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This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of four supervised machine learning algorithms, K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), to accurately classify Enterococcus species. This study involved a comprehensive dataset of 410 strains, generating 1640 individual spectra through on-plate and off-plate protein extraction methods. Although the commercial database correctly identified 76.9% of the strains, machine learning classifiers demonstrated superior performance (accuracy 0.991). In the RF model, top informative peaks played a significant role in the classification. Whole-genome sequencing showed that the most informative peaks are biomarkers connected to proteins, which are essential for understanding bacterial classification and evolution. The integration of MALDI-TOF MS and machine learning provides a rapid and accurate method for identifying Enterococcus species, improving healthcare and food safety.
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Enterococcus , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción , Aprendizaje Automático Supervisado , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción/métodos , Enterococcus/clasificación , Enterococcus/química , Enterococcus/aislamiento & purificación , Enterococcus/genética , Algoritmos , Máquina de Vectores de Soporte , Técnicas de Tipificación Bacteriana/métodos , Aprendizaje AutomáticoRESUMEN
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systemic effects, e.g., heart failure or voice distortion. However, the systemic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systemic effects could be helpful to detect the condition in its early stages. OBJECTIVE: The proposed study aims to explore whether the voice features extracted from the vowel "a" utterance carry any information that can be predictive of COPD by employing Machine Learning (ML) on a newly collected voice dataset. METHODS: Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel "a" utterance commenced following an information and consent meeting with each participant using the VoiceDiagnostic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature-importance measures. RESULTS: The classifiers RF, SVM, and CB achieved a maximum accuracy of 77 %, 69 %, and 78 % on the test set and 93 %, 78 % and 97 % on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82 % and AP of 76 %. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order. CONCLUSION: This study concludes that the utterance of vowel "a" recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis. CLINICAL TRIAL REGISTRATION NUMBER: NCT05897944.
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Aprendizaje Automático , Enfermedad Pulmonar Obstructiva Crónica , Enfermedad Pulmonar Obstructiva Crónica/clasificación , Enfermedad Pulmonar Obstructiva Crónica/fisiopatología , Enfermedad Pulmonar Obstructiva Crónica/diagnóstico , Humanos , Masculino , Femenino , Anciano , Persona de Mediana Edad , Voz/fisiología , Máquina de Vectores de SoporteRESUMEN
Chinese oolong tea is famous for its rich and diverse aromas, which is an important indicator for sensor quality evaluation. To accurately and rapidly evaluate sensory quality, a novel colorimetric sensor array (CSA) was developed to detect volatile organic compounds (VOCs) in oolong tea. We further explored the binding mechanism between colorimetric dyes that trigger changes in charge transfer and visible color changes. Based on this, we modified and optimized the CSA to improve the sensitivity by 17.1-234.9% and the stability by 8.7-33.3%. The study also assessed the effectiveness of this method by comparing two linear and two non-linear classification models, with the support vector machine (SVM) model achieving the highest accuracy, identifying different flavor intensity and grades with rates of 100% and 95.83%, respectively. These findings sufficiently demonstrated that the novel CSA, integrated with the SVM model, has promising potential for predicting the sensory quality of oolong tea.
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Colorimetría , Odorantes , Máquina de Vectores de Soporte , Gusto , Té , Compuestos Orgánicos Volátiles , Té/química , Compuestos Orgánicos Volátiles/análisis , Colorimetría/métodos , Odorantes/análisis , Olfato , Camellia sinensis/química , HumanosRESUMEN
Rodent-infested bald spots are crucial indicators of rodent infestation in grasslands. Leveraging Unmanned Aerial Vehicle (UAV) remote sensing technology for discerning detrimental bald spots among plateau pikas has significant implications for assessing associated ecological hazards. Based on UAV-visible light imagery, we classified and recognized the characteristics of plateau pika habitats with five supervised classification algorithms, i.e., minimum distance classification (MinD), maximum likelihood classification (ML), support vector machine classification (SVM), Mahalanobis distance classification (MD), and neural network classification (NN) . The accuracy of the five methods was evaluated using a confusion matrix. Results showed that NN and SVM exhibited superior performance than other methods in identifying and classifying features indicative of plateau pika habitats. The mapping accuracy of NN for grassland and bald spots was 98.1% and 98.5%, respectively, with corresponding user accuracy was 98.8% and 97.7%. The overall model accuracy was 98.3%, with a Kappa coefficient of 0.97, reflecting minimal misclassification and omission errors. Through practical verification, NN exhibited good stability. In conclusion, the neural network method was suitable for identifying rodent-damaged bald spots within alpine meadows.
