جستجوی مقالات مرتبط با کلیدواژه « prediction » در نشریات گروه « پزشکی »

In trauma patients, various severity scoring indices have been developed to predict the severity of injury and mortality. This study aimed to investigate the relationship between severity scoring indices for predicting survival in moderate and severe trauma patients. This cross-sectional study was conducted among 100 trauma patients. Information on each of the Shock Index, Glasgow Coma Scale/Age/Pressure (GAP), RGAP, New Trauma Score (NTS), Mechanism/Glasgow Coma Scale/Age/Pressure (MGAP), Modified Early Warning Score (MEWS), and Trauma and Injury Severity Score (TRISS) indices was separately completed for moderate and severe trauma patients. Statistical analyses were performed using SPSS version 21. In this study, no significant differences were observed between surviving and deceased patients in terms of the Shock Index, GAP, RGAP, NTS, MGAP, MEWS, and TRISS indices. However, in all these indices, significant differences were observed between multi-trauma patients with and without morbidity. According to the ROC curve, a value less than 92.5% for the SPO2 variable was the best cutoff point for predicting the probability of death in multi-trauma patients. Also, ROC curves showed that a value higher than 95.5% for SPO2, a GCS score less than 7.5, an RR value less than 19.5, a GAP score less than 14.5, an RGAP score less than 13.5, an MGAP score less than 18, a TRISS score less than 54.2, an MEWS score higher than 3.5, and an NTS score less than 14.5 were the best ways to tell if a patient with multiple injuries was likely to be hospitalized. The GAP, RGAP, MGAP, NTS, and TRISS scoring systems performed well in predicting morbidity in multi-trauma patients. However, according to the ROC curve, the GAP and RGAP indices performed slightly better than other indices. The findings of this study can be useful in better assessing survival rates, mortality, and timely treatment and intervention in trauma patients.

The recent novel coronavirus disease 2019 (COVID-19) pandemic has underlined the importance of risk score models in public health emergencies. This study aimed to develop a risk prediction score to identify high-risk hospitalized patients for disease progression on admission.

This prospective cohort study included 171 COVID-19 patients, identified through the reverse transcription polymerase chain reaction test, admitted to Bohlool Hospital in Gonabad City, Iran, between April 4 and June 5, 2021. The patients’ demographic, clinical, and laboratory data were collected upon admission, and clinical outcomes were monitored until the end of the study. The discovery dataset (80% of the data) was used to develop the risk score model based on clinical and laboratory features and patient characteristics to predict COVID-19 progression. An additive risk score model was developed based on the regression coefficients of the significant variables in a multiple logistic regression model. The performance of the risk score model was evaluated on the validation dataset (20% of the data) using the receiver operating characteristic (ROC) curve. Statistical analyses were performed with SPSS software, version 21.

The Mean±SD for age of participants was 59.54±20.52 years, and 48.6% were male. Most patients (82.5%) fully recovered or showed improvement, while 5.2% experienced disease progression and 12.3% died. Three variables, interleukin-6, neutrophil-to-lymphocyte ratio, and lung involvement, were found to be significant in predicting risk, with a good discriminatory ability, having an area under the ROC curve of 0.970 (95% CI, 0.935%, 1.00%) in the discovery set and 0.973 (95% CI, 0.923%, 1.00%) in the validation set.

The developed risk score model in this study can be used as a clinical diagnostic tool to identify COVID-19 patients at higher risk of disease progression and aid in informed decision-making and resource utilization in similar situations, such as respiratory disease outbreaks in the post-corona era.

Although several studies have been published about COVID-19, ischemic stroke is known yet as a complicated problem for COVID-19 patients. Scientific reports have indicated that in many cases, the incidence of stroke in patients with COVID-19 leads to death. 

The obtained mathematical equation in this study can help physicians’ decision-making about treatment and identification of influential clinical factors for early diagnosis. 

In this retrospective study, data from 128 patients between March and September 2020, including their demographic information, clinical characteristics, and laboratory parameters were collected and analyzed statistically. A logistic regression model was developed to identify the significant variables in predicting stroke incidence in patients with COVID-19. 

