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

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Wang, Yifei, Xu, Julia, Zhang, Jie, Xu, Hong ORCID: 0000-0002-2968-9804, Sun, Yuzhong, Miao, Yuan ORCID: 0000-0002-6712-3465 and Wen, Tiancai ORCID: 0000-0002-7102-2210 (2023) SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances. BMC Medical Informatics and Decision Making, 23. ISSN 1472-6947


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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Item type Article
DOI 10.1186/s12911-023-02141-3
Official URL
Subjects Current > FOR (2020) Classification > 4203 Health services and systems
Current > Division/Research > College of Science and Engineering
Keywords disease treatment, multiple prescription pattern, clinical data, chronic disease
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