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Algoritmos , Ecosistema , Pradera , Tecnología de Sensores Remotos , Roedores , Dispositivos Aéreos No Tripulados , Animales , Tecnología de Sensores Remotos/métodos , Lagomorpha , Redes Neurales de la Computación , Monitoreo del Ambiente/métodos , Máquina de Vectores de Soporte , ChinaRESUMEN
INTRODUCTION: Human and veterinary medicine are practiced separately, but literature databases such as Pubmed include articles from both fields. This impedes supporting clinical decisions with automated information retrieval, because treatment considerations would not ignore the discipline of mixed sources. Here we investigate data-driven methods from computational linguistics for automatically distinguishing between human and veterinary medical texts. METHODS: For our experiments, we selected language models after a literature review of benchmark datasets and reported performances. We generated a dataset of around 48,000 samples for binary text classification, specifically designed to differentiate between human medical and veterinary subjects. Using this dataset, we trained and fine-tuned classifiers based on selected transformer-based models as well as support vector machines (SVM). RESULTS: All trained classifiers achieved more than 99% accuracy, even though the transformer-based classifiers moderately outperformed the SVM-based one. DISCUSSION: Such classifiers could be applicable in clinical decision support functions that build on automated information retrieval.
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Procesamiento de Lenguaje Natural , Máquina de Vectores de Soporte , Humanos , Medicina Veterinaria , Almacenamiento y Recuperación de la Información/métodos , AnimalesRESUMEN
Accurate inflow forecasting is an essential non-engineering strategy to guarantee flood management and boost the effectiveness of the water supply. As inflow is the primary reservoir input, precise inflow forecasting may also offer appropriate reservoir design and management assistance. This study aims to generalize the machine learning model using the support vector machine (SVM), which is support vector regression (SVR), to predict the discharges of the Euphrates River upstream of the Haditha Dam reservoir in Anbar province West of Iraq. Time series data were collected for the period (1986-2024) for the river's daily, monthly, and seasonal flow. Different kernel functions of SVR were applied in this study. The kernels are linear, Quadratic, and Gaussian (RBF). The results showed that the daily time scale is better than the monthly and seasonal performance. In contrast, the linear kernel outperformed the other SVR kernel with a time delay of one day based on the value of the coefficient of determination (R2 = 0.95) and the root mean square error (RMSE = 53.29) m3/sec for predicting daily river flow. The results showed that the proposed machine learning model performed well in predicting the daily flow of the Euphrates River upstream of the Haditha Dam reservoir; this indicates that the model might effectively forecast flows, which helps improve water resource management and dam operations.
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Predicción , Ríos , Máquina de Vectores de Soporte , Irak , Predicción/métodos , Abastecimiento de Agua , Estaciones del AñoRESUMEN
The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniques to predict the desire to have children can offer a promising solution to address these challenges. Therefore, this study aimed to predict the childbearing tendency in women on the verge of marriage using ML techniques. Data from 252 participants (203 expressing a "desire to have children" and 49 indicating "reluctance to have children") in Abadan, and Khorramshahr cities (Khuzestan Province, Iran) was analyzed. Seven ML algorithms, including multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and K-nearest neighbors (KNN), were employed. The performance of these algorithms was assessed using metrics derived from the confusion matrix. The RF algorithm showed superior performance, with the highest sensitivity (99.5%), specificity (95.6%), and receiver operating characteristic curve (90.1%) values. Meanwhile, MLP emerged as the top-performing algorithm, showcasing the best overall performance in accuracy (77.75%) and precision (81.8%) compared to other algorithms. Factors such as age of marriage, place of residence, and strength of the family center with the birth of a child were the most effective predictors of a woman's desire to have children. Conversely, the number of daughters, the wife's ethnicity, and the spouse's ownership of assets such as cars and houses were among the least important factors in predicting this desire. ML algorithms exhibit excellent predictive capabilities for childbearing tendencies in women on the verge of marriage, highlighting their remarkable effectiveness. This capacity to offer accurate prognoses holds significant promise for advancing research in this field.