Clinical characteristics and laboratory parameters for 128 patients (including 76 males and 52 females; with a mean age of 57.109±15.97 years) were considered as the inputs that included ventilator dependence, comorbidities, and laboratory tests, including WBC, neutrophil, lymphocyte, platelet count, C-reactive protein, blood urea nitrogen, alanine transaminase (ALT), aspartate transaminase (AST) and lactate dehydrogenase (LDH). Receiver operating characteristic–area under the curve (ROC-AUC), accuracy, sensitivity, and specificity were considered indices to determine the model capability. The accuracy of the model classification was also addressed by 93.8%. The area under the curve was 97.5% with a 95% CI.

The findings showed that ventilator dependence, cardiac ejection fraction, and LDH are associated with the occurrence of stroke and the proposed model can predict the stroke effectively.

This study aimed to predict the risk of mortality among COVID-19 patients in the central region of Iran by employing the Charlson Comorbidity Index (CCI), with adjustments made for age in the predictive model.

In this cross-sectional study, encompassing all probable, suspicious, and confirmed COVID-19 cases from the onset of the pandemic (55307 individuals), 3415 cases resulting in death were designated as the study group, while the survivors constituted the control group.

The Charlson Comorbidity Index revealed that over 11 percent of all patients had at least one underlying medical condition. Logistic regression analysis indicated a significantly elevated likelihood of mortality among patients with comorbidities. Specifically, individuals with a CCI score of 6 or higher were more than twice as likely to succumb to the virus compared to those without underlying diseases. Those with a score of 6 or more exhibited the highest odds ratio (OR 2.4; 95% CI 1.3-4.5).

The study findings underscore the heightened vulnerability of individuals to COVID-19 mortality, particularly among the elderly with pre-existing health conditions. The coexistence of age and comorbidities substantially increased the risk of death due to COVID-19 in this population. Consequently, targeted interventions and focused care strategies may be crucial for this high-risk demographic in pandemic management efforts.

Heart disease is a major public health concern with millions of reported deaths annually. Data mining techniques have received attention in recent years as a tool aiding diagnosis and prediction of heart disease cases. This systematic review examines the application of data mining methods to cardiac disease diagnosis in order to identify specific types of heart-related disease that are diagnosed using data mining techniques as well as the most successful data mining methods.

This study involved a systematic review of IEEE, Science Direct, Google Scholar, Web of Science, Scopus and MEDLINE databases from 2008 until April 2023. Inclusion criteria were original papers that used data mining methods for heart disease diagnosis. Non-English papers, those without full text, studies conducted on animals, and other types of papers (conference abstracts and letters) were excluded from the study. All the retrieved references were then assessed by title and abstract according to PRISMA, after which full texts of relevant articles were analyzed. The final sample comprised of 47 articles.

Various classification methods have been utilized to diagnose heart-related disease using different mining tools, with genetic neural network data mining method having the highest accuracy among the studied techniques. Results show that predicting cardiac disease is the most commonly performed task. The demographic, bio-clinical, personal and exercise-related attributes, as well as other features used for classification were identified. The findings suggest that data mining methods hold great potential for detecting and preventing heart disease on both individual and population scales.

The study findings have implications for the prevention and treatment of cardiac disease, especially in high-risk individuals. Data mining methods can be widely applied to detect and prevent heart disease on a population scale, as well as supporting decisions for the most suitable treatment for individual patients to prevent death and reduce treatment costs.

In this note, we focus on statistical analysis and try to show the deleterious effects of inappropriate use of statistical analysis in medical research.Recently, Foji et al published an article entitled above and showed that the dermatology life quality index can predict the quality of life in patients with neurofibromatosis (1). However, these findings are doubtful due to the following reasons:1. The mean score of quality of life (the total score of the SF-36 questionnaire) is not clear.2. Although the correlation between SF-36 and DLQI can be informative, no correlation was found in the results section.3. The main aim of the study is to predict quality of life using the dermatology life quality index but there is no related model. The reported models are about the prediction of SF-36 dimensions.4. All the reported R-squares are very low (about 10%) indicating that the proposed models are not appropriate for the prediction aims.5. In regression modeling, statistical significance reflects no information regarding the prediction capability. Therefore, the interpretation of the results is not true. For more information, reading an article entitled “to explain or to predict” is highly suggested (2).6. To investigate the prediction capability of each variable, the amount of changes in the adjusted R-squares must be reported.7. In the data analysis section, it was claimed that the significant variables in simple regression were included in the multiple regression but it was not performed. For example, in the “role limitations due to emotional problems” dimension, all of the six variables are significant in the simple model, but just one variable was entered in the multivariate model.8. Finally, the linear regression is not appropriate for molding response variables with a limited range (the scores of SF-36 are between 0 and 100). The appropriate method for these outcomes is beta regression (3).In conclusion, the research hypothesis is rejected and the dermatology life quality index cannot predict the quality of life. Thus, the conclusion of Foji’s studies is not acceptable due to the fact that is not based on reported findings.Conflict of InterestNothing to declare.

