• ADEPT
• Data Preprocessing
• Data Quality Assessment
• Healthcare Data Analysis

• Cancer survivorship
• Oncology
• Predictive modeling
• Radiotherapy
• Skin toxicity

• COVID-19
• artificial intelligence
• coronavirus
• eHealth
• long-term monitoring
• rule-based system
• telehealth
• telemedicine

• artificial intelligence
• automated
• machine learning

• COVID-19
• SARS-CoV2
• artificial intelligence
• cardiovascular disease
• clustering
• machine learning

• Humans
• COVID-19/epidemiology
• COVID-19/mortality
• Male
• Female
• Registries
• Aged
• Machine Learning
• Hospitalization/statistics & numerical data
• Aged, 80 and over
• Cardiovascular Diseases/epidemiology
• Cardiovascular Diseases/diagnosis
• Phenotype
• Risk Factors
• Prospective Studies
• SARS-CoV-2
• Risk Assessment/methods
• Prognosis
• Cluster Analysis

• CatBoost
• LightGBM
• Matthews correlation coefficient (MCC)
• TF-IDF
• XGBoost
• anatomical therapeutic chemical classification (ATC)
• area under the curve

• COVID-19
• Convolutional neural network
• Deep learning
• Ensemble learning
• Ensemble method

• Covid-19
• permutation entropy
• risk stratification

• Humans
• COVID-19
• SARS-CoV-2
• Entropy
• Risk Assessment

• Humans
• Cholera/epidemiology
• Machine Learning
• Disease Outbreaks
• Algorithms
• Health Services Research

• Humans
• Artificial Intelligence
• COVID-19 Vaccines
• Pandemics
• Prospective Studies
• COVID-19
• Algorithms
• Renal Dialysis

• COVID-19
• artificial intelligence
• coronavirus
• eHealth
• long-term monitoring
• rule-based system
• telehealth
• telemedicine

• Libera Università di Bolzano

• COVID-19
• Clinical data analysis
• Machine learning
• Meta learners
• Stacked ensemble
• Stacked generalization

• Cypher
• Decision tree
• Graph database
• Java
• Neo4j
• Python
• R

• Humans
• Algorithms
• Biomedical Research
• Body Mass Index
• Databases, Factual
• Decision Trees

• Deutsche Forschungsgemeinschaft

• 1DCNN
• Blood tests
• COVID-19
• Equilibrium optimization
• Feature pool sparsification
• Feature selection
• Pearson correlation

• Artificial intelligence
• COVID-19
• Deep learning
• Machine learning
• Pandemic

• cnb
• diagnosis
• fna
• lymph node tuberculosis
• pus
• rt-pcr
• tissue

• Humans
• Mycobacterium tuberculosis/genetics
• Reverse Transcriptase Polymerase Chain Reaction
• Retrospective Studies
• Sensitivity and Specificity
• Tuberculosis, Lymph Node/diagnosis
• Lymph Nodes/pathology
• Suppuration

• diagnostic markers
• predictive markers
• risk factors

• ICU readmission prediction
• Knowledge Graph embeddings
• Machine learning
• Ontologies
• Semantic annotations

• COVID-19 diagnosis
• Differently expressed genes
• Feature selection
• GO analysis
• Machine learning models
• Transcriptome data

Based on medical reports, it is hard to find levels of different hospitalized symptomatic COVID-19 patients according to their features in a short time. Besides, there are common and special features for COVID-19 patients at different levels based on physicians’ knowledge that make diagnosis difficult. For this purpose, a hierarchical model is proposed in this paper based on experts’ knowledge, fuzzy C-mean (FCM) clustering, and adaptive neuro-fuzzy inference system (ANFIS) classifier.

