Classification of Opioid Usage Through Semi-Supervised Learning for Total Joint Replacement Patients

Lee, Sujee; Wei, Shujing; White, Veronica M.; Bain, Philip A.; Baker, Christine; Li, Jingshan

doi:10.1109/jbhi.2020.2992973

Cited by 14 publications

(5 citation statements)

References 30 publications

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“…However, the algorithm considers the training categorization on a multidrug basis and does not consider the class weights; therefore, there is still a gap in its application in clinical practice. There has been relatively little research on prescription classification methods, and most previous research has focused on identifying prescription abuse and invalid prescriptions [ 30 , 31 ]. This study introduced the concept of weighting to the traditional ISR algorithm and the similarly threshold.…”

Section: Discussionmentioning

confidence: 99%

SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances

Wang

Zhang

et al. 2023

BMC Med Inform Decis Mak

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Background Clinical practices have demonstrated that disease treatment can be very complex. Patients with chronic diseases often suffer from more than one disease. Complex diseases are often treated with a variety of drugs, including both primary and auxiliary treatments. This complexity and multidimensionality increase the difficulty of extracting knowledge from clinical data. Methods In this study, we proposed a subgroup identification algorithm for complex prescriptions (SIAP). We applied the SIAP algorithm to identify the importance level of each drug in complex prescriptions. The algorithm quickly classified and determined valid prescription combinations for patients. The algorithm was validated through classification matching of classical prescriptions in traditional Chinese medicine. We collected 376 formulas and their compositions from a formulary to construct a database of standard prescriptions. We also collected 1438 herbal prescriptions from clinical data for automated prescription identification. The prescriptions were divided into training and test sets. Finally, the parameters of the two sub-algorithms of SIAP and SIAP-All, as well as those of the combination algorithm SIAP + All, were optimized on the training set. A comparison analysis was performed against the baseline intersection set rate (ISR) algorithm. The algorithm for this study was implemented with Python 3.6. Results The SIAP-All and SIAP + All algorithms outperformed the benchmark ISR algorithm in terms of accuracy, recall, and F1 value. The F1 values were 0.7568 for SIAP-All and 0.7799 for SIAP + All, showing improvements of 8.73% and 11.04% over the existing ISR algorithm, respectively. Conclusion We developed an algorithm, SIAP, to automatically match sub-prescriptions of complex drugs with corresponding standard or classic prescriptions. The matching algorithm weights the drugs in the prescription according to their importance level. The results of this study can help to classify and analyse the drug compositions of complex prescriptions.

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Section: Discussionmentioning

confidence: 99%

SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances

Wang

Zhang

et al. 2023

BMC Med Inform Decis Mak

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“…The SB-SVM produces promising results to overcome present competitors in providing the best trade-off between computation time and predictive performance. Similarly, with the help of EHRs, the protagonists of clinical welfare have developed several mechanisms such as admission prediction of Dementia patients [ 74 ], classification of Opioid usage for Joint Replacement patients [ 75 ], morbidity identification [ 15 ], to consider a few.…”

Section: Applications Of ML In Healthcarementioning

confidence: 99%

Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

Swain

Bhushan

Viriyasitavat

2022

Arch Computat Methods Eng

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Machine Learning (ML) has been categorized as a branch of Artificial Intelligence (AI) under the Computer Science domain wherein programmable machines imitate human learning behavior with the help of statistical methods and data. The Healthcare industry is one of the largest and busiest sectors in the world, functioning with an extensive amount of manual moderation at every stage. Most of the clinical documents concerning patient care are hand-written by experts, selective reports are machine-generated. This process elevates the chances of misdiagnosis thereby, imposing a risk to a patient's life. Recent technological adoptions for automating manual operations have witnessed extensive use of ML in its applications. The paper surveys the applicability of ML approaches in automating medical systems. The paper discusses most of the optimized statistical ML frameworks that encourage better service delivery in clinical aspects. The universal adoption of various Deep Learning (DL) and ML techniques as the underlying systems for a variety of wellness applications, is delineated by challenges and elevated by myriads of security. This work tries to recognize a variety of vulnerabilities occurring in medical procurement, admitting the concerns over its predictive performance from a privacy point of view. Finally providing possible risk delimiting facts and directions for active challenges in the future.

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“…Several recent publications in RMDs reflect the goals of precision medicine, which can be understood as the provision of the right treatment 32 , at the right dose 33 , to the right person, at the right time 34 , while minimizing unnecessary testing ,side effects and overuse issues, including opioid use and abuse [35][36][37] , specifically opioid use around TJR [38][39][40] , and to explore issues of inequity in classification 41 .…”

Section: Ai/ml For Precision Medicine: Using Data To Guide Therapy An...mentioning

confidence: 99%

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

Nelson

Arbeeva

2022

J Rheumatol

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There has been rapid growth in the use of artificial intelligence analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers given the "black box" nature of most algorithms and the unfamiliarity of the terms and lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.

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Classification of Opioid Usage Through Semi-Supervised Learning for Total Joint Replacement Patients

Cited by 14 publications

References 30 publications

SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances

SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances

Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

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