Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19.

BACKGROUND: The exponential spread of coronavirus disease 2019 (COVID-19) causes unexpected economic burdens to worldwide health systems with severe shortages in hospital resources (beds, staff, equipment). Managing patients' length of stay (LOS) to optimize clinical care and utilization of hospital resources is very challenging. Projecting the future demand requires reliable prediction of patients' LOS, which can be beneficial for taking appropriate actions. Therefore, the purpose of this research is to develop and validate models using a multilayer perceptron-artificial neural network (MLP-ANN) algorithm based on the best training algorithm for predicting COVID-19 patients' hospital LOS. METHODS: Using a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020 to December 20, 2020 were analyzed. In this study, first, the correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at a P-value < 0.2 were used in model construction. Then, the prediction models were developed based on 12 training algorithms according to full and selected feature datasets (90% of the training, with 10% used for model validation). Afterward, the root mean square error (RMSE) was used to assess the models' performance in order to select the best ANN training algorithm. Finally, a total of 343 patients were used for the external validation of the models. RESULTS: After implementing feature selection, a total of 20 variables were determined as the contributing factors to COVID-19 patients' LOS in order to build the models. The conducted experiments indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian regularization (BR) training algorithm for whole and selected features with an RMSE of 1.6213 and 2.2332, respectively. CONCLUSIONS: MLP-ANN-based models can reliably predict LOS in hospitalized patients with COVID-19 using readily available data at the time of admission. In this regard, the models developed in our study can help health systems to optimally allocate limited hospital resources and make informed evidence-based decisions.

Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms.

Background: Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA). Objective: This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality. Material and Methods: In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models. Results: A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively. Conclusion: The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.

Using logistic regression to develop a diagnostic model for COVID-19: A single-center study.

BACKGROUND: The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the many other respiratory diseases. In addition, the existence of numerous uncertainties in the prognosis of this condition has multiplied the need to establish a valid and accurate prediction model. This study aimed to develop a diagnostic model based on logistic regression to enhance the diagnostic accuracy of COVID-19. MATERIALS AND METHODS: A standardized diagnostic model was developed on data of 400 patients who were referred to Ayatollah Talleghani Hospital, Abadan, Iran, for the COVID-19 diagnosis. We used the Chi-square correlation coefficient for feature selection, and logistic regression in SPSS V25 software to model the relationship between each of the clinical features. Potentially diagnostic determinants extracted from the patient's history, physical examination, and laboratory and imaging testing were entered in a logistic regression analysis. The discriminative ability of the model was expressed as sensitivity, specificity, accuracy, and area under the curve, respectively. RESULTS: After determining the correlation of each diagnostic regressor with COVID-19 using the Chi-square method, the 15 important regressors were obtained at the level of P < 0.05. The experimental results demonstrated that the binary logistic regression model yielded specificity, sensitivity, and accuracy of 97.3%, 98.8%, and 98.2%, respectively. CONCLUSION: The destructive effects of the COVID-19 outbreak and the shortage of healthcare resources in fighting against this pandemic require increasing attention to using the Clinical Decision Support Systems equipped with supervised learning classification algorithms such as logistic regression.

Internet of Things (IoT) Adoption Model for Early Identification and Monitoring of COVID-19 Cases: A Systematic Review.

Background: The 2019 coronavirus disease (COVID-19) is a mysterious and highly infectious disease that was declared a pandemic by the World Health Organization. The virus poses a great threat to global health and the economy. Currently, in the absence of effective treatment or vaccine, leveraging advanced digital technologies is of great importance. In this respect, the Internet of Things (IoT) is useful for smart monitoring and tracing of COVID-19. Therefore, in this study, we have reviewed the literature available on the IoT-enabled solutions to tackle the current COVID-19 outbreak. Methods: This systematic literature review was conducted using an electronic search of articles in the PubMed, Google Scholar, ProQuest, Scopus, Science Direct, and Web of Science databases to formulate a complete view of the IoT-enabled solutions to monitoring and tracing of COVID-19 according to the FITT (Fit between Individual, Task, and Technology) model. Results: In the literature review, 28 articles were identified as eligible for analysis. This review provides an overview of technological adoption of IoT in COVID-19 to identify significant users, either primary or secondary, required technologies including technical platform, exchange, processing, storage and added-value technologies, and system tasks or applications at "on-body," "in-clinic/hospital," and even "in-community" levels. Conclusions: The use of IoT along with advanced intelligence and computing technologies for ubiquitous monitoring and tracking of patients in quarantine has made it a critical aspect in fighting the spread of the current COVID-19 and even future pandemics.

Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis.

Purpose: Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis. Methods: Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes. Results: This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157-1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307-9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both P > 0.2). The shapes of Begg's funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg's test P = 0.945 and 0.356, respectively). Conclusions: The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement.

Using logistic regression to develop a diagnostic model for COVID-19: A single-center study

BACKGROUND: The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the many other respiratory diseases. In addition, the existence of numerous uncertainties in the prognosis of this condition has multiplied the need to establish a valid and accurate prediction model. This study aimed to develop a diagnostic model based on logistic regression to enhance the diagnostic accuracy of COVID-19. MATERIALS AND METHODS: A standardized diagnostic model was developed on data of 400 patients who were referred to Ayatollah Talleghani Hospital, Abadan, Iran, for the COVID-19 diagnosis. We used the Chi-square correlation coefficient for feature selection, and logistic regression in SPSS V25 software to model the relationship between each of the clinical features. Potentially diagnostic determinants extracted from the patient's history, physical examination, and laboratory and imaging testing were entered in a logistic regression analysis. The discriminative ability of the model was expressed as sensitivity, specificity, accuracy, and area under the curve, respectively. RESULTS: After determining the correlation of each diagnostic regressor with COVID-19 using the Chi-square method, the 15 important regressors were obtained at the level of P < 0.05. The experimental results demonstrated that the binary logistic regression model yielded specificity, sensitivity, and accuracy of 97.3%, 98.8%, and 98.2%, respectively. CONCLUSION: The destructive effects of the COVID-19 outbreak and the shortage of healthcare resources in fighting against this pandemic require increasing attention to using the Clinical Decision Support Systems equipped with supervised learning classification algorithms such as logistic regression.

Predictive modeling for COVID-19 readmission risk using machine learning algorithms.

INTRODUCTION: The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients. METHODS: In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified. RESULTS: The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795). CONCLUSIONS: Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.

Design of an artificial neural network to predict mortality among COVID-19 patients.

Introduction: The fast pandemic of coronavirus disease 2019 (COVID-19) has challenged clinicians with many uncertainties and ambiguities regarding disease outcomes and complications. To deal with these uncertainties, our study aimed to develop and evaluate several artificial neural networks (ANNs) to predict the mortality risk in hospitalized COVID-19 patients. Material and methods: The data of 1710 hospitalized COVID-19 patients were used in this retrospective and developmental study. First, a Chi-square test (P < 0.05), Eta coefficient (η > 0.4), and binary logistics regression (BLR) analysis were performed to determine the factors affecting COVID-19 mortality. Then, using the selected variables, two types of feed-forward (FF) models, including the back-propagation (BP) and distributed time delay (DTD) were trained. The models' performance was assessed using mean squared error (MSE), error histogram (EH), and area under the ROC curve (AUC-ROC) metrics. Results: After applying the univariate and multivariate analysis, 13 variables were selected as important features in predicting COVID-19 mortality at P < 0.05. A comparison of the two ANN architectures using the MSE showed that the BP-ANN (validation error: 0.067, most of the classified samples having 0.049 and 0.05 error rates, and AUC-ROC: 0.888) was the best model. Conclusions: Our findings show the acceptable performance of ANN for predicting the risk of mortality in hospitalized COVID-19 patients. Application of the developed ANN-based CDSS in a real clinical environment will improve patient safety and reduce disease severity and mortality.

Survey of the patients' perspectives and preferences in adopting telepharmacy versus in-person visits to the pharmacy: a feasibility study during the COVID-19 pandemic.

