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Non-steroidal anti-inflammatory drug (NSAID) use has been documented as being widespread among older people with persistent pain. Though NSAIDs are fundamental in maintaining their quality of life, the risk of the use of multiple medications drug interactions and adverse effects is of the utmost significance as the elderly usually require multiple medications for their co-morbidities. Prescriptions are likely to expose patients to hazardous interactions with medicines and potentially deadly adverse effects if they are not properly monitored and controlled. The purpose of this study was to evaluate the appropriateness of NSAID use and analyze the risk of potential interactions with NSAIDs. The purpose of this study was to evaluate the suitability of NSAID use and examine the risk of potential reactions to NSAIDs. The model is tested on a hold-out set after being trained on a portion of the data. The model can accurately forecast the danger level of medications such as weaknesses, side effects, etc. According to the results, which may help with drug development and regulatory decision making, the proposed work performs successfully as the RMSE of Grid Search-CV LR is 0.5 and the accuracy score is 80%.

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Assessing NSAID Threat Degree of Unfavorable Medical Reactions Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Raja Vikram Gandham,
  • P. Tharun Kumar,
  • B. Mahesh Gopal,
  • M. Karthik,
  • Krishan Dev Nidumolu

摘要

Non-steroidal anti-inflammatory drug (NSAID) use has been documented as being widespread among older people with persistent pain. Though NSAIDs are fundamental in maintaining their quality of life, the risk of the use of multiple medications drug interactions and adverse effects is of the utmost significance as the elderly usually require multiple medications for their co-morbidities. Prescriptions are likely to expose patients to hazardous interactions with medicines and potentially deadly adverse effects if they are not properly monitored and controlled. The purpose of this study was to evaluate the appropriateness of NSAID use and analyze the risk of potential interactions with NSAIDs. The purpose of this study was to evaluate the suitability of NSAID use and examine the risk of potential reactions to NSAIDs. The model is tested on a hold-out set after being trained on a portion of the data. The model can accurately forecast the danger level of medications such as weaknesses, side effects, etc. According to the results, which may help with drug development and regulatory decision making, the proposed work performs successfully as the RMSE of Grid Search-CV LR is 0.5 and the accuracy score is 80%.