Experts considered a special set of features for different groups of COVID-19 patients to find their treatment plans. Accordingly, the structure of the proposed hierarchical model is designed based on experts’ knowledge. In the proposed model, we applied clustering methods to patients’ data to determine some clusters. Then, we learn classifiers for each cluster in a hierarchical model. Regarding different common and special features of patients, FCM is considered for the clustering method. Besides, ANFIS had better performances than other classification methods. Therefore, FCM and ANFIS were considered to design the proposed hierarchical model. FCM finds the membership degree of each patient’s data based on common and special features of different clusters to reinforce the ANFIS classifier. Next, ANFIS identifies the need of hospitalized symptomatic COVID-19 patients to ICU and to find whether or not they are in the end-stage (mortality target class). Two real datasets about COVID-19 patients are analyzed in this paper using the proposed model. One of these datasets had only clinical features and another dataset had both clinical and image features. Therefore, some appropriate features are extracted using some image processing and deep learning methods.

According to the results and statistical test, the proposed model has the best performance among other utilized classifiers. Its accuracies based on clinical features of the first and second datasets are 92% and 90% to find the ICU target class. Extracted features of image data increase the accuracy by 94%.

The accuracy of this model is even better for detecting the mortality target class among different classifiers in this paper and the literature review. Besides, this model is compatible with utilized datasets about COVID-19 patients based on clinical data and both clinical and image data, as well.

• A new hierarchical model is proposed using ANFIS classifiers and FCM clustering method in this paper. Its structure is designed based on experts’ knowledge and real medical process. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients.

• Two real datasets about COVID-19 patients are studied in this paper. One of these datasets has both clinical and image data. Therefore, appropriate features are extracted based on its image data and considered with available meaningful clinical data. Different levels of hospitalized symptomatic COVID-19 patients are considered in this paper including the need of patients to ICU and whether or not they are in end-stage.

• Well-known classification methods including case-based reasoning (CBR), decision tree, convolutional neural networks (CNN), K-nearest neighbors (KNN), learning vector quantization (LVQ), multi-layer perceptron (MLP), Naive Bayes (NB), radial basis function network (RBF), support vector machine (SVM), recurrent neural networks (RNN), fuzzy type-I inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS) are designed for these datasets and their results are analyzed for different random groups of the train and test data;

• According to unbalanced utilized datasets, different performances of classifiers including accuracy, sensitivity, specificity, precision, F-score, and G-mean are compared to find the best classifier. ANFIS classifiers have the best results for both datasets.

• To reduce the computational time, the effects of the Principal Component Analysis (PCA) feature reduction method are studied on the performances of the proposed model and classifiers. According to the results and statistical test, the proposed hierarchical model has the best performances among other utilized classifiers.

The online version contains supplementary material available at 10.1007/s42600-023-00268-w.

• covid-19
• feature selection
• image processing
• deep learning, classification and clustering

• COVID-19
• diagnosis
• eXplainable AI
• feature importance
• machine learning

• Humans
• COVID-19/diagnosis
• COVID-19/epidemiology
• SARS-CoV-2
• Brazil/epidemiology
• Pandemics
• Machine Learning

• Comorbidity
• Liver disease
• Liver inflammation
• Machine learning
• Viral diseases

• Humans
• Carcinoma, Hepatocellular
• COVID-19/epidemiology
• Liver Neoplasms
• Virus Diseases
• Machine Learning

• ACTH
• Addison's disease
• artificial intelligence
• cortisol

• Humans
• COVID-19/diagnostic imaging
• Pandemics
• Artificial Intelligence
• Deep Learning
• SARS-CoV-2

• COVID-19
• Clinical profiles
• Costa Rica
• Diagnosis
• Machine learning
• SARS-CoV-2

• INCIENSA
• Vicerrectoría de Investigación, Universidad de Costa Rica

• European region
• Health and well-being
• Health emergencies
• Machine learning
• Universal health coverage

• Artificial Intelligence
• Emergencies
• Health Promotion
• Humans
• Reproducibility of Results
• Systematic Reviews as Topic
• Universal Health Insurance

• 001 World Health Organization

• Benchmarking
• COVID-19/diagnosis
• Humans
• Machine Learning
• Pakistan/epidemiology
• SARS-CoV-2

