본 연구에서는 데이터 마이닝 기법을 적용하여 특발성 폐섬유증의 급성 악화(AE-IPF)에 대한 중약 처방 패턴을 분석하였습니다. Xu Zhiying 교수의 임상 진료에서 도출된 84건의 처방을 바탕으로 고빈도 약재, 핵심 조합 및 잠재적 처방을 식별하였으며, 이를 통해 향후 임상 및 실험적 검증을 위한 구조적 통찰을 제공하였습니다.
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본 연구에서는 데이터 마이닝 기법을 적용하여 특발성 폐섬유증의 급성 악화(AE-IPF)에 대한 중약 처방 패턴을 분석하였습니다. Xu Zhiying 교수의 임상 진료에서 도출된 84건의 처방을 바탕으로 고빈도 약재, 핵심 조합 및 잠재적 처방을 식별하였으며, 이를 통해 향후 임상 및 실험적 검증을 위한 구조적 통찰을 제공하였습니다.
특발성 폐섬유증의 급성 악화(AE-IPF)는 효과적인 치료 옵션이 제한적이고 사망률이 높은 치명적인 합병증입니다. 중약(Chinese herbal medicine)이 보조적 방법으로 점점 더 많이 사용되고 있음에도 불구하고, AE-IPF에 대한 숙련된 중의학 전문가의 처방 패턴과 잠재적 치료 원칙은 아직 충분히 규명되지 않았습니다. 본 연구는 데이터 마이닝 기법을 사용하여 Xu Zhiying 교수가 특발성 폐섬유증의 급성 악화(AE-IPF)에 사용한 중약 처방 패턴을 분석하는 것을 목표로 하였습니다. 2019년 9월부터 2020년 12월까지 절강성 중의병원(Zhejiang Provincial Hospital of Traditional Chinese Medicine)에서 치료받은 AE-IPF 진단 환자의 의료 기록을 후향적으로 검토하였습니다. 총 42명의 환자로부터 84건의 처방전을 수집하여 중의학 전승 및 컴퓨팅 플랫폼(Traditional Chinese Medicine Inheritance and Computing Platform)에 입력하였습니다. 처방 특성은 빈도 분석, 연관 규칙 마이닝 및 엔트로피 계층적 클러스터링을 사용하여 분석하였습니다. 확인된 112종의 약재 중 누적 빈도는 3,004회에 달했으며, 22종의 약재가 50회 이상 사용되었습니다. 가장 빈번하게 사용된 약재로는 황금(Radix Scutellariae), 浙貝母(Bulbus Fritillariae Thunbergii Miq.), 반하(Pinellia ternata), 연교(Forsythia suspensa), 단삼(Radix et Rhizoma Salviae Miltiorrhizae) 및 후박(Magnolia officinalis)이 포함되었습니다. 주요 경락 분포는 폐, 위, 비, 간, 대장 경락이 포함되었습니다. 연관 규칙 분석 결과, 지지도가 50~84, 신뢰도가 0.82~1.00, 향상도가 1보다 큰 11가지 핵심 약재 조합이 확인되어, 약재 조합 간의 안정적인 양의 상관관계를 나타냈습니다. 엔트로피 계층적 클러스터링을 통해 청열(heat-clearing), 거담(phlegm-resolving), 해독(toxin-removing) 및 건비(spleen-supporting) 효과를 특징으로 하는 4가지 후보 처방이 생성되었습니다. 본 연구는 AE-IPF에 대한 전문가의 처방 패턴을 체계적으로 규명하였으며, 향후 임상 및 실험적 검증을 위한 가설을 제공합니다.
Idiopathic pulmonary fibrosis (IPF) is the most common type of idiopathic interstitial pneumonia. This progressive, irreversible fibrotic lung disorder is driven by epithelial injury, fibroblast activation, and excessive extracellular matrix deposition, ultimately resulting in declining pulmonary function and respiratory failure1. Despite advances in antifibrotic therapies, including pirfenidone and nintedanib, the clinical prognosis of IPF remains poor, particularly among patients experiencing acute exacerbation of IPF (AE-IPF), which represents a devastating clinical event with rapid deterioration and high mortality. Its clinical manifestations include progressive dyspnea and a decline in lung function, among others2. Currently, the occurrence rate of IPF is increasing, with a global annual incidence ranging from 0.2 to 93.7 per 100,000 people3. During AE-IPF, there is a rapid intensification of inflammatory response, involving excessive apoptosis of alveolar epithelial cells, neutrophilic inflammation, and abnormal activation of cytokines such as IL-23 and IL-17A4, often leading to death due to respiratory failure, with a mortality rate as high as 50%5. However, current therapeutic strategies for AE-IPF remain insufficient. Although corticosteroids are widely used in clinical practice, their efficacy is controversial, and immunosuppressive treatment may be accompanied by considerable adverse effects6,7. Moreover, no standardized pharmacological intervention has been established to effectively prevent disease progression or improve survival during AE-IPF episodes. Therefore, identifying novel therapeutic strategies with multi-target regulatory potential has become an important research priority.
