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Rahimi M, Hosseini SM, Mohtarami SA, Mostafazadeh B, Evini PET, Fathy M, Kazemi A, Khani S, Mortazavi SM, Soheili A, Vahabi SM, Shadnia S. Prediction of acute methanol poisoning prognosis using machine learning techniques. Toxicology 2024; 504:153770. [PMID: 38458534 DOI: 10.1016/j.tox.2024.153770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/21/2024] [Accepted: 03/03/2024] [Indexed: 03/10/2024]
Abstract
Methanol poisoning is a global public health concern, especially prevalent in developing nations. This study focuses on predicting the severity of methanol intoxication using machine learning techniques, aiming to improve early identification and prognosis assessment. The study, conducted at Loghman Hakim Hospital in Tehran, Iran. The data pertaining to individuals afflicted with methanol poisoning was retrieved retrospectively and divided into training and test groups at a ratio of 70:30. The selected features were then inputted into various machine learning methods. The models were implemented using the Scikit-learn library in the Python programming language. Ultimately, the efficacy of the developed models was assessed through ten-fold cross-validation techniques and specific evaluation criteria, with a confidence level of 95%. A total number of 897 patients were included and divided in three groups including without sequel (n = 573), with sequel (n = 234), and patients who died (n = 90). The two-step feature selection was yielded 43 features in first step and 23 features in second step. In best model (Gradient Boosting Classifier) test dataset metric by 32 features younger age, higher methanol ingestion, respiratory symptoms, lower GCS scores, type of visual symptom, duration of therapeutic intervention, ICU admission, and elevated CPK levels were among the most important features predicting the prognosis of methanol poisoning. The Gradient Boosting Classifier demonstrated the highest predictive capability, achieving AUC values of 0.947 and 0.943 in the test dataset with 43 and 23 features, respectively. This research introduces a machine learning-driven prognostic model for methanol poisoning, demonstrating superior predictive capabilities compared to traditional statistical methods. The identified features provide valuable insights for early intervention and personalized treatment strategies.
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Affiliation(s)
- Mitra Rahimi
- Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sayed Masoud Hosseini
- Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mohtarami
- Department of Computer Engineering and Information Technology (PNU), Tehran, Iran
| | - Babak Mostafazadeh
- Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Peyman Erfan Talab Evini
- Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mobin Fathy
- Students Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arya Kazemi
- Students Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sina Khani
- Students Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Mortazavi
- Students Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amirali Soheili
- Students Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Rajaie Cardiovascular Medical and Research Center, Iran university of medical sciences, Tehran, Iran
| | | | - Shahin Shadnia
- Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Mehrpour O, Hoyte C, Nakhaee S, Megarbane B, Goss F. Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures. BMC Med Inform Decis Mak 2023; 23:102. [PMID: 37264381 DOI: 10.1186/s12911-023-02188-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 05/04/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND This study aimed to compare clinical and laboratory characteristics of supra-therapeutic (RSTI) and acute acetaminophen exposures using a predictive decision tree (DT) algorithm. METHODS We conducted a retrospective cohort study using the National Poison Data System (NPDS). All patients with RSTI acetaminophen exposure (n = 4,522) between January 2012 and December 2017 were included. Additionally, 4,522 randomly selected acute acetaminophen ingestion cases were included. After that, the DT machine learning algorithm was applied to differentiate acute acetaminophen exposure from supratherapeutic exposures. RESULTS The DT model had accuracy, precision, recall, and F1-scores of 0.75, respectively. Age was the most relevant variable in predicting the type of acetaminophen exposure, whether RSTI or acute. Serum aminotransferase concentrations, abdominal pain, drowsiness/lethargy, and nausea/vomiting were the other most important factors distinguishing between RST and acute acetaminophen exposure. CONCLUSION DT models can potentially aid in distinguishing between acute and RSTI of acetaminophen. Further validation is needed to assess the clinical utility of this model.
