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1.
PLoS One ; 15(10): e0239960, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33017421

RESUMO

The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , Probabilidade , Máquina de Vetores de Suporte , China/epidemiologia , Previsões , Lógica Fuzzy , Humanos , Redes Neurais de Computação , Pandemias , Estados Unidos/epidemiologia
2.
Nat Commun ; 11(1): 5033, 2020 10 06.
Artigo em Inglês | MEDLINE | ID: mdl-33024092

RESUMO

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.


Assuntos
Infecções por Coronavirus/mortalidade , Aprendizado de Máquina , Pandemias , Pneumonia Viral/mortalidade , Idoso , Betacoronavirus , China/epidemiologia , Feminino , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Medição de Risco , Máquina de Vetores de Suporte
3.
BMC Bioinformatics ; 21(Suppl 14): 359, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998692

RESUMO

BACKGROUND: The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as protein-protein interaction (PPI) network, gene co-expression (CE) network and pathway information to identify robust and accurate biomarkers for metastasis prediction, reflecting the common belief that cancer is a systems biology disease. However, controversy exists in the literature regarding whether network markers are indeed better features than genes alone for predicting as well as understanding metastasis. We believe much of the existing results may have been biased by the overly complicated prediction algorithms, unfair evaluation, and lack of rigorous statistics. In this study, we propose a simple approach to use network edges as features, based on two types of networks respectively, and compared their prediction power using three classification algorithms and rigorous statistical procedure on one of the largest datasets available. To detect biomarkers that are significant for the prediction and to compare the robustness of different feature types, we propose an unbiased and novel procedure to measure feature importance that eliminates the potential bias from factors such as different sample size, number of features, as well as class distribution. RESULTS: Experimental results reveal that edge-based feature types consistently outperformed gene-based feature type in random forest and logistic regression models under all performance evaluation metrics, while the prediction accuracy of edge-based support vector machine (SVM) model was poorer, due to the larger number of edge features compared to gene features and the lack of feature selection in SVM model. Experimental results also show that edge features are much more robust than gene features and the top biomarkers from edge feature types are statistically more significantly enriched in the biological processes that are well known to be related to breast cancer metastasis. CONCLUSIONS: Overall, this study validates the utility of edge features as biomarkers but also highlights the importance of carefully designed experimental procedures in order to achieve statistically reliable comparison results.


Assuntos
Biomarcadores Tumorais/metabolismo , Neoplasias da Mama/patologia , Máquina de Vetores de Suporte , Área Sob a Curva , Neoplasias da Mama/genética , Feminino , Redes Reguladoras de Genes/genética , Humanos , Modelos Logísticos , Metástase Neoplásica , Mapas de Interação de Proteínas/genética , Curva ROC
4.
Nat Commun ; 11(1): 5033, 2020 10 06.
Artigo em Inglês | MEDLINE | ID: covidwho-834869

RESUMO

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.


Assuntos
Infecções por Coronavirus/mortalidade , Aprendizado de Máquina , Pandemias , Pneumonia Viral/mortalidade , Idoso , Betacoronavirus , China/epidemiologia , Feminino , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Medição de Risco , Máquina de Vetores de Suporte
5.
PLoS One ; 15(10): e0239960, 2020.
Artigo em Inglês | MEDLINE | ID: covidwho-814640

RESUMO

The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , Probabilidade , Máquina de Vetores de Suporte , China/epidemiologia , Previsões , Lógica Fuzzy , Humanos , Redes Neurais de Computação , Pandemias , Estados Unidos/epidemiologia
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 180-183, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017959

