Leili Tapak

Bio: Leili Tapak is an academic researcher from Hamedan University of Medical Sciences. The author has contributed to research in topics: Medicine & Proportional hazards model. The author has an hindex of 9, co-authored 90 publications receiving 510 citations. Previous affiliations of Leili Tapak include Shiraz University of Medical Sciences & Iran University of Medical Sciences.

Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran

Abstract: Forecasting the time of future outbreaks would minimize the impact of diseases by taking preventive steps including public health messaging and raising awareness of clinicians for timely treatment and diagnosis. The present study investigated the accuracy of support vector machine, artificial neural-network, and random-forest time series models in influenza like illness (ILI) modeling and outbreaks detection. The models were applied to a data set of weekly ILI frequencies in Iran. The root mean square errors (RMSE), mean absolute errors (MAE), and intra-class correlation coefficient (ICC) statistics were employed as evaluation criteria. It was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequencies (RMSE = 22.78, MAE = 14.99 and ICC = 0.88 for the test set). In addition neural-network was better in outbreaks detection with total accuracy of 0.889 for the test set. The results showed that the used time series models had promising performances suggesting they could be effectively applied for predicting weekly ILI frequencies and outbreaks.

Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests

Abstract: Background: Kidney transplantation is the best alternative treatment for end-stage renal disease. Several studies have been devoted to investigate predisposing factors of graft rejection. However, there is inconsistency between the results. The objective of the present study was to utilize an intuitive and robust approach for variable selection, random survival forests (RSF), and to identify important risk factors in kidney transplantation patients. Methods: The data set included 378 patients with kidney transplantation obtained through a historical cohort study in Hamadan, western Iran, from 1994 to 2011. The event of interest was chronic nonreversible graft rejection and the duration between kidney transplantation and rejection was considered as the survival time. RSF method was used to identify important risk factors for survival of the patients among the potential predictors of graft rejection. Results: The mean survival time was 7.35±4.62 yr. Thirty-seven episodes of rejection were occurred. The most important predictors of survival were cold ischemic time, recipient's age, creatinine level at discharge, donors’ age and duration of hospitalization. RSF method predicted survival better than the conventional Cox-proportional hazards model (out-of-bag C-index of 0.965 for RSF vs. 0.766 for Cox model and integrated Brier score of 0.081 for RSF vs. 0.088 for Cox model). Conclusion: A RSF model in the kidney transplantation patients outperformed traditional Cox-proportional hazard model. RSF is a promising method that may serve as a more intuitive approach to identify important risk factors for graft rejection.

The impact of indoor air temperature on the executive functions of human brain and the physiological responses of body.

Abstract: Background: This study aimed to investigate the effect size (ES) of air temperature on the executive functions of human brain and body physiological responses. Methods: In this empirical study, the participants included 35 male students who were exposed to 4 air temperature conditions of 18°C, 22°C, 26°C and 30°C in 4 separate sessions in an air conditioning chamber. The participants were simultaneously asked to take part in the N-back test. The accuracy, electrocardiogram (ECG) signals and the respiration rate were recorded to determine the effect of air temperature. Results: Compared to moderate air temperatures (22°C), high (30°C) and low (18°C) air temperatures had a much more profound effect on changes in heart beat rate, the accuracy of brain executive functions and the response time to stimuli. There were statistically significant differences in the accuracy by different workload levels and various air temperature conditions(P 0.05). Conclusion: The results confirmed that the unfavorable air temperatures may considerably affect the physiological responses and the cognitive functions among indoor employees.Therefore, providing them with thermal comfort may improve their performance within indoor environments.

Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology.

Abstract: Gastric cancer is the third leading cause of cancer-related deaths worldwide. Over 95% of gastric cancers are adenocarcinomas, which are typically classified based on anatomic location and histologic type. Gastric cancer generally carries a poor prognosis because it is often diagnosed at an advanced stage. Systemic therapy can provide palliation, improved survival, and enhanced quality of life in patients with locally advanced or metastatic disease. The implementation of biomarker testing, especially analysis of HER2 status, microsatellite instability (MSI) status, and the expression of programmed death-ligand 1 (PD-L1), has had a significant impact on clinical practice and patient care. Targeted therapies including trastuzumab, nivolumab, and pembrolizumab have produced encouraging results in clinical trials for the treatment of patients with locally advanced or metastatic disease. Palliative management, which may include systemic therapy, chemoradiation, and/or best supportive care, is recommended for all patients with unresectable or metastatic cancer. Multidisciplinary team management is essential for all patients with localized gastric cancer. This selection from the NCCN Guidelines for Gastric Cancer focuses on the management of unresectable locally advanced, recurrent, or metastatic disease.

COVID-19 outbreak prediction with machine learning

Abstract: Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.