Chronic kidney disease (CKD) poses a significant health burden worldwide, affecting approximately 10 - 15% of the global population. As one of the leading non-communicable diseases, CKD is a major cause of morbidity and mortality. Early identification of CKD is crucial for reducing its adverse effects on patient health. Prompt detection can significantly lessen the harmful consequences and enhance health outcomes for individuals with CKD.

This study aimed to evaluate and compare the effectiveness of various machine learning models in predicting the occurrence of CKD.

The study involved the collection of data from a sample of 400 patients. We applied the well-established cross-industry standard process (CRISP) methodology for data mining to analyze the data. As part of this process, we efficiently handled missing data using the mode approach and addressed outliers through the interquartile range (IQR) method. We utilized sophisticated techniques, such as CatBoost (CB), random forest (RF), and artificial neural network (ANN) models to predict outcomes. For evaluation, we used the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC).

An analysis of 400 patient records in this study identified that variables like serum creatinine, packed cell volume, specific gravity, and hemoglobin were most influential in predicting CKD. The results indicated that the CB and RF models surpassed the ANN in predicting the disease. Ten critical predictors were pinpointed for accurate disease prediction.

The ensemble models in this study not only showcased remarkable speed but also demonstrated superior accuracy. These findings suggest the potential of ensemble models as an effective tool for enhancing predictive performance in similar studies.

 The purpose of this study was to develop a robust and externally predictive in silico QSAR-neural network model for predicting plasma protein binding of drugs. This model aims to enhance drug discovery processes by reducing the need for chemical synthesis and extensive laboratory testing.

 A dataset of 277 drugs was used to develop the QSAR-neural network model. The model was constructed using a Filter method to select 55 molecular descriptors. The validation set’s external accuracy was assessed through the predictive squared correlation coefficient Q2 and the root mean squared error (RMSE).

The developed QSAR-neural network model demonstrated robustness and good applicability domain. The external accuracy of the validation set was high, with a predictive squared correlation coefficient Q2 of 0.966 and a root mean squared error (RMSE) of 0.063. Comparatively, this model outperformed previously published models in the literature.

 The study successfully developed an advanced QSAR-neural network model capable of predicting plasma protein binding in human plasma for a diverse set of 277 drugs. This model’s accuracy and robustness make it a valuable tool in drug discovery, potentially reducing the need for resource-intensive chemical synthesis and laboratory testing.

In recent years, artificial intelligence (AI) has revolutionized several aspects of human life. The availability of high-dimensionality datasets with progression in high-performance computing, and innovative deep learning architectures which are the subdomains of AI, have led to promising functions of AI in the medical contexts, particularly in oncology. Regarding the capacity of AI models in recognition and learning patterns as well as associations, these systems can be utilized in various aspects of cancer research including cancer diagnosis and treatment. To be precise, AI models are able to analyze medical images such as stained histopathology slides and radiology images and consequently pave the way for cancer diagnosis, grading, classification, tumor characterization, and prognosis prediction. Moreover, AI algorithms can assess a myriad of medical data to recognize patterns and make predictions about patient treatment outcomes, enabling more personalized treatment plans. Accordingly, AIassisted cancer treatment strategies have been shown to notably improve the quality of cancer treatment with chemotherapy, immunotherapy, and even radiotherapy while reducing the treatment toxicities.

This study aims to find candidates for testicular spermatozoa retrieval biomarkers among the seminal plasma exLncRNA pairs.

A set of exLncRNA pairs with the best potential biomarkers was selected and validated in 96 NOA samples. Weighted correlation network analysis (WGCNA) and Least Absolute Shrinkage and Selection Operator were used to identify possible biomarkers for these pairs (LASSO). These pairs' potential biomarkers were identified using receiver operating curves. Confusion matrices and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), FP, false-negative rates (FNR), and F1 scores are calculated. Through F1 scores, we selected the best threshold value.

The relative differential expression of each pair in testicular spermatozoa retrieval (+) and testicular spermatozoa retrieval (-) men were validated. The six pairs displayed the best biomarker potential. Among them, CCDC37.DT-LOCI00505685 pair and LOC440934- LOCI01929088 (XR_001745218.1) pair showed the most significant potential and stability for detecting testicular spermatozoa retrieval in the selected and validated cohort.