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Aprendizaje Automático , Matrimonio , Humanos , Femenino , Adulto , Irán , Algoritmos , Máquina de Vectores de Soporte , Adulto Joven , Conducta ReproductivaRESUMEN
Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min-Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635-0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC.
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Neoplasias Colorrectales , Inestabilidad de Microsatélites , Humanos , Neoplasias Colorrectales/genética , Neoplasias Colorrectales/patología , Femenino , Masculino , Persona de Mediana Edad , Estudios Retrospectivos , Anciano , Tomografía Computarizada por Rayos X/métodos , Adulto , Algoritmos , Máquina de Vectores de Soporte , Modelos LogísticosRESUMEN
OBJECTIVE: To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB). METHODS: In this retrospective analysis, medical records of 99 individuals afflicted with NTM-PD and 285 individuals with PTB in Zhejiang Chinese and Western Medicine Integrated Hospital were examined. Random numbers generated by a computer were utilized to stratify the study cohort, with 80% designated as the training cohort and 20% as the validation cohort. A total of 2153 radiomics features were extracted using Python (Pyradiomics package) to analyse the CT characteristics of the large disease areas. The identification of significant factors was conducted through the least absolute shrinkage and selection operator (LASSO) regression. The following four supervised learning classifier models were developed: random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost). For assessment and comparison of the predictive performance among these models, receiver-operating characteristic (ROC) curves and the areas under the ROC curves (AUCs) were employed. RESULTS: The Student's t-test, Levene test, and LASSO algorithm collectively selected 23 optimal features. ROC analysis was then conducted, with the respective AUC values of the XGBoost, LR, SVM, and RF models recorded to be 1, 0.9044, 0.8868, and 0.7982 in the training cohort. In the validation cohort, the respective AUC values of the XGBoost, LR, SVM, and RF models were 0.8358, 0.8085, 0.87739, and 0.7759. The DeLong test results noted the lack of remarkable variation across the models. CONCLUSION: The CT radiomics features can help distinguish between NTM-PD and PTB. Among the four classifiers, SVM showed a stable performance in effectively identifying these two diseases.
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Aprendizaje Automático , Infecciones por Mycobacterium no Tuberculosas , Tomografía Computarizada por Rayos X , Tuberculosis Pulmonar , Humanos , Estudios Retrospectivos , Tuberculosis Pulmonar/diagnóstico por imagen , Tuberculosis Pulmonar/diagnóstico , Tomografía Computarizada por Rayos X/métodos , Masculino , Femenino , Persona de Mediana Edad , Infecciones por Mycobacterium no Tuberculosas/diagnóstico por imagen , Infecciones por Mycobacterium no Tuberculosas/microbiología , Infecciones por Mycobacterium no Tuberculosas/diagnóstico , Diagnóstico Diferencial , Anciano , Adulto , Algoritmos , Curva ROC , Máquina de Vectores de Soporte , RadiómicaRESUMEN
Non-alcoholic fatty liver disease (NAFLD) and myocardial infarction (MI) are two major health burdens with significant prevalence and mortality. This study aimed to explore the co-expressed genes to understand the relationship between NAFLD and MI and identify potential crucial biomarkers of NAFLD-related MI using bioinformatics and machine learning. Functional enrichment analysis was conducted, a co-protein-protein interaction (PPI) network diagram was constructed, and support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) techniques were employed to identify one differentially expressed gene (DEG), Thrombospondin 1 (THBS1). THBS1 demonstrated strong performance in distinguishing NAFLD patients (AUC = 0.981) and MI patients (AUC = 0.900). Immuno-infiltration analysis revealed significantly lower CD8+ T cells and higher neutrophil levels in patients with NAFLD and MI. CD8+ T cells and neutrophils were effective in distinguishing NAFLD/MI from healthy controls. Correlation analysis showed that THBS1 was positively correlated with CCR (chemokine receptor), MHC class (major histocompatibility complex class), neutrophils, parainflammation, and Tfh (follicular helper T cells), and negatively correlated with CD8+ T cells, cytolytic activity, and TIL (tumor-infiltrating lymphocytes) in NAFLD and MI patients. THBS1 emerged as a novel biomarker for diagnosing NAFLD/MI in comparison to healthy controls. The results indicate that CD8+ T cells and neutrophils could serve as inflammatory immune features for differentiating patients with NAFLD/MI from healthy individuals.