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Affiliation(s)
- Omid Mehrpour
- Michigan Poison & Drug Information Center, Wayne State University School of Medicine, Detroit, MI, USA.
| | | | - Samaneh Nakhaee
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Bruno Megarbane
- Department of Medical and Toxicological Critical Care, Lariboisière Hospital, INSERM UMRS, University of Paris, Paris, 1144, France
| | - Foster Goss
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
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Mehrpour O, Hoyte C, Al Masud A, Biswas A, Schimmel J, Nakhaee S, Nasr MS, Delva-Clark H, Goss F. Deep learning neural network derivation and testing to distinguish acute poisonings. Expert Opin Drug Metab Toxicol 2023; 19:367-380. [PMID: 37395108 DOI: 10.1080/17425255.2023.2232724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 06/30/2023] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs. RESEARCH DESIGN & METHODS Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium). Two Deep Neural Networks (PyTorch and Keras) designed for multi-class classification tasks were applied. RESULTS There were 201,031 single-agent poisonings included in the analysis. For distinguishing among selected poisonings, PyTorch model had specificity of 97%, accuracy of 83%, precision of 83%, recall of 83%, and a F1-score of 82%. Keras had specificity of 98%, accuracy of 83%, precision of 84%, recall of 83%, and a F1-score of 83%. The best performance was achieved in the diagnosis of single-agent poisoning in diagnosing poisoning by lithium, sulfonylureas, diphenhydramine, calcium channel blockers, then acetaminophen, in PyTorch (F1-score = 99%, 94%, 85%, 83%, and 82%, respectively) and Keras (F1-score = 99%, 94%, 86%, 82%, and 82%, respectively). CONCLUSION Deep neural networks can potentially help in distinguishing the causative agent of acute poisoning. This study used a small list of drugs, with polysubstance ingestions excluded.Reproducible source code and results can be obtained at https://github.com/ashiskb/npds-workspace.git.
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Affiliation(s)
- Omid Mehrpour
- Michigan Poison & Drug Information Center, Wayne State University School of Medicine, Detroit, MI, USA
| | - Christopher Hoyte
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Ashis Biswas
- Department of Computer Science and Engineering, University of Colorado, Denver, CO, USA
| | - Jonathan Schimmel
- Department of Emergency Medicine, Division of Medical Toxicology, Mount Sinai Hospital Icahn School of Medicine, New York, NY, USA
| | - Samaneh Nakhaee
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Mohammad Sadegh Nasr
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA
| | | | - Foster Goss
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
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Mehrpour O, Saeedi F, Hoyte C, Goss F, Shirazi FM. Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System. BMC Pharmacol Toxicol 2022; 23:49. [PMID: 35831909 PMCID: PMC9281002 DOI: 10.1186/s40360-022-00588-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 06/27/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classification methods, including support vector machine (SVM) and decision tree (DT). METHODS This study is a retrospective cohort study of National Poison Data System (NPDS) data, the largest data repository of poisoning cases in the United States. The SVM and DT algorithms were developed using training and test datasets. We also used precision-recall and ROC curves and Area Under the Curve value (AUC) for model evaluation. RESULTS Our model showed that acidosis, hypoglycemia, electrolyte abnormality, hypotension, elevated anion gap, elevated creatinine, tachycardia, and renal failure are the most important determinants in terms of outcome prediction of metformin poisoning. The average negative predictive value for the decision tree and SVM models was 92.30 and 93.30. The AUC of the ROC curve of the decision tree for major, minor, and moderate outcomes was 0.92, 0.92, and 0.89, respectively. While this figure of SVM model for major, minor, and moderate outcomes was 0.98, 0.90, and 0.82, respectively. CONCLUSIONS In order to predict the prognosis of metformin poisoning, machine learning algorithms might help clinicians in the management and follow-up of metformin poisoning cases.
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Affiliation(s)
- Omid Mehrpour
- Data Science Institute, Southern Methodist University, Dallas, TX, USA. .,Rocky Mountain Poison & Drug Safety, Denver Health and Hospital Authority, Denver, CO, USA.
| | - Farhad Saeedi
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran.,Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Christopher Hoyte
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran.,University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Foster Goss
- University of Colorado Hospital, Aurora, CO, USA.,Department of Emergency Medicine, University of Colorado Hospital, Aurora, CO, USA
| | - Farshad M Shirazi
- Arizona Poison & Drug Information Center, the University of Arizona, College of Pharmacy and University of Arizona, College of Medicine, Tucson, AZ, USA
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