Prediction of acute organophosphate poisoning severity using machine learning techniques

Hosseini, Sayed Masoud; Rahimi, Mitra; Afrash, Mohammad Reza; Ziaeefar, Pardis; Yousefzadeh, Parsa; Pashapour, Sanaz; Evini, Peyman Erfan Talab; Mostafazadeh, Babak; Shadnia, Shahin

doi:10.1016/j.tox.2023.153431

Cited by 5 publications

(2 citation statements)

References 53 publications

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“…The base algorithms also included Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM). These algorithms were selected based on their popularity in high-performing capability for prediction purposes and their extensive use in studies on healthcare topics [ 34 – 36 ].…”

Section: Methodsmentioning

confidence: 99%

Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study

Nopour

2024

BMC Med Inform Decis Mak

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Background and aim Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decreasing the mortality rate and improving the prognosis. So, this study. Materials and methods In this retrospective study, we used 654 alive and dead PC cases to establish the prediction model for PC. The six chosen machine learning algorithms and prognostic factors were utilized to build the prediction models. The importance of the predictive factors was assessed using the relative importance of a high-performing algorithm. Results The XG-Boost with AU-ROC of 0.933 (95% CI= [0.906–0.958]) and AU-ROC of 0.836 (95% CI= [0.789–0.865] in internal and external validation modes were considered as the best-performing model for predicting the mortality risk of PC. The factors, including tumor size, smoking, and chemotherapy, were considered the most influential for prediction. Conclusion The XG-Boost gained more performance efficiency in predicting the mortality risk of PC patients, so this model can promote the clinical solutions that doctors can achieve in healthcare environments to decrease the mortality risk of these patients.

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Section: Methodsmentioning

confidence: 99%

Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study

Nopour

2024

BMC Med Inform Decis Mak

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“…The three main variables that contributed the most to the prediction of prognosis in patients with organophosphorus poisoning were venous blood gas pH, white blood cells, and plasma cholinesterase activity. XGBoost model accuracy was 90.1%, specificity 91.4%, sensitivity 89.5%, F-measure 91.2% and the kappa statistic was 91.2% [ 28 ]. Validation of a machine learning (random forest) predictive model of blood lead levels (EBLLs) and its comparison with simple logistic regression was carried out in the study of Potash et al The AUC for the random forest was 0.69, while for logistic regression it was 0.64.…”

Section: Introductionmentioning

confidence: 99%

Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms

Rahimi,

Afrash,

Shadnia

et al. 2024

BMC Med Inform Decis Mak

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Background Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. Despite its effectiveness, there needs to be more research on this method precisely, with most studies focusing on specific poisoning. This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient undergoing hemodialysis. Methods Using a registry database from 2016 to 2022, this study conducted a retrospective cohort study at Loghman Hakim Hospital. First, the relief feature selection algorithm was used to identify the most important variables influencing the prognosis of poisoned patients undergoing hemodialysis. Second, four machine learning algorithms, including extreme gradient boosting (XGBoost), histgradient boosting (HGB), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost), were trained to construct predictive models for predicting the prognosis of poisoned patients undergoing hemodialysis. Finally, the performance of paired feature selection and machine learning (ML) algorithm were evaluated to select the best models using five evaluation metrics including accuracy, sensitivity, specificity the area under the curve (AUC), and f1-score. Result The study comprised 980 patients in total. The experimental results showed that ten variables had a significant influence on prognosis outcomes including age, intubation, acidity (PH), previous medical history, bicarbonate (HCO3), Glasgow coma scale (GCS), intensive care unit (ICU) admission, acute kidney injury, and potassium. Out of the four models evaluated, the HGB classifier stood out with superior results on the test dataset. It achieved an impressive mean classification accuracy of 94.8%, a mean specificity of 93.5 a mean sensitivity of 94%, a mean F-score of 89.2%, and a mean receiver operating characteristic (ROC) of 92%. Conclusion ML-based predictive models can predict the prognosis of poisoned patients undergoing hemodialysis with high performance. The developed ML models demonstrate valuable potential for providing frontline clinicians with data-driven, evidence-based tools to guide time-sensitive prognosis evaluations and care decisions for poisoned patients in need of hemodialysis. Further large-scale multi-center studies are warranted to validate the efficacy of these models across diverse populations.

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Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence)

Mohtarami,

Mostafazadeh,

Shadnia

et al. 2024

DARU J Pharm Sci

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Prediction of acute organophosphate poisoning severity using machine learning techniques

Cited by 5 publications

References 53 publications

Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study

Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study

Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms

Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence)

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