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Enfermedad del Hígado Graso no Alcohólico , Trombospondina 1 , Humanos , Enfermedad del Hígado Graso no Alcohólico/inmunología , Enfermedad del Hígado Graso no Alcohólico/genética , Enfermedad del Hígado Graso no Alcohólico/metabolismo , Trombospondina 1/genética , Trombospondina 1/metabolismo , Infarto del Miocardio/inmunología , Infarto del Miocardio/metabolismo , Infarto del Miocardio/genética , Máquina de Vectores de Soporte , Biomarcadores/metabolismo , Biomarcadores/análisisRESUMEN
Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.
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Teorema de Bayes , Aprendizaje Profundo , Enfermedades de las Plantas , Hojas de la Planta , Solanum lycopersicum , Algoritmos , Máquina de Vectores de Soporte , Redes Neurales de la Computación , Aprendizaje AutomáticoRESUMEN
This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes that can be put in practice in the area of public health. Numeration analysis and association with health parameters was carried out by using Analytical tools such as, AIR Data, ARIMA,ANN, SVM and Exponential smoothing. Amongst the models, Support Vector Machine came again on top, with high accuracy yielding Mean Absolute Percentage Error of approximately 1%. Mortality of Air pollution in Qat from the case of Mortality of Air Pollution in Qatar is 959 while Auto regressive Integrated Moving average is 11.096, Exponential Smoothing 9.892 and Artificial Neural Networks are the source of inspiration for the development of this paper 4.61. The above perceptions indicate that there is need to adapt modeling strategies depending on the context and establish that it is possible to implement ML models in public health planning basket. This paper publishes the methodological frameworks for the purpose of modeling and analysis of the EHDs and serves as policy prescription for the policy makers to intending to reduce the effects of air borne pollution on health.
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Contaminación del Aire , Redes Neurales de la Computación , Máquina de Vectores de Soporte , Contaminación del Aire/análisis , Humanos , Medio Oriente , Qatar , Salud Pública , Contaminantes Atmosféricos/análisis , Contaminantes Atmosféricos/efectos adversosRESUMEN
OBJECTIVES: This study aimed to assess the accuracy of machine learning (ML) models with feature selection technique in classifying cervical vertebral maturation stages (CVMS). Consensus-based datasets were used for models training and evaluation for their model generalization capabilities on unseen datasets. METHODS: Three clinicians independently rated CVMS on 1380 lateral cephalograms, resulting in the creation of five datasets: two consensus-based datasets (Complete Agreement and Majority Voting), and three datasets based on a single rater's evaluations. Additionally, landmarks annotation of the second to fourth cervical vertebrae and patients' information underwent a feature selection process. These datasets were used to train various ML models and identify the top-performing model for each dataset. These models were subsequently tested on their generalization capabilities. RESULTS: Features that considered significant in the consensus-based datasets were consistent with a CVMS guideline. The Support Vector Machine model on the Complete Agreement dataset achieved the highest accuracy (77.4%), followed by the Multi-Layer Perceptron model on the Majority Voting dataset (69.6%). Models from individual ratings showed lower accuracies (60.4-67.9%). The consensus-based training models also exhibited lower coefficient of variation (CV), indicating superior generalization capability compared to models from single raters. CONCLUSION: ML models trained on consensus-based datasets for CVMS classification exhibited the highest accuracy, with significant features consistent with the original CVMS guidelines. These models also showed robust generalization capabilities, underscoring the importance of dataset quality.