BACKGROUND: Following the coronavirus disease 2019 (COVID-19) pandemic, the health authorities recommended the implementation of strict social distancing and complete lockdown regulations to reduce disease spread. The pharmacists quickly adopted telemedicine (telepharmacy) as a solution against this crisis, but awareness about this technology is lacking. Therefore, the purpose of this research was to explore the patients' perspectives and preferences regarding telepharmacy instead of traditional in-person visits. METHODS: An electronic questionnaire was designed and sent to 313 patients who were eligible for the study (from March to April 2021). The questionnaire used five-point Likert scales to inquire about motivations for adopting telepharmacy and in-person visits, their perceived advantages and disadvantages, and the declining factors of telepharmacy. Finally, the results were descriptively analyzed using SPSS 22. RESULTS: Of all 313 respondents, a total of 241 (77%) preferred appointments via telepharmacy while 72 (23%) preferred in-person services. There was a significant difference between the selection percentage of telepharmacy and in-person services (chi-square 91.42; p < 0.0001). Preference bout the telepharmacy system versus in-person visits to the pharmacy was associated with factors such as "reducing the incidence of contagious disease" (4.41; ± 0.78), "spending less time receiving pharmaceutical services" (4.24; ± 0.86)), and "traveling a shorter distance for receiving pharmaceutical services" (4.25; ± 0.86). "Reducing costs" (90.87%), "saving time" (89.21%), and "reducing the incidence of contagious disease" (87.13%) were the most important reasons for choosing telepharmacy services. Also, "face-to-face communication with the pharmacist" (25%), "low internet bandwidth" (25%), and "reduction of patients' anxiety and the increase of their peace of mind" (23.61%) were the most important reasons for choosing in-person visits. CONCLUSION: Survey data indicate that most participants are likely to prefer the use of telepharmacy, especially during crises such as the current COVID-19 pandemic. Telepharmacy can be applied as an important means and a crucial service to lessen the load on healthcare organizations and expand drug supply shelters in pharmacies. However, there are still substantial hurdles to overcome in order to successfully implement the telemedicine platform as part of mainstream practice.

Using decision tree algorithms for estimating ICU admission of COVID-19 patients

Introduction Coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As intensive care unit (ICU) capacities are limited, deciding on the proper allocation of required resources is crucial. This study aimed to develop and compare models for early predicting ICU admission in COVID-19 patients at the point of hospital admission. Materials and methods Using a single-center registry, we studied the records of 512 COVID-19 patients. First, the most important variables were identified using Chi-square test (at p < 0.01) and logistic regression (with odds ratio at P < 0.05). Second, we trained seven decision tree (DT) algorithms (decision stump (DS), Hoeffding tree (HT), LMT, J-48, random forest (RF), random tree (RT) and REP-Tree) using the selected variables. Finally, the models’ performance was evaluated. Furthermore, we used an external dataset to validate the prediction models. Results Using the Chi-square test, 20 important variables were identified. Then, 12 variables were selected for model construction using logistic regression. Comparing the DT methods demonstrated that J-48 (F-score of 0.816 and AUC of 0.845) had the best performance. Also, the J-48 (F-score = 80.9% and AUC = 0.822) gained the best performance in generalizability using the external dataset. Conclusions The study results demonstrated that DT algorithms can be used to predict ICU admission requirements in COVID-19 patients based on the first time of admission data. Implementing such models has the potential to inform clinicians and managers to adopt the best policy and get prepare during the COVID-19 time-sensitive and resource-constrained situation.

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.

Introduction: The Coronavirus 2019 (COVID-19) epidemic stunned the health systems with severe scarcities in hospital resources. In this critical situation, decreasing COVID-19 readmissions could potentially sustain hospital capacity. This study aimed to select the most affecting features of COVID-19 readmission and compare the capability of Machine Learning (ML) algorithms to predict COVID-19 readmission based on the selected features. Material and methods: The data of 5791 hospitalized patients with COVID-19 were retrospectively recruited from a hospital registry system. The LASSO feature selection algorithm was used to select the most important features related to COVID-19 readmission. HistGradientBoosting classifier (HGB), Bagging classifier, Multi-Layered Perceptron (MLP), Support Vector Machine ((SVM) kernel = linear), SVM (kernel = RBF), and Extreme Gradient Boosting (XGBoost) classifiers were used for prediction. We evaluated the performance of ML algorithms with a 10-fold cross-validation method using six performance evaluation metrics. Results: Out of the 42 features, 14 were identified as the most relevant predictors. The XGBoost classifier outperformed the other six ML models with an average accuracy of 91.7%, specificity of 91.3%, the sensitivity of 91.6%, F-measure of 91.8%, and AUC of 0.91%. Conclusion: The experimental results prove that ML models can satisfactorily predict COVID-19 readmission. Besides considering the risk factors prioritized in this work, categorizing cases with a high risk of reinfection can make the patient triaging procedure and hospital resource utilization more effective.

Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study.

Background: Owing to the shortage of ventilators, there is a crucial demand for an objective and accurate prognosis for 2019 coronavirus disease (COVID-19) critical patients, which may necessitate a mechanical ventilator (MV). This study aimed to construct a predictive model using machine learning (ML) algorithms for frontline clinicians to better triage endangered patients and priorities who would need MV. Methods: In this retrospective single-center study, the data of 482 COVID-19 patients from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms including, multi-layer perception (MLP), logistic regression (LR), J-48 decision tree, and Naïve Bayes (NB). First, the most important clinical variables were identified using the Chi-square test at P < 0.01. Then, by comparing the ML algorithms' performance using some evaluation criteria, including TP-Rate, FP-Rate, precision, recall, F-Score, MCC, and Kappa, the best performing one was identified. Results: Predictive models were trained using 15 validated features, including cough, contusion, oxygen therapy, dyspnea, loss of taste, rhinorrhea, blood pressure, absolute lymphocyte count, pleural fluid, activated partial thromboplastin time, blood glucose, white cell count, cardiac diseases, length of hospitalization, and other underline diseases. The results indicated the J-48 with F-score = 0.868 and AUC = 0.892 yielded the best performance for predicting intubation requirement. Conclusion: ML algorithms are potentials to improve traditional clinical criteria to forecast the necessity for intubation in COVID-19 in-hospital patients. Such ML-based prediction models may help physicians with optimizing the timing of intubation, better sharing of MV resources and personnel, and increase patient clinical status.

Developing an artificial neural network for detecting COVID-19 disease.

BACKGROUND: From December 2019, atypical pneumonia termed COVID-19 has been increasing exponentially across the world. It poses a great threat and challenge to world health and the economy. Medical specialists face uncertainty in making decisions based on their judgment for COVID-19. Thus, this study aimed to establish an intelligent model based on artificial neural networks (ANNs) for diagnosing COVID-19. MATERIALS AND METHODS: Using a single-center registry, we studied the records of 250 confirmed COVID-19 and 150 negative cases from February 9, 2020, to October 20, 2020. The correlation coefficient technique was used to determine the most significant variables of the ANN model. The variables at P < 0.05 were used for model construction. We applied the back-propagation technique for training a neural network on the dataset. After comparing different neural network configurations, the best configuration of ANN was acquired, then its strength has been evaluated. RESULTS: After the feature selection process, a total of 18 variables were determined as the most relevant predictors for developing the ANN models. The results indicated that two nested loops' architecture of 9-10-15-2 (10 and 15 neurons used in layer 1 and layer 2, respectively) with the area under the curve of 0.982, the sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94% was introduced as the best configuration model for COVID-19 diagnosis. CONCLUSION: The proposed ANN-based clinical decision support system could be considered as a suitable computational technique for the frontline practitioner in early detection, effective intervention, and possibly a reduction of mortality in patients with COVID-19.

Comparing machine learning algorithms for predicting COVID-19 mortality.

BACKGROUND: The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. METHODS: In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. RESULTS: The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. CONCLUSION: It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.

Performance analysis of data mining algorithms for diagnosing COVID-19.