• COVID-19
• Classification
• Clinical data
• Coronavirus
• Feature selection

• Algorithms
• COVID-19
• Humans
• Machine Learning
• Pandemics

• COVID-19
• Charlson Comorbidities Index
• cluster analysis
• individualized management
• longitudinal cluster

• FACINGLCOVID-CM Community of Madrid and the European Union, through the European Regional Development Fund (ERDF) - REACT-EU resources of the Madrid Operational Program 2014-2020, in the action line of R+D+i projects in response to COVID 19
• METAINFLAMATION - Y2020/BIO-6600 Synergic R&D Projects in New and Emerging Scientific Areas on the Frontier Of Science And In-terdisciplinary Nature Of The Community Of Madrid

• Artificial Intelligence
• Blood tests
• COVID-19
• Machine Learning
• RT-PCR

•

Early non-pharmacological interventions are necessary limit the spread of COVID-19.

Symptoms-based models for predicting SARS-CoV-2 infection may improve clinical decision-making and be an alternative to resource allocation in under-resourced settings. In this study we aimed to test a model based on symptoms to predict a positive test result for SARS-CoV-2 infection during the COVID-19 pandemic using logistic regression and a machine-learning approach, in Bogotá, Colombia. Participants from the CoVIDA project were included. A logistic regression using the model was chosen based on biological plausibility and the Akaike Information criterion. Also, we performed an analysis using machine learning with random forest, support vector machine, and extreme gradient boosting. The study included 58,577 participants with a positivity rate of 5.7%. The logistic regression showed that anosmia (aOR = 7.76, 95% CI [6.19, 9.73]), fever (aOR = 4.29, 95% CI [3.07, 6.02]), headache (aOR = 3.29, 95% CI [1.78, 6.07]), dry cough (aOR = 2.96, 95% CI [2.44, 3.58]), and fatigue (aOR = 1.93, 95% CI [1.57, 2.93]) were independently associated with SARS-CoV-2 infection. Our final model had an area under the curve of 0.73. The symptoms-based model correctly identified over 85% of participants. This model can be used to prioritize resource allocation related to COVID-19 diagnosis, to decide on early isolation, and contact-tracing strategies in individuals with a high probability of infection before receiving a confirmatory test result. This strategy has public health and clinical decision-making significance in low- and middle-income settings like Latin America.

• COVID-19
• Competing risks
• Composite ranking
• Survival analysis

• COVID-19
• Humans
• Leukocyte Count
• Neutrophils
• Risk Factors
• SARS-CoV-2

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.

A healthcare monitoring system needs the support of recent technologies such as artificial intelligence (AI), machine learning (ML), and big data, especially during the COVID-19 pandemic. This global pandemic has already taken millions of lives. Both infected and uninfected people have generated big data where AI and ML can use to combat and detect COVID-19 at an early stage. Motivated by this, an improved ML framework for the early detection of this disease is proposed in this paper. The state-of-the-art Harris hawks optimization (HHO) algorithm with an improved objective function is proposed and applied to optimize the hyperparameters of the ML algorithms, namely HHO-based eXtreme gradient boosting (HHOXGB), light gradient boosting (HHOLGB), categorical boosting (HHOCAT), random forest (HHORF) and support vector classifier (HHOSVC). An ensemble technique was applied to these optimized ML models to improve the prediction performance. Our proposed method was applied to publicly available big COVID-19 data and yielded a prediction accuracy of 92.38% using the ensemble model. In contrast, HHOXGB provided the highest accuracy of 92.23% as a single optimized model. The performance of the proposed method was compared with the traditional algorithms and other ML-based methods. In both cases, our proposed method performed better. Furthermore, not only the classification improvement, but also the features are analyzed in terms of feature importance calculated by SHapely adaptive exPlanations (SHAP) values. A graphical user interface is also discussed as a potential tool for nonspecialist users such as clinical staff and nurses. The processed data, trained model, and codes related to this study are available at GitHub.