RESUMO

Dengue fever (DF) is a viral infection with possible fatal consequence. NS1 is a recent antigen based biomarker for dengue fever (DF), as an alternative to current serum and antibody based biomarkers. Convolutional Neural Network (CNN) has demonstrated impressive performance in machine learning problems. Our previous research has captured NS1 molecular fingerprint in saliva using Surface Enhanced Raman Spectroscopy (SERS) with great potential as an early, noninvasive detection method. SERS is an enhanced variant of Raman spectroscopy, with extremely high amplification that enables spectra of low concentration matter, such as NS1 in saliva, readable. The spectrum contains 1801 features per sample, at a total of 284 samples. Principal Component Analysis (PCA) transforms high dimensional correlated signal to a lower dimension uncorrelated principal components (PCs), at no sacrifice of the original signal content. This paper aims to unravel an optimal Scree-CNN model for classification of salivary NS1 SERS spectra. Performances of a total of 490 classifier models were examined and compared in terms of performance indicators [accuracy, sensitivity, specificity, precision, kappa] against a WHO recommended clinical standard test for DF, enzyme-linked immunosorbent assay (ELISA). Effects of CNN parameters on performances of the classifier models were also observed. Results showed that Scree-CNN classifier model with learning rate of 0.01, mini-batch size of 64 and validation frequency of 50, reported an across-the-board 100% for all performance indicators.


Assuntos
Redes Neurais de Computação , Proteínas não Estruturais Virais , Sensibilidade e Especificidade , Análise Espectral Raman , Máquina de Vetores de Suporte
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 236-239, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017972

RESUMO

Researchers have been using signal processing based methods to assess speech from Parkinson's disease (PD) patients and identify the contrasting features in comparison to speech from healthy controls (HC). The methodologies follow conventional approach of segmenting speech over a fixed window (≈25ms to 30ms) followed by feature extraction and classification. The proposed methodology uses MFCCs extracted from pitch synchronous and fixed window (25ms) based speech segments for classification using fine Gaussian support vector machines (SVM). Three word utterances with three different vowel sounds are used for this analysis. Clustering experiments are aimed at identifying two clusters and class labels (PD/HC) are assigned based on number of participants from the respective class in the cluster. The features are divided into 9 groups based on the vowel content to evaluate the effect of different vowel sounds. Principal component analysis (PCA) is used for dimensionality reduction along with a 10-fold cross-validation. From the results, we observed that pitch synchronous segmentation yields better classification performance compared to fixed window based segmentation. The results of this analysis support our hypothesis that pitch synchronous segmentation is better suited for PD classification using connected speech.Clinical Relevance- The automatic speech analysis framework used in this analysis establishes the greater efficiency of pitch synchronous segmentation over the traditional methods.


Assuntos
Doença de Parkinson , Máquina de Vetores de Suporte , Algoritmos , Humanos , Análise de Componente Principal , Fala
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 637-640, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018068

RESUMO

Feature extraction from ECG-derived heart rate variability signal has shown to be useful in classifying sleep apnea. In earlier works, time-domain features, frequency-domain features, and a combination of the two have been used with classifiers such as logistic regression and support vector machines. However, more recently, deep learning techniques have outperformed these conventional feature engineering and classification techniques in various applications. This work explores the use of convolutional neural networks (CNN) for detecting sleep apnea segments. CNN is an image classification technique that has shown robust performance in various signal classification applications. In this work, we use it to classify one-dimensional heart rate variability signal, thereby utilizing a one-dimensional CNN (1-D CNN). The proposed technique resizes the raw heart rate variability data to a common dimension using cubic interpolation and uses it as a direct input to the 1-D CNN, without the need for feature extraction and selection. The performance of the method is evaluated on a dataset of 70 overnight ECG recordings, with 35 recordings used for training the model and 35 for testing. The proposed method achieves an accuracy of 88.23% (AUC=0.9453) in detecting sleep apnea epochs, outperforming several baseline techniques.


Assuntos
Eletrocardiografia , Síndromes da Apneia do Sono , Frequência Cardíaca , Humanos , Redes Neurais de Computação , Síndromes da Apneia do Sono/diagnóstico , Máquina de Vetores de Suporte
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 906-909, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018131

RESUMO

A correct and early diagnosis of cardiac arrhythmias could improve patients' quality of life. The aim of this study is to classify the cardiac rhythm (atrial fibrillation, AF, or normal sinus rhythm NSR) from the photoplethysmographic (PPG) signal and assess the effect of the observation window length. Simulated signals are generated with a PPG simulator previously proposed. The different window lengths taken into account are 20, 30, 40, 50, 100, 150, 200, 250 and 300 beats. After systolic peak detection algorithm, 10 features are computed on the inter-systolic interval series, assessing variability and irregularity of the series. Then, feature selection was performed (using the sequential forward floating search algorithm) which identified two variability parameters (Mean and rMSSD) as the best selection. Finally, the classification by linear support vector machine was performed. Using only two features, accuracy was very high for all the analyzed observation window lengths, going from 0.913±0.055 for length equal to 20 to 0.995±0.011 for length equal to 300 beats.Clinical relevance These preliminary results show that short PPG signals (20 beats) can be used to correctly detect AF.