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Vértebras Cervicales , Aprendizaje Automático , Variaciones Dependientes del Observador , Humanos , Vértebras Cervicales/crecimiento & desarrollo , Masculino , Femenino , Niño , Cefalometría/métodos , Máquina de Vectores de Soporte , Adolescente , Determinación de la Edad por el Esqueleto/métodos , Conjuntos de Datos como AsuntoRESUMEN
BACKGROUND: Sepsis-induced cardiomyopathy (SICM) is a common and life-threatening complication of sepsis, significantly contributing to elevated mortality. This study aimed to identify crucial indicators for the prompt and early assessment of SICM. METHODS: Patients diagnosed with sepsis or SICM within 24 h of intensive care unit (ICU) admission were enrolled in this prospective observational study. Patients were assigned to the training set, validation set and external test set. The primary endpoint was 7-day ICU mortality, and the secondary endpoint was 28-day ICU mortality. Three machine learning algorithms were utilized to identify relevant indicators for diagnosing SICM, incorporating 64 indicators including serum biomarkers associated with cardiac, renal, and liver function, lipid metabolism, coagulation, and inflammation. Internal and external validations were performed on the screening results. Patients were then stratified based on the cut-off value of the most diagnostically effective biomarker identified, and their prognostic outcomes were observed and analyzed. RESULTS: A total of 270 patients were included in the training and validation set, and 52 patients were included in the external test set. Age, sex, and comorbidities did not significantly differ between the sepsis and SICM groups (P > 0.05). The support vector machine (SVM) algorithm identified six indicators with an accuracy of 84.5%, the random forest (RF) algorithm identified six indicators with an accuracy of 81.9%, and the logistic regression (LR) algorithm screened out seven indicators. Following rigorous selection, a diagnostic model for sepsis-induced cardiomyopathy was established based on heart-type fatty acid binding protein (H-FABP) (OR 1.308, 95% CI 1.170-1.462, P < 0.001) and retinol-binding protein (RBP) (OR 1.020, 95% CI 1.006-1.034, P < 0.05). H-FABP alone exhibited the highest diagnostic performance in both the internal (AUROC 0.689, P < 0.05) and external sets (AUROC 0.845, P < 0.05). Patients with SICM were further stratified based on an H-FABP diagnostic cut-off value of 8.335 ng/mL. Kaplan-Meier curve analysis demonstrated that elevated H-FABP levels at admission were associated with higher 7-day ICU mortality in patients with SICM (P < 0.05). CONCLUSIONS: This study revealed that H-FABP concentrations measured within 24 h of patient admission could serve as a crucial biomarker for the early and rapid diagnosis and short-term prognostic evaluation of SICM.
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Biomarcadores , Cardiomiopatías , Proteínas de Unión a Ácidos Grasos , Sepsis , Humanos , Masculino , Femenino , Biomarcadores/sangre , Cardiomiopatías/sangre , Cardiomiopatías/diagnóstico , Cardiomiopatías/etiología , Sepsis/sangre , Sepsis/complicaciones , Sepsis/diagnóstico , Persona de Mediana Edad , Estudios Prospectivos , Proteínas de Unión a Ácidos Grasos/sangre , Anciano , Proteína 3 de Unión a Ácidos Grasos/sangre , Unidades de Cuidados Intensivos , Pronóstico , Curva ROC , Máquina de Vectores de SoporteRESUMEN
This study aims to evaluate the applicability of the high-resolution WaveFront Phase Imaging Sensor (WFPI) in eyes with Fuchs' Endothelial Corneal Dystrophy (FECD) through qualitative and quantitative analysis using a custom-designed Automatic Guttae Detection Method (AGDM). The ocular phase was measured using the t · eyede aberrometer and then was processed to obtain its High-Pass Filter Map (HPFM). The subjects were pathological and healthy patients from the Fundación Jiménez-Díaz Hospital (Madrid, Spain). The AGDM was developed and applied in pupils with 3 and 5 mm of diameter. A set of metrics were extracted and evaluated like the Root-Mean-Square error (RMS), Number of guttae, Guttae Area, and Area of Delaunay Triangulation (DT). Finally, a Support Vector Machine (SVM) model was trained to classify between pathological and healthy eyes. Quantitatively, the HPFM reveals a dark spots pattern according to the ophthalmologist's description of the slit-lamp examination of guttae distribution. There were significant statistical differences in all the metrics when FECD and Healthy groups were compared using the same pupil size; but comparing both pupil sizes for the same group there were significant differences in most of the variables. This sensor is a value tool to objectively diagnose and monitor this pathology through wavefront phase changes.