BACKGROUND: An outbreak of atypical pneumonia termed COVID-19 has widely spread all over the world since the beginning of 2020. In this regard, designing a prediction system for the early detection of COVID-19 is a critical issue in mitigating virus spread. In this study, we have applied selected machine learning techniques to select the best predictive models based on their performance. MATERIALS AND METHODS: The data of 435 suspicious cases with COVID-19 which were recorded from the Imam Khomeini Hospital database between May 9, 2020 and December 20, 2020, have been taken into consideration. The Chi-square method was used to determine the most important features in diagnosing the COVID-19; eight selected data mining algorithms including multilayer perceptron (MLP), J-48, Bayesian Net (Bayes Net), logistic regression, K-star, random forest, Ada-boost, and sequential minimal optimization (SMO) were applied in data mining. Finally, the most appropriate diagnostic model for COVID-19 was obtained based on comparing the performance of the selected algorithms. RESULTS: As the result of using the Chi-square method, 21 variables were identified as the most important diagnostic criteria in COVID-19. The results of evaluating the eight selected data mining algorithms showed that the J-48 with true-positive rate = 0.85, false-positive rate = 0.173, precision = 0.85, recall = 0.85, F-score = 0.85, Matthews Correlation Coefficient = 0.68, and area under the receiver operator characteristics = 0.68, respectively, had the higher performance than the other algorithms. CONCLUSION: The results of evaluating the performance criteria showed that the J-48 can be considered as a suitable computational prediction model for diagnosing COVID-19 disease.

A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method.

BACKGROUND: Predicting severe respiratory failure due to COVID-19 can help triage patients to higher levels of care, resource allocation and decrease morbidity and mortality. The need for this research derives from the increasing demand for innovative technologies to overcome complex data analysis and decision-making tasks in critical care units. Hence the aim of our paper is to present a new algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the intubation risk of hospitalized COVID-19 patients. METHODS: In this retrospective single-center study, the data of 1225 COVID-19 patients from February 9, 2020, to July 20, 2021, were analyzed by several ML algorithms which included, Decision Tree(DT), Support Vector Machine (SVM), Multilayer perceptron (MLP), and K-Nearest Neighbors(K-NN). First, the most important predictors were identified using the Horse herd Optimization Algorithm (HOA). Then, by comparing the ML algorithms' performance using some evaluation criteria, the best performing one was identified. RESULTS: Predictive models were trained using 12 validated features. Also, it found that proposed DT-based predictive model enables a reasonable level of accuracy (=93%) in predicting the risk of intubation among hospitalized COVID-19 patients. CONCLUSIONS: The experimental results demonstrate the effectiveness of the proposed meta-heuristic feature selection technique in combining with DT model in predicting intubation risk for hospitalized patients with COVID-19. The proposed model have the potential to inform frontline clinicians with quantitative and non-invasive tool to assess illness severity and to identify high risk patients.

Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features.

BACKGROUND: Given coronavirus disease (COVID-19's) unknown nature, diagnosis, and treatment is very complex up to the present time. Thus, it is essential to have a framework for an early prediction of the disease. In this regard, machines learning (ML) could be crucial to extract concealed patterns from mining of huge raw datasets then it establishes high-quality predictive models. At this juncture, we aimed to apply different ML techniques to develop clinical predictive models and select the best performance of them. MATERIALS AND METHODS: The dataset of Ayatollah Talleghani hospital, COVID-19 focal center affiliated to Abadan University of Medical Sciences have been taken into consideration. The dataset used in this study consists of 501 case records with two classes (COVID-19 and non COVID-19) and 32 columns for the diagnostic features. ML algorithms such as Naïve Bayesian, Bayesian Net, random forest (RF), multilayer perceptron, K-star, C4.5, and support vector machine were developed. Then, the recital of selected ML models was assessed by the comparison of some performance indices such as accuracy, sensitivity, specificity, precision, F-score, and receiver operating characteristic (ROC). RESULTS: The experimental results indicate that RF algorithm with the accuracy of 92.42%, specificity of 75.70%, precision of 92.30%, sensitivity of 92.40%, F-measure of 92.00%, and ROC of 97.15% has the best capability for COVID-19 diagnosis and screening. CONCLUSION: The empirical results reveal that RF model yielded higher performance as compared to other six classification models. It is promising to the implementation of RF model in the health-care settings to increase the accuracy and speed of disease diagnosis for primary prevention, screening, surveillance, and early treatment.

Systematic review on telemedicine platforms in lockdown periods: Lessons learned from the COVID-19 pandemic.