Assuntos
Fibrilação Atrial , Algoritmos , Fibrilação Atrial/diagnóstico , Humanos , Fotopletismografia , Qualidade de Vida , Máquina de Vetores de Suporte
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1047-1050, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018165

RESUMO

The present study proposes a new personalized sleep spindle detection algorithm, suggesting the importance of an individualized approach. We identify an optimal set of features that characterize the spindle and exploit a support vector machine to distinguish between spindle and nonspindle patterns. The algorithm is assessed on the open source DREAMS database, that contains only selected part of the polysomnography, and on whole night polysomnography recordings from the SPASH database. We show that on the former database the personalization can boost sensitivity, from 84.2% to 89.8%, with a slight increase in specificity, from 97.6% to 98.1%. On a whole night polysomnography instead, the algorithm reaches a sensitivity of 98.6% and a specificity of 98.1%, thanks to the personalization approach. Future work will address the integration of the spindle detection algorithm within a sleep scoring automated procedure.


Assuntos
Eletroencefalografia , Sono , Algoritmos , Polissonografia , Máquina de Vetores de Suporte
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1067-1070, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018170

RESUMO

Accurate segmentation of brain tumors is a challenging task and also a crucial step in diagnosis and treatment planning for cancer patients. Magnetic resonance imaging (MRI) is the standard imaging modality for detection, characterization, treatment planning and outcome evaluation of brain tumors. MRI scans are usually acquired at multiple sessions before and after the treatment. An automatic segmentation framework is highly desirable to segment brain tumors in MR images as it streamlines the image-guided radiation therapy workflow considerably. Automatic segmentation of brain tumors also facilitates an incremental development of data-driven systems for therapy outcome prediction based on radiomics analysis. In this study, an outlier-detection-based segmentation framework is proposed to delineate brain tumors in magnetic resonance (MR) images automatically. The proposed method considers the tumor and edema pixels in an MR image as outliers compared to the pixels associated with the healthy tissue. The framework generates two outlier masks using independent one-class support vector machines that operate on post-contrast T1-weighted (T1w) and T2-weighted-fluid-attenuation-inversion-recovery (T2-FLAIR) images. The outlier masks are subsequently refined and fused using a number of morphological and logical operators to estimate a tumor mask for each image slice. The framework was constructed and evaluated using the MRI data acquired from 35 and 5 patients with brain metastasis, respectively. The obtained results demonstrated an average Dice similarity coefficient and Hausdorff distance of 0.84 ± 0.06 and 1.85 ± 0.48 mm, respectively, between the manual (ground truth) and automatic tumor contours, on the independent test set.


Assuntos
Neoplasias Encefálicas , Máquina de Vetores de Suporte , Algoritmos , Neoplasias Encefálicas/diagnóstico por imagem , Humanos , Imagem por Ressonância Magnética
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1343-1346, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018237

RESUMO

Asbestos is a toxic ore widely used in construction and commercial products. Asbestos tends to dissolve into fibers and after years inhaling them, these fibers calcify and form plaques on the pleura. Despite being benign, pleural plaques may indicate an immunologic deficiency or dysfunctional lung areas. We propose a pipeline for asbestos-related pleural plaque detection in CT images of the human thorax based on the following operations: lung segmentation, 3D patch selection along the pleura, a convolutional neural network (CNN) for feature extraction, and classification by support vector machines (SVM). Due to the scarcity of publicly available and annotated datasets of pleural plaques, the proposed CNN relies on architecture learning with random weights obtained by a PCA-based approach instead of using traditional filter learning by backpropagation. Experiments show that the proposed CNN can outperform its counterparts based on backpropagation for small training sets.