CCDC37.DT-LOCI00505685 pair and LOC440934- LOCI01929088 (XR_001745218.1) pair have the potential to become new molecular biomarkers that could help to select clinical strategies for microdissection testicular sperm extraction.

 This study was designed to find the best statistical approach to scorpion sting predictions.

Study Design:

 A retrospective study.

 Multiple regression, seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly scorpion sting cases in El Oued province. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to quantitatively compare different models.

 In general, 96909 scorpion stings were recorded in El Oued province from 2005-2020. The incidence rate experienced a gradual decrease until 2012 and since then slight fluctuations have been noted. Scorpion stings occurred throughout the year with peaks in September followed by July and August and troughs in December and January. Sting cases were not evenly distributed across demographic groups; the most affected age group was 15-49 years, and males were more likely to be stung. Of the reported deaths, more than half were in children 15 and younger. Scorpion’s activity was conditioned by climate factors, and temperature had the highest effect. The SARIMA(2,0,2)(1,1,1)12, NNAR(1,1,2)12, and SARIMA(2,0,2)(1,1,1)12-NNAR(1,1,2)12 were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were lower than those of the NNAR model in fitting and forecasting; however, the NNAR model could produce better predictive accuracy.

 The NNAR model is preferred for short-term monthly scorpion sting predictions. An in-depth understanding of the epidemiologic triad of scorpionism and the development of predictive models ought to establish enlightened, informed, better-targeted, and more effective policies.

Air pollution is a major environmental challenge worldwide and predicting air quality is key to regulating air pollution. The extent of air pollution is quantified by the Air Quality Index (AQI). Air quality forecasting has become an important area of research. Deep Neural Networks (DNN) are useful in predicting the AQI instead of traditional methods which involve numerous computations. The aim of this research paper is to investigate the use of the deep neural networks as a framework for predicting the air quality index based on time series data of pollutants.

To resolve this problem, the study proposes a DNN to develop the best model for predicting the AQI. Long Short-Term Memory (LSTM) and Bi-directional LSTM have been introduced in the study to understand and predict the relationship between the pollutants affecting the AQI. The model’s performance is evaluated using the metrics, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and coefficient of determination (R2). To conduct the study, real-time hourly data for the period November 2017 to January 2020 from an air quality monitoring station was considered for the proposed capital region of the state of Andhra Pradesh in India.

The multivariate modeling considers seven pollutants as independent variables and AQI as the target variable. After experimenting and training the algorithm on the dataset, Bi-directional LSTM was shown to have the lowest MAE and RMSE values and the highest R2, indicating that it has the highest accuracy in AQI prediction.

The development of a capital city involves massive construction activity resulting in air pollution. The results are helpful to the authorities to monitor the quality of air of develop air quality management programs thus avoiding the impact of air pollution on health.

Diagnosing patient deterioration and preventing unexpected deaths in the emergency department is a complex task that relies on the expertise and comprehensive understanding of emergency physicians concerning extensive clinical data.

Our study aimed to predict emergency department mortality and compare different models.

During a one-month period, demographic information and records were collected from 1,000 patients admitted to the emergency department of a selected hospital in Tehran.We rigorously followed The Cross Industry Standard Process for data mining and methodically progressed through its sequential steps. We employed Cat Boost and Random Forest models for prediction purposes. To prevent overfitting, Random Forest feature selection was employed. Expert judgment was utilized to eliminate features with an importance score below 0.0095. To achieve a more thorough and dependable assessment, weimplemented a K-fold cross-validation method with a value of 5.

The Cat Boost model outperformed Random Forest significantly, showcasing an impressive mean accuracy of 0.94 (standard deviation: 0.03). Ejection fraction, urea (body waste materials), and diabetes had the greatest impact on prediction.

This study sheds light on the exceptional accuracy and efficiency of machine learning in predicting emergency department mortality, surpassing the performance of traditional models. Implementing such models can result in significant improvements in early diagnosis and intervention. This, in turn, allows for optimal resource allocation in the emergency department, preventing the excessive consumption of resources and ultimately saving lives while enhancing patient outcomes.

Total hip arthroplasty (THA) is an effective surgery for patients with end-stage hip joint degenerative arthritis. This study aimed to determine peri-operative factors that impact the length of stay (LOS) and design a formula to predict LOS in patients undergoing THA.