With the onset of the coronavirus disease 2019 (COVID-19) outbreak, the transformation of the care delivery model from conventional in-person (face to face) to largely virtual or remote care has been accelerated to appropriately allocate resources and constrain the spread of the virus. In this regard, telemedicine is a breakthrough technology to battle against the COVID-19 emergency. Therefore, we sought to identify the telemedicine applications in the COVID-19 pandemic (tele-COVID) according to interaction modes, transmission modalities, and disease categories. This systematic review was conducted through searching five databases including PubMed, Scopus, ProQuest, Web of Science, and Science Direct. Inclusion criteria were studies clearly outlining any use of telemedicine interactive mode during the COVID-19 pandemic, written in English language and published in peer-reviewed journals in 2020. Finally, 43 articles met the inclusion out of the 1118 search results. Telemedicine provides a diversity of interaction modes and modalities affordable by patients and physicians including short message service, E-mail and web portals, secure telephone calls or VOIP, video calls, interactive mobile health applications (m-Health), remote patient monitoring, and video conferencing. Transmission of video data using synchronized video calls via common social media had the highest and exchange of data using store-forward service via secure messaging technology and prerecorded multimedia had the lowest popularity for virtual disease management during the COVID-19 outbreak. Selection of telemedicine communication services and interaction modes with regard to its use-case, disease category, and application plays a significant role in the success of remote disease management infrastructures in this scenario and their implication for a better future healthcare system.

Notifiable diseases interoperable framework toward improving Iran public health surveillance system: Lessons learned from COVID-19 pandemic.

BACKGROUND: Direct transmission of notifiable disease information in a real-time and reliable way to public health decision-makers is imperative for early identification of epidemiological trends as well as proper response to potential pandemic like ongoing coronavirus disease 2019 crisis. Thus, this research aimed to develop of semantic-sharing and collaborative-modeling to meet the information exchange requirements of Iran's notifiable diseases surveillance system. MATERIALS AND METHODS: First, the Iran's Notifiable diseases Minimum Data Set (INMDS) was determined according to a literature review coupled with agreements of experts. Then the INMDS was mapped to international terminologies and classification systems, and the Health Level seven-Clinical Document Architecture (HL7-CDA) standard was leveraged to define the exchangeable and machine-readable data formats. RESULTS: A core dataset consisting of 15 classes and 96 data fields was defined. Data elements and response values were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) reference terminology. Then HL7-CDA standard for interoperable data exchange were defined. CONCLUSION: The notifiable disease surveillance requires an integrative participation of multidisciplinary team. In this field, data interoperability is more essential due to the heterogeneous nature of health information systems. Developing of INMDS based on HL7-CDA along with SNOMED-CT codes offers an inclusive and interoperable dataset that can help make notifiable diseases data more comparable and reportable across studies and organizations. The proposed data model will be further modifications in the future according probable changes in Iran's notifiable diseases list.

Performance evaluation of selected decision tree algorithms for COVID-19 diagnosis using routine clinical data.

Background: The novel 2019 Coronavirus disease (COVID-19) poses a great threat to global public health and the economy. The earlier detection of COVID-19 is the key to its treatment and mitigating the transmission of the virus. Given that Machine Learning (ML) could be potentially useful in COVID-19 identification, we compared 7 decision tree (DT) algorithms to select the best clinical diagnostic model. Methods: A hospital-based retrospective dataset was used to train the selected DT algorithms. The performance of DT models was measured using performance criteria, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and precision-recall curves (PRC). Finally, the best decision model was obtained based on comparing the mentioned performance criteria. Results: Based on the Gini Index (GI) scoring model, 13 diagnostic criteria, including the lung lesion existence (GI= 0217), fever (GI= 0.205), history of contact with suspected people (GI= 0.188), O2 saturation rate in the blood (GI= 0.181), rhinorrhea (GI= 0.177), dyspnea (GI = 0.177), cough (GI = 0.159), history of taking the immunosuppressive drug (GI= 0.145), history of respiratory failure (ARDS) (GI= 0.141), lung lesion situation (GI= 0.133) and appearance (GI= 0.126), diarrhea (GI= 0.112), and nausea and vomiting (GI = 0.092) have been obtained as the most important criteria in diagnosing COVID-19. The results indicated that the J-48, with the accuracy= 0.85, F-Score= 0.85, ROC= 0.926, and PRC= 0.93, had the best performance for diagnosing COVID-19. Conclusion: According to the empirical results, it is promising to implement J-48 in health care settings to increase the accuracy and speed of COVID-19 diagnosis.