Assuntos
Asbestos , Doenças Pleurais , Asbestos/efeitos adversos , Humanos , Redes Neurais de Computação , Pleura/diagnóstico por imagem , Doenças Pleurais/diagnóstico , Máquina de Vetores de Suporte
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1742-1745, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018334

RESUMO

Convolutional Neural Network (CNN) has been successfully applied on classification of both natural images and medical images but limited studies applied it to differentiate patients with schizophrenia from healthy controls. Given the subtle, mixed, and sparsely distributed brain atrophy patterns of schizophrenia, the capability of automatic feature learning makes CNN a powerful tool for classifying schizophrenia from controls as it removes the subjectivity in selecting relevant spatial features. To examine the feasibility of applying CNN to classification of schizophrenia and controls based on structural Magnetic Resonance Imaging (MRI), we built 3D CNN models with different architectures and compared their performance with a handcrafted feature-based machine learning approach. Support vector machine (SVM) was used as classifier and Voxel-based Morphometry (VBM) was used as feature for handcrafted feature-based machine learning. 3D CNN models with sequential architecture, inception module and residual module were trained from scratch. CNN models achieved higher cross-validation accuracy than handcrafted feature-based machine learning. Moreover, testing on an independent dataset, 3D CNN models greatly outperformed handcrafted feature-based machine learning. This study underscored the potential of CNN for identifying patients with schizophrenia using 3D brain MR images and paved the way for imaging-based individual-level diagnosis and prognosis in psychiatric disorders.


Assuntos
Imagem por Ressonância Magnética , Esquizofrenia , Encéfalo/diagnóstico por imagem , Humanos , Redes Neurais de Computação , Esquizofrenia/diagnóstico por imagem , Máquina de Vetores de Suporte
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1762-1765, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018339

RESUMO

Subjective cognitive decline (SCD) is a high-risk preclinical stage in the progress of Alzheimer's disease (AD). Its timely diagnosis is of great significance for older adults. Though multi-parameter magnetic resonance imaging (MPMRI) is a noninvasive neuroimaging technique to detect SCD, the lack of biomarkers and computed aided diagnosis (CAD) tools is a major concern for its application. Radiomics, a high-dimensional imaging feature extraction method, has been widely used for identifying biomarkers and developing CAD tools in oncological studies. Therefore, in this study, we aimed to investigate whether the radiomic approach could be used for the diagnosis of SCD. In the proposed radiomic approach, we mainly performed four steps: image preprocessing, feature extraction and screening, and classification. The dataset from Xuanwu Hospital, Beijing, China, was used in this study, including 105 healthy controls (HC) and 130 SCD subjects. All subjects were divided into one training & validation group and one test group. We extracted 30128 radiomic features from MPMRI of each subject. The t-test, autocorrelation, and Fisher score were performed for feature selection, and we deployed the support vector machine (SVM) for classification. The above process was performed 100 times with 5-fold cross-validation. The results showed that the accuracy, sensitivity, and specificity of classification was 89.03%±5.37%, 85.44%±9.28% and 91.97%±6.38% in the validation set and 84.70%±4.68%, 86.98%±10.49% and 82.59%±7.07% in the test set. In conclusion, this study has shown that the radiomic approach could be used to discriminate SCD and HC with high accuracy and sensitivity effectively.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Idoso , Doença de Alzheimer/diagnóstico , China , Disfunção Cognitiva/diagnóstico , Humanos , Imagem por Ressonância Magnética , Máquina de Vetores de Suporte
15.
Artigo em Inglês | MEDLINE | ID: mdl-32993005

RESUMO

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter's Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019-June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.