The Coronavirus Disease 2019 (COVID-19) has spread all over the world and impacted many people’s lives. The characteristics of COVID-19 and other types of pneumonia have both similarities and differences, which confused doctors initially to separate and understand them. Here we presented a retrospective analysis for both COVID-19 and other types of pneumonia by combining the COVID-19 clinical data, eICU and MIMIC-III databases. Machine learning models, including logistic regression, random forest, XGBoost and deep learning neural networks, were developed to predict the severity of COVID-19 infections as well as the mortality of pneumonia patients in intensive care units (ICU). Statistical analysis and feature interpretation, including the analysis of two-level attention mechanisms on both temporal and non-temporal features, were utilized to understand the associations between different clinical variables and disease outcomes. For the COVID-19 data, the XGBoost model obtained the best performance on the test set (AUROC = 1.000 and AUPRC = 0.833). On the MIMIC-III and eICU pneumonia datasets, our deep learning model (Bi-LSTM_Attn) was able to identify clinical variables associated with death of pneumonia patients (AUROC = 0.924 and AUPRC = 0.802 for 24-hour observation window and 12-hour prediction window). The results highlighted clinical indicators, such as the lymphocyte counts, that may help the doctors to predict the disease progression and outcomes for both COVID-19 and other types of pneumonia.

• COVID-19/diagnosis
• Humans
• Intensive Care Units
• Machine Learning
• Pneumonia/diagnosis
• Retrospective Studies

• artificial intelligence
• coronavirus disease 2019
• deep learning
• spiral computed
• tomography

To better understand the different clinical phenotypes across the disease spectrum in patients with COVID-19 using an unsupervised machine learning clustering approach.

A population-based retrospective study was conducted utilizing demographics, clinical characteristics, comorbidities, and clinical outcomes of 7,606 COVID-19–positive patients on admission to public hospitals in Hong Kong in the year 2020. An unsupervised machine learning clustering was used to explore this large cohort.

Four clusters of differing clinical phenotypes based on data at initial admission was derived in which 86.6% of the deceased cases were aggregated in one of the clusters without prior knowledge of their clinical outcomes. Other distinctive clinical characteristics of this cluster were old age and high concurrent comorbidities as well as laboratory characteristics of lower hemoglobin/hematocrit levels, higher neutrophil, C-reactive protein, lactate dehydrogenase, and creatinine levels. The clinical patterns captured by the cluster analysis was validated on other temporally distinct cohorts in 2021. The phenotypes aligned with existing literature.

The study demonstrated the usefulness of unsupervised machine learning techniques with the potential to uncover latent clinical phenotypes. It could serve as a more robust classification for patient triaging and patient-tailored treatment strategies.

Coronavirus disease 2019 (COVID-19) is still ongoing spreading globally, machine learning techniques were used in disease diagnosis and to predict treatment outcomes, which showed favorable performance. The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest (RF), support vector machine (SVM), and logistic regression (LR). Feature importance to COVID-19 severity were further identified.

A retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28, 2020, eighty-six demographic, clinical, and laboratory features were selected with LassoCV method, Spearman’s rank correlation, experts’ opinions, and literature evaluation. RF, SVM, and LR were performed to predict severe COVID-19, the performance of the models was compared by the area under curve (AUC). Additionally, feature importance to COVID-19 severity were analyzed by the best performance model.

A total of 287 patients were enrolled with 36.6% severe cases and 63.4% non-severe cases. The median age was 60.0 years (interquartile range: 49.0–68.0 years). Three models were established using 23 features including 1 clinical, 1 chest computed tomography (CT) and 21 laboratory features. Among three models, RF yielded better overall performance with the highest AUC of 0.970 than SVM of 0.948 and LR of 0.928, RF also achieved a favorable sensitivity of 96.7%, specificity of 69.5%, and accuracy of 84.5%. SVM had sensitivity of 93.9%, specificity of 79.0%, and accuracy of 88.5%. LR also achieved a favorable sensitivity of 92.3%, specificity of 72.3%, and accuracy of 85.2%. Additionally, chest-CT had highest importance to illness severity, and the following features were neutrophil to lymphocyte ratio, lactate dehydrogenase, and D-dimer, respectively.