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Distrofia Endotelial de Fuchs , Humanos , Distrofia Endotelial de Fuchs/diagnóstico , Femenino , Masculino , Persona de Mediana Edad , Anciano , Máquina de Vectores de Soporte , Aberrometría/métodos , Aberrometría/instrumentación , AdultoRESUMEN
BACKGROUND: Machine learning (ML) is extensively employed for forecasting the outcome of various illnesses. The objective of the study was to develop ML based classifiers using a stacking ensemble strategy to predict the Japanese Orthopedic Association (JOA) recovery rate for patients with degenerative cervical myelopathy (DCM). METHODS: A total of 672 patients with DCM were included in the study and labeled with JOA recovery rate by 1-year follow-up. All data were collected during 2012-2023 and were randomly divided into training and testing (8:2) sub-datasets. A total of 91 initial ML classifiers were developed, and the top 3 initial classifiers with the best performance were further stacked into an ensemble classifier with a supported vector machine (SVM) classifier. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicted outcome was the JOA recovery rate. RESULTS: By applying an ensemble learning strategy (e.g., stacking), the accuracy of the ML classifier improved following combining three widely used ML models (e.g., RFE-SVM, EmbeddingLR-LR, and RFE-AdaBoost). Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top 3 initial classifiers varied a lot in predicting JOA recovery rate in DCM patients. CONCLUSIONS: The ensemble classifiers successfully predict the JOA recovery rate in DCM patients, which showed a high potential for assisting physicians in managing DCM patients and making full use of medical resources.
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Vértebras Cervicales , Aprendizaje Automático , Humanos , Vértebras Cervicales/cirugía , Masculino , Femenino , Persona de Mediana Edad , Resultado del Tratamiento , Anciano , Enfermedades de la Médula Espinal/cirugía , Máquina de Vectores de Soporte , Recuperación de la Función , Estudios de Seguimiento , PredicciónRESUMEN
Objective: This study aims to develop risk prediction models for neck and shoulder musculoskeletal disorders among healthcare professionals. Methods: A stratified sampling method was employed to select employees from medical institutions in Nanning City, yielding 617 samples. The Boruta algorithm was used for feature selection, and various models, including Tree-Based Models, Single Hidden-Layer Neural Network Models (MLP), Elastic Net Models (ENet), and Support Vector Machines (SVM), were applied to predict the selected variables, utilizing SHAP algorithms for individual-level local explanations. Results: The SVM model excels in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and exhibits more stable performance when generalizing to unseen data. The Random Forest model exhibited relatively high overall performance on the training set. The MLP model emerges as the most consistent and accurate in predicting shoulder musculoskeletal disorders, while the SVM model shows strong fitting capabilities during the training phase, with occupational factors identified as the main contributors to WMSDs. Conclusion: This study successfully constructs work-related musculoskeletal disorder risk prediction models for healthcare professionals, enabling a quantitative analysis of the impact of occupational factors. This advancement is beneficial for future economical and convenient work-related musculoskeletal disorder screening in healthcare professions.