Assuntos
Infecções por Coronavirus/psicologia , Conhecimentos, Atitudes e Prática em Saúde , Pneumonia Viral/psicologia , Racismo/estatística & dados numéricos , Mídias Sociais , Grupo com Ancestrais do Continente Asiático , Betacoronavirus , Humanos , Pandemias , Aprendizado de Máquina Supervisionado , Máquina de Vetores de Suporte , Estados Unidos
16.
J Environ Manage ; 271: 111014, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32778297

RESUMO

The negative sample selection method is a key issue in studies of using machine learning approaches to spatially assess natural hazards. Recently, a Repeatedly Random Undersampling (RRU) was proposed to address the randomness problem faced in Single Random Sampling. However, the RRU cannot guarantee that the generated classifier has the best classification performance during the repeatedly random sampling process. To address this weakness, in this study we proposed an optimized RRU, which follows the idea of RRU, and then changing its rule to find a best classifier. Then, the selected classifier, the actual most accurate classifier (MAC), was employed to compute the probability of hazard occurrence. Support Vector Machine (SVM) was selected as the analysis method, and Genetic Algorithm was employed to compute the parameters of SVM. Forest fire susceptibility was assessed in Huichang County in China due to its forest values and frequent fire events. The results indicated that compared with the RRU, the optimized RRU can find out an actual MAC which has the best classification performance among possible MACs; also, the fire susceptibility map generated by the actual MAC comforts to objective facts. The generated fire susceptibility map can provide useful decision supports for local government to reduce forest fire risks. Moreover, the proposed sampling method, the optimized RRU, presented an enhanced approach for selecting negative samples, which makes the results of forest fire susceptibility assessment more reliable and accurate.


Assuntos
Máquina de Vetores de Suporte , Incêndios Florestais , Algoritmos , China , Aprendizado de Máquina
17.
Environ Monit Assess ; 192(9): 576, 2020 Aug 10.
Artigo em Inglês | MEDLINE | ID: mdl-32778970

RESUMO

Drought, which has become one of the most severe environmental problems worldwide, has serious impacts on ecological, economic, and socially sustainable development. The drought monitoring process is essential in the management of drought risks, and drought index calculation is critical in the tracking of drought. The Palmer Drought Severity Index is one of the most widely used methods in drought calculation. The drought calculation according to Palmer is a time-consuming process. Such a troublesome can be made easier using advanced machine learning algorithms. Therefore, in this study, the advanced machine learning algorithms (LR, ANN, SVM, and DT) were employed to calculate and estimate the Palmer drought Z-index values from the meteorological data. Palmer Z-index values, which will be used as training data in the classification process, were obtained through a special-purpose software adopting the classical procedure. This special-purpose software was developed within the scope of the study. According to the classification results, the best R-value (0.98) was obtained in the ANN method. The correlation coefficient was 0.98, Mean Squared Error was 0.40, and Root Mean Squared Error was 0.56 in this success. Consequently, the findings showed that drought calculation and prediction according to the Palmer Index could be successfully carried out with advanced machine learning algorithms. Graphical Abstract.


Assuntos
Secas , Máquina de Vetores de Suporte , Algoritmos , Monitoramento Ambiental , Aprendizado de Máquina
18.
PLoS One ; 15(8): e0229367, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32790672

RESUMO

Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Previsões/métodos , Algoritmos , Teorema de Bayes , Feminino , Humanos , Aprendizado de Máquina , Imageamento de Micro-Ondas , Modelos Teóricos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Máquina de Vetores de Suporte
19.
SAR QSAR Environ Res ; 31(9): 655-675, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32799684

RESUMO

We report new consensus models estimating acute toxicity for algae, Daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSEext = 0.56-0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/ColorAtom utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracy and data coverage. Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending the applicability domain and relevance for application in an industrial context. Generated models and collected public data are made freely available.


Assuntos
Daphnia/efeitos dos fármacos , Peixes , Microalgas/efeitos dos fármacos , Relação Quantitativa Estrutura-Atividade , Testes de Toxicidade Aguda , Poluentes Químicos da Água/toxicidade , Animais , Máquina de Vetores de Suporte
20.
BMC Bioinformatics ; 21(Suppl 10): 347, 2020 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-32838752

RESUMO

BACKGROUND: The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. RESULTS: We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. CONCLUSIONS: the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/.


Assuntos
Algoritmos , Farmacorresistência Bacteriana Múltipla , Aprendizado de Máquina , Modelos Biológicos , Infecções Urinárias/diagnóstico , Idoso , Área Sob a Curva , Feminino , Humanos , Itália , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Curva ROC , Máquina de Vetores de Suporte
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