Our results indicated that RF could be a useful predictive tool to identify patients with severe COVID-19, which may facilitate effective care and further optimize resources.

The online version contains supplementary material available at 10.1186/s40249-022-00946-4.

• COVID-19
• Logistic regression
• Machine learning
• Random Forest
• Severity
• Support vector machine

• Artificial intelligence
• COVID-19 pandemic
• Electronic health record
• Machine learning

• RF1 AG072449 NIA NIH HHS

• COVID-19
• machine learning
• predictive algorithm
• prognostic model

• Adult
• Algorithms
• COVID-19
• Cohort Studies
• Humans
• Machine Learning
• Pandemics
• Prognosis
• Prospective Studies
• Retrospective Studies
• SARS-CoV-2

Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies. For example, using clinical and imaging data combined with machine learning methods, the researchers may be able to distinguish among lung alterations induced by corona virus and those produced by immunotherapy and radiation. During this epidemic, artificial intelligence (AI) may be utilized to guarantee that the appropriate individuals are recruited in cancer clinical trials more quickly and effectively than in the past, which was done in a conventional and complicated manner. In order to better care for cancer patients and find novel and more effective therapies, It is critical that we move beyond traditional research methods and use artificial intelligence (AI) and machine learning to update our research (ML). Artificial intelligence (AI) and machine learning (ML) are being utilised to help with several aspects of the COVID-19 epidemic, such as epidemiology, molecular research and medication development, medical diagnosis and treatment, and socioeconomics. The use of artificial intelligence (AI) and machine learning (ML) in the diagnosis and treatment of COVID-19 patients is also being investigated. The combination of artificial intelligence and machine learning in COVID-19 may help to identify positive patients more quickly. In order to understand the dynamics of an epidemic that is relevant to artificial intelligence, when used in different patient groups, AI-based algorithms can quickly detect CT scans with COVID-19 linked pneumonia, as well as discriminate non-COVID connected pneumonia with high specificity and accuracy. It is possible to utilize the existing difficulties and future views presented in this study to guide an optimal implementation of AI and machine learning technologies in an epidemic.

Severe acute respiratory syndrome coronavirus (SARS-COV) is a major family of viruses that cause infections in both animals and humans, including common cold, coronavirus disease (COVID-19), severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome. This study primarily aims to predict the number of COVID-19 positive cases in 36 states of Nigeria using a long short-term memory (LSTM) algorithm of deep learning. The proposed approach employs K-means clustering to detect outliers and principal component analysis (PCA) to select important features from the dataset. The LSTM was chosen because of its non-linear characteristics to handle the dataset. As COVID-19 cases follow non-linear characteristics, LSTM is the most suitable algorithm for predicting their numbers. For comparison, several types of machine learning algorithms, such as naive Bayes, XG-boost, and SVM, were employed. After the comparison, LSTM was observed to be superior among all algorithms.

Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance.

SVM being the best performant is used as our proposed supervised machine learning algorithm. An accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% were obtained with the dataset from Kaggle (https://www.kaggle.com/einsteindata4u/covid19) after applying optimization to SVM. The same procedure and work were performed with the dataset taken from San Raffaele Hospital (https://zenodo.org/record/3886927#.YIluB5AzbMV). Once more, the SVM presented the best performance among other machine learning algorithms, and 92.86%, 93.55%, and 90.91% for accuracy, sensitivity, and specificity, respectively, were obtained.

The obtained results, when compared with others from the literature based on these same datasets, are superior, leading us to conclude that our proposed solution is reliable for the COVID-19 diagnosis.

• Artificial intelligence (AI)
• coronavirus disease 2019 (COVID-19)
• deep learning
• machine learning
• severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)