Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms.
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| Title: | Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms. |
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| Authors: | Rahimi M; Toxicological Research Center, Excellence Center & Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Afrash MR; Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran., Shadnia S; Toxicological Research Center, Excellence Center & Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Mostafazadeh B; Toxicological Research Center, Excellence Center & Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Evini PET; Toxicological Research Center, Excellence Center & Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Bardsiri MS; Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.; Department of Clinical Toxicology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran., Ramezani M; Department of Pharmacology, School of Medicine, Arak University of Medical Sciences, Arak, Iran. ramezanima1365@gmail.com.; Traditional and Complementary Medicine Research Center, Arak University of Medical Sciences, Arak, Iran. ramezanima1365@gmail.com. |
| Source: | BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Feb 06; Vol. 24 (1), pp. 38. Date of Electronic Publication: 2024 Feb 06. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1472-6947 |
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| DOI: | 10.1186/s12911-024-02443-0 |
