Machine Learning Predict Patient Outcomes from Medical Records: Spine Surgery as A Case
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- Category: Vol. 86, December 2018
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Machine Learning Predict Patient Outcomes from Medical Records: Spine Surgery as A Case
Abstract
Background: The aim of this study was to explore the ap-plications of machine learning (ML) techniques utilizing elec-tronic medical records (EMRs) in the field of spine surgery,assessing its potential impact on clinical decision-making and healthcare management. Methods: Various ML algorithms were applied to EMR data to address different aspects of spine surgery, including risk stratification, personalized treatment algorithms, clinical prognostication, and reimbursement optimization. Methods involved data extraction from EMRs, algorithm training, and validation using large datasets, often from publicly available sources. Additionally, studies assessed the integration of ML tools into clinical workflows to evaluate their efficacy in re-al-world settings. Results: Demonstrated the potential of ML in improving risk assessment, treatment planning, and postoperative out-comes prediction in spine surgery. ML models showed promis-ing accuracy in identifying high-risk patients, predicting com-plications, and optimizing resource allocation. Furthermore, the integration of ML techniques with EMRs enabled the develop-ment of personalized treatment algorithms tailored to individu-al patient profiles. In Conclusion: ML applications utilizing EMR data hold significant promise for enhancing various aspects of spine surgery, ranging from preoperative planning to postoperative care. Despite challenges such as data quality, algorithm gener-alizability, and ethical considerations, the potential benefits of ML-driven approaches in spine surgery are substantial. Future research and development efforts should focus on addressing these challenges to maximize the clinical utility of ML in spine surgery and improve patient outcomes