learning Algorithms ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Brier Score ◽

Patient Counselling ◽

Selection Of Variables

Abstract Background: Preoperative prognostication of clinical and surgical outcome in patients with neurosurgical diseases can improve the risk stratification, thus can guide in implementing targeted treatment to minimize these events. Therefore, the author aims to highlight the development and validation of predictive models determining neurosurgical outcomes through machine learning algorithms using logistic regression.Methods: Logistic regression (enter, backward and forward) and least absolute shrinkage and selection operator (LASSO) method for selection of variables from selected database can eventually lead to multiple candidate models. The final model with a set of predictive variables must be selected based upon the clinical knowledge and numerical results.Results: The predictive model which performed best on the discrimination, calibration, Brier score and decision curve analysis must be selected to develop machine learning algorithms. Logistic regression should be compared with the LASSO model. Usually for the big databases, the predictive model selected through logistic regression gives higher Area Under the Curve (AUC) than those with LASSO model. The predictive probability derived from the best model could be uploaded to an open access web application which is easily deployed by the patients and surgeons to make a risk assessment world-wide.Conclusions: Machine learning algorithms provide promising results for the prediction of outcomes following cranial and spinal surgery. These algorithms can provide useful factors for patient-counselling, assessing peri-operative risk factors, and predicting post-operative outcomes after neurosurgery.

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Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer

Scientific Reports ◽

10.1038/s41598-020-71927-6 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Bum-Joo Cho ◽

Kyoung Min Kim ◽

Sanchir-Erdene Bilegsaikhan ◽

Yong Joon Suh

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Risk Factors ◽

Febrile Neutropenia ◽

Prediction Models ◽

Learning Algorithms ◽

Area Under The Curve ◽

Primary Prophylaxis ◽

Machine Learning Algorithms ◽

Significant Difference

Abstract Febrile neutropenia (FN) is one of the most concerning complications of chemotherapy, and its prediction remains difficult. This study aimed to reveal the risk factors for and build the prediction models of FN using machine learning algorithms. Medical records of hospitalized patients who underwent chemotherapy after surgery for breast cancer between May 2002 and September 2018 were selectively reviewed for development of models. Demographic, clinical, pathological, and therapeutic data were analyzed to identify risk factors for FN. Using machine learning algorithms, prediction models were developed and evaluated for performance. Of 933 selected inpatients with a mean age of 51.8 ± 10.7 years, FN developed in 409 (43.8%) patients. There was a significant difference in FN incidence according to age, staging, taxane-based regimen, and blood count 5 days after chemotherapy. The area under the curve (AUC) built based on these findings was 0.870 on the basis of logistic regression. The AUC improved by machine learning was 0.908. Machine learning improves the prediction of FN in patients undergoing chemotherapy for breast cancer compared to the conventional statistical model. In these high-risk patients, primary prophylaxis with granulocyte colony-stimulating factor could be considered.

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Evaluation of machine learning algorithms for detection of road induced shocks buried in vehicle vibration signals

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407018756201 ◽

2018 ◽

Vol 233 (4) ◽

pp. 935-947 ◽

Cited By ~ 3

Author(s):

Julien Lepine ◽

Vincent Rouillard ◽

Michael Sek

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Harmful Effect ◽

Area Under The Curve ◽

Detection Algorithm ◽

Machine Learning Algorithms ◽

Gaussian Kernel ◽

Support Vector ◽

Structural Fatigue ◽

Road Surfaces

Road surface imperfections and aberrations generate shocks causing vehicles to sustain structural fatigue and functional defects, driver and passenger discomfort, injuries, and damage to freight. The harmful effect of shocks can be mitigated at different levels, for example, by improving road surfaces, vehicle suspension and protective packaging of freight. The efficiency of these methods partly depends on the identification and characterisation of the shocks. An assessment of four machine learning algorithms (Classifiers) that can be used to identify shocks produced on different roads and test tracks is presented in this paper. The algorithms were trained using synthetic signals. These were created from a model made from acceleration measurements on a test vehicle. The trained Classifiers were assessed on different measurement signals made on the same vehicle. The results show that the Support Vector Machine detection algorithm used in conjunction with a Gaussian Kernel Transform can accurately detect shocks generated on the test track with an area under the curve (AUC) of 0.89 and a Pseudo Energy Ratio Fall-Out (PERFO) of 8%.

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Improving Diabetic Diagnosis and Prevention with Machine Learning on Retinal Imaging

E3S Web of Conferences ◽

10.1051/e3sconf/202127101034 ◽

2021 ◽

Vol 271 ◽

pp. 01034

Author(s):

Yushan Min

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Diabetic Retinopathy ◽

Learning Algorithms ◽

Area Under The Curve ◽

Retinal Imaging ◽

Machine Learning Algorithms ◽

Support Vector ◽

Retinal Images ◽

Support Vector Machine Algorithm

If the retinal images show evidences of abnormalities such as change in volume, diameter, and unusual spots in the retina, then there is a positive correlation to the diabetic progress. Mathematical and statistical theories behind the machine learning algorithms are powerful enough to detect signs of diabetes through retinal images. Several machine learning algorithms: Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks were applied to predict whether images contain signs of diabetic retinopathy or not. After building the models, the computed results of these algorithms were compared by confusion matrixes, receiver operating characteristic curves, and Precision-Recall curves. The performance of the Support Vector Machine algorithm was the best since it had the highest true-positive rate, area under the curve for ROC curve, and area under the curve for Precision-Recall curve. This conclusion shows that the most complex algorithms doesn’t always give the best performance, the final accuracy also depends on the dataset. For this dataset of retinal imaging, the Support Vector Machine algorithm achieved the best results. Detecting signs of diabetic retinopathy is helpful for detecting for diabetes since more than 60% of patients with diabetes have signs of diabetic retinopathy. Machine learning algorithms can speed up the process and improve the accuracy of diagnosis. When the method is reliable enough, it can be utilized in diabetes diagnosis directly in clinics. Current methods require going on diets and taking blood samples, which could be very time consuming and inconvenient. Using machine learning algorithms is fast and noninvasive compared to the existing methods. The purpose of this research was to build an optimized model by machine learning algorithms that can improve the diagnosis accuracy and classification of patients at high risk of diabetes using retinal imaging.

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Landslide Susceptibility Assessment Using an AutoML Framework

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182010971 ◽

2021 ◽

Vol 18 (20) ◽

pp. 10971

Author(s):

Adrián G. Bruzón ◽

Patricia Arrogante-Funes ◽

Fátima Arrogante-Funes ◽

Fidel Martín-González ◽

Carlos J. Novillo ◽

...

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Spatial Databases ◽

Learning Algorithms ◽

Area Under The Curve ◽

Remote Sensing Data ◽

Machine Learning Algorithms ◽

Sources Of Information ◽

Natural Catastrophes ◽

Input Variables

The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides.

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