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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | ARTIFICIAL INTELLIGENCE APPROACH IN OPTIMISATION OF HEPARIN DOSING IN POST COVID-19 PATIENTS DURING HEMODIALYSIS |
| Authors Name: | Dr SIVAKUMAR V , Dr SATHISH KUMAR A , Dr MONICA F , Dr PRIYADHARSHINI K |
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IJNRD_219822
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| Published Paper Id: | IJNRD2404765 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | ABSTRACT: INTRODUCTION: Hemodialysis patients have a higher risk of thrombo-embolic events and hence they require anti-coagulation therapy throughout the dialysis procedure. Post COVID patients undergoing hemodialysis experience an increased risk for dialysis circuit clotting compared to others. This study aims to optimize heparin dosing for post COVID-19 pateints during hemodialysis using machine learning (ML). METHODS: A prospective observational study, conducted at Dialysis unit, PSG hospitals, Coimbatore for a duration of 6 months. 136 samples were collected and the data included age, gender, BMI, DM-2, hypothyroidism, ionized calcium, serum creatinine, serum albumin, clotting history, COVID-19 infected and vaccine doses which was fed into Machine Learning to optimize heparin dosing. We used decision tree algorithm for ML model. RESULTS: In this study, a prototype model of Machine Learning was developed and optimization of heparin dose during hypercoagulation was done. A sample data was validated in the model which gave a promising performance with a sensitivity score of 0.86. Other factors such as serum albumin, ionized calcium, anemia, body mass index and hypothyroidism were found to influence the clotting risk in hemodialysis patients. CONCLUSION: Heparin dose optimization was carried out using the prototype model. A variation of 24.39% is observed for heparin dose given based on the protocol and the model predicted dose. This model has to be fine tuned to improve sensitivity score and model accuracy, to analyze further other influencing factors for clotting and to predict heparin dosing for more complex data. |
| Keywords: | Hemodialysis,Machine learning,Hypercoagulation,Heparin |
| Cite Article: | "ARTIFICIAL INTELLIGENCE APPROACH IN OPTIMISATION OF HEPARIN DOSING IN POST COVID-19 PATIENTS DURING HEMODIALYSIS", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.h623-h629, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404765.pdf |
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404765 Registration ID: 219822 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: h623-h629 Country: kallakurichi, Tamil Nadu, India Research Area: Pharmacy Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404765 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404765 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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