This cross-sectional study was performed from September 2019 to January 2020. For this study, all patients who underwent THA over a period of 12 years since 2005 were included in the study. Data about the LOS and several variables including demographic variables, surgery-related variables, transfusion, intensive care unit (ICU) admission, past drug history, comorbidities, and laboratory data, were gathered. Qualitative variables are presented as numbers (%), and quantitative variables are presented as mean Mann± standard deviation. Mann Whitney test , Kruskal-Wallis test, and Spearman’s rank correlation test were also used.

A total of 524 patients were included in the study; 12 were excluded .261 (51%) were female and 251(49%) male. The mean age was 56.13±17.04 years. In the univariate analysis, the day of admission, surgery indication, transfusion, diabetes mellitus, oral anti-diabetic drugs, American Society of Anesthesiology (ASA) score, preoperative hemoglobin (Hb) level, and type of prosthesis showed significant relation with LOS. Significant variables entered to zero truncated negative binomial regression. Among them, the day of admission, ASA score, preoperative Hb level, and type of prosthesis showed significant relation with LOS (P < 0.05) and were used for model design.

Preoperative Hb level, ASA score, day of admission, and prosthesis type have an impact on LOS and can predict LOS in patients who are candidates for THA.

Colorectal cancer remains a significant health challenge, particularly in its advanced Stage III. Timely forecasting of recurrence and metastasis in these patients is crucial for optimizing postoperative care and treatment strategies. The aim of this study is to predict the likelihood of recurrence and metastasis in stage III colorectal cancer patients who have undergone laparoscopic surgery and laparotomy.

In this retrospective analysis, a total of 528 patients with Stage III colorectal cancer were included. Among them, 386 underwent laparoscopy, and 142 underwent laparotomies. logistic regression was employed to assess the influence of the surgical approach on the binary outcomes of recurrence and metastasis. The data were analyzed using SPSS 25, and Odds Ratios along with significance testing were performed with a threshold of p < 0.05 to determine statistical significance.

In the laparoscopy group, the recurrence rate was 23.7%, and although older patients (61-98 years) exhibited a higher risk of recurrence (Odds Ratio:1.88, 95% CI:0.92-3.84, p=0.083), this difference did not reach statistical significance. Gender did not significantly impact recurrence. In the laparotomy group, the recurrence rate was 29.6%, and neither age nor gender had a significant influence on recurrence. Notably, in the laparoscopy group, metastasis was significantly associated with age (Odds Ratio:5.044, 95% CI:2.08-12.23, p=0.001), while gender did not play a significant role in metastasis. Similarly, in the laparotomy group, neither age nor gender significantly affected metastasis.

This study underscores age's influence on recurrence and metastasis rates in laparoscopic treatment for stage III colorectal cancer, highlighting the need for tailored approaches in elderly patients. In contrast, laparotomy seems to be less affected by age, with tumor size emerging as a crucial predictor of disease progression. Surgical approach significantly impacts outcomes in stage III colorectal cancer, with age affecting laparoscopy outcomes more than laparotomy. These findings emphasize the importance of personalized treatments and call for further research to validate results and enhance patient outcomes in advanced colorectal cancer.

The global COVID-19 pandemic has led to a health crisis, emphasizing the need to identify high-risk patients for effective resource allocation and prioritized hospitalization. Previous studies have been limited in their use of algorithms and variables, while this research expands to include lifestyle factors and optimizes hyperparameters for twenty machine learning algorithms, enhancing prediction accuracy and identifying key predictors.

In this cross-sectional study, we analyzed data from 207 COVID-19 patients. The Boruta algorithm was used to select the best features for twenty classification algorithms, and RandomizedSearchCV was utilized to optimize hyperparameters. The models were evaluated using performance metrics such as accuracy, f-measure, and area under the curve (AUC).

The study identified eight key predictors of COVID-19 hospitalization, which include gamma-glutamyl transpeptidase, alkaline phosphatase, diagnosis by CT scan, mean platelet volume, mean corpuscular volume, fasting blood sugar, red blood cell count, and mean corpuscular hemoglobin concentration. By optimizing the hyperparameters of twenty machine learning algorithms, the accuracy and AUC were improved. With an outstanding AUC of 81.25, the XGBClassifier model exhibited superior performance.

The findings of this study can assist clinicians in allocating resources effectively and improving patient care. Additionally, this approach can aid healthcare researchers in leveraging artificial intelligence to manage diseases.