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Personal de Salud , Aprendizaje Automático , Enfermedades Musculoesqueléticas , Enfermedades Profesionales , Humanos , Personal de Salud/estadística & datos numéricos , Enfermedades Musculoesqueléticas/epidemiología , Enfermedades Profesionales/epidemiología , Masculino , Femenino , Adulto , Persona de Mediana Edad , Máquina de Vectores de Soporte , Factores de Riesgo , Medición de Riesgo/métodos , Algoritmos , HombroRESUMEN
BACKGROUND: Mechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a predictive model to estimate hospital mortality among MV patients, utilizing comprehensive health data to assist ICU physicians with early-stage alerts. METHODS: We developed a Machine Learning (ML) framework to predict hospital mortality in ICU patients receiving MV. Using the MIMIC-III database, we identified 25,202 eligible patients through ICD-9 codes. We employed backward elimination and the Lasso method, selecting 32 features based on clinical insights and literature. Data preprocessing included eliminating columns with over 90% missing data and using mean imputation for the remaining missing values. To address class imbalance, we used the Synthetic Minority Over-sampling Technique (SMOTE). We evaluated several ML models, including CatBoost, XGBoost, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, using a 70/30 train-test split. The CatBoost model was chosen for its superior performance in terms of accuracy, precision, recall, F1-score, AUROC metrics, and calibration plots. RESULTS: The study involved a cohort of 25,202 patients on MV. The CatBoost model attained an AUROC of 0.862, an increase from an initial AUROC of 0.821, which was the best reported in the literature. It also demonstrated an accuracy of 0.789, an F1-score of 0.747, and better calibration, outperforming other models. These improvements are due to systematic feature selection and the robust gradient boosting architecture of CatBoost. CONCLUSION: The preprocessing methodology significantly reduced the number of relevant features, simplifying computational processes, and identified critical features previously overlooked. Integrating these features and tuning the parameters, our model demonstrated strong generalization to unseen data. This highlights the potential of ML as a crucial tool in ICUs, enhancing resource allocation and providing more personalized interventions for MV patients.
Asunto(s)
Mortalidad Hospitalaria , Unidades de Cuidados Intensivos , Aprendizaje Automático , Respiración Artificial , Humanos , Respiración Artificial/mortalidad , Masculino , Femenino , Persona de Mediana Edad , Anciano , Máquina de Vectores de Soporte , Enfermedad Crítica/mortalidad , Bases de Datos FactualesRESUMEN
In recent decades, unfavorable solubility of novel therapeutic agents is considered as an important challenge in pharmaceutical industry. Supercritical carbon dioxide (SCCO2) is known as a green, cost-effective, high-performance, and promising solvent to develop the low solubility of drugs with the aim of enhancing their therapeutic effects. The prominent objective of this study is to improve and modify disparate predictive models through artificial intelligence (AI) to estimate the optimized value of the Oxaprozin solubility in SCCO2 system. In this paper, three different models were selected to develop models on a solubility dataset. Pressure (bar) and temperature (K) are the two inputs for each vector, and each vector has one output (solubility). Selected models include NU-SVM, Linear-SVM, and Decision Tree (DT). Models were optimized through hyper-parameters and assessed applying standard metrics. Considering R-squared metric, NU-SVM, Linear-SVM, and DT have scores of 0.994, 0.854, and 0.950, respectively. Also, they have RMSE error rates of 3.0982E-05, 1.5024E-04, and 1.1680E-04, respectively. Based on the evaluations made, NU-SVM was considered as the most precise method, and optimal values can be summarized as (T = 336.05 K, P = 400.0 bar, solubility = 0.00127) employing this model. Fig 4.
Asunto(s)
Inteligencia Artificial , Nanopartículas , Solubilidad , Nanopartículas/química , Dióxido de Carbono/química , Modelos Teóricos , Tecnología Química Verde/métodos , Simulación por Computador , Temperatura , Máquina de Vectores de SoporteRESUMEN
Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impairments, identifying specific joint disorders, and a combined model for both tasks. Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature extraction, and models were fine-tuned using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB). The IMU-based system achieved over 91% accuracy in classifying the three types of gait. In contrast, the walkway system achieved less than 77% accuracy in classifying the three types of gait, primarily due to high misclassification rates between knee and ankle joint impairments. The IMU-based system shows promise for accurate gait assessment in patients with joint impairments, suggesting future research for clinical application improvements in rehabilitation and patient management.