In the long-term clinical management of IPF, traditional Chinese medicine (TCM) has been increasingly investigated as a complementary therapeutic approach. According to TCM theory, the core pathogenesis of IPF is obstruction of the lung collaterals. AE-IPF often manifests as patterns such as turbid phlegm obstructing the lung, phlegm-heat interbinding, or dual deficiency of Qi and Yin8,9. Increasing evidence suggests that compound Chinese herbal formulas based on syndrome differentiation may regulate inflammatory responses, oxidative stress, immune imbalance, and fibrotic progression through multi-component and multi-target mechanisms. Several clinical and experimental studies have indicated potential benefits of Chinese herbal medicine in improving respiratory symptoms and delaying pulmonary fibrosis progression. However, most previous studies have focused on stable-stage IPF, whereas therapeutic principles and prescription characteristics specifically targeting AE-IPF remain insufficiently investigated. Combinations of tonifying herbs (such as Astragalus membranaceus), phlegm-resolving herbs (such as Bulbus Fritillariae Thunbergii Miq.), and heat-clearing herbs (such as Radix Scutellariae) can significantly alleviate clinical symptoms in IPF patients10,11. Nevertheless, several important knowledge gaps remain. First, existing studies of Chinese herbal medicine for IPF mainly focus on chronic disease management, while systematic investigations targeting the acute exacerbation phase are limited. Second, traditional clinical experience is often transmitted through individual case reports or expert consensus, and objective approaches for identifying prescription patterns and core herbal combinations are still lacking. Third, the potential medication principles and candidate formulas used by experienced clinicians for AE-IPF have not been sufficiently characterized using modern analytical approaches. Current evidence is predominantly centered on the stable phase of IPF, with a lack of systematic exploration into syndrome differentiation patterns, medication principles, and mechanisms of action during AE-IPF12. Furthermore, the inheritance of traditional empirical formulas often relies on case summaries, and large-sample data mining analyses are scarce, which hinders the standardization and promotion of TCM experience13,14.
Professor Xu Zhiying, a senior respiratory medicine specialist at Zhejiang Provincial Hospital of Traditional Chinese Medicine, has accumulated long-term clinical experience in the syndrome differentiation and prescription management of interstitial lung diseases, including IPF and AE-IPF11. His therapeutic approach integrates traditional TCM theories, such as phlegm-heat obstructing the lung and lung collateral obstruction, with individualized clinical decision-making. Previous studies have reported that prescription experiences of experienced TCM practitioners may provide valuable insights into the standardization and inheritance of complex herbal treatment strategies through data-mining approaches. However, the present study does not aim to establish Professor Xu’s prescriptions as universal therapeutic rules or confirm clinical efficacy, but rather to systematically characterize his prescription patterns as a representative clinical experience for hypothesis generation and future validation. Data mining (DM) methods provide a systematic approach for analyzing complex clinical prescription datasets and extracting hidden therapeutic patterns11. Through association rule analysis, clustering algorithms, and network visualization, DM methods can identify high-frequency herbs, core combinations, and potential novel formulas, thereby facilitating the transformation of empirical clinical knowledge into structured and evidence-oriented therapeutic strategies13,14. Therefore, this study aimed to systematically characterize the prescription patterns of Chinese herbal medicine used by Professor Xu Zhiying for AE-IPF based on data-mining techniques. Specifically, this study focused on identifying frequently used herbs, association patterns, and potential formula combinations rather than evaluating therapeutic efficacy. The findings were intended to provide structured information regarding expert clinical experience and generate hypotheses for subsequent clinical and experimental validation. We hypothesized that data mining analysis could reveal consistent prescription characteristics, identify core herbal combinations, and provide potential candidate formulas reflecting the therapeutic principles of AE-IPF management. The novelty of this study lies in its focus on the acute exacerbation phase of IPF, the integration of large-scale clinical prescription data with modern analytical approaches, and the systematic identification of medication rules from the experience of an expert clinician. These findings may provide structured information regarding prescription characteristics and support future clinical and experimental validation studies.
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This study was approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang Chinese Medical University (Approval No. 2020-KL-072-01). Due to the retrospective nature of the study and the use of existing medical records, informed consent was waived. Patient confidentiality was maintained throughout data extraction and analysis, and only de-identified information was used for research purposes.
Data source
Medical records of patients diagnosed with AE-IPF treated by Professor Xu at Zhejiang Provincial Hospital of Traditional Chinese Medicine from September 2019 to December 2020 were retrospectively reviewed. A total of 96 patients with AE-IPF were initially screened. After applying the predefined inclusion and exclusion criteria, 42 eligible patients were included, and 84 complete Chinese herbal medicine prescriptions were obtained for subsequent data mining analysis. The 84 prescriptions were derived from 42 patients, with each prescription representing an independent clinical adjustment based on syndrome differentiation and reflecting real-world prescribing decisions during the AE-IPF treatment period. Each prescription was considered as an independent analytical unit, and all prescriptions were derived from real-world clinical practice during the acute exacerbation phase. Diagnostic Criteria: The diagnosis of AE-IPF was established based on the diagnostic criteria for interstitial lung diseases issued by the European Respiratory Society (ERS) and the American Thoracic Society (ATS) in 201815, as well as the Chinese Expert Consensus on the Diagnosis and Treatment of Acute Exacerbation of Idiopathic Pulmonary Fibrosis formulated by the Interstitial Lung Disease Group of the Chinese Thoracic Society16. Inclusion criteria includes: meeting the diagnostic criteria for IPF and being in the acute exacerbation phase; fulfilling the diagnostic criteria for AE-IPF according to Professor Xu’s staging theory for IPF treatment, with TCM syndrome differentiation identified as phlegm-heat obstructing the lung or phlegm-dampness obstructing the lung; experiencing typical respiratory worsening or acute deterioration within one month; high-resolution computed tomography (HRCT) revealing new ground-glass opacities or consolidations superimposed on pre-existing reticulations or honeycombing; absence of confirmed pulmonary infection. Exclusion criteria includes patients with severe primary diseases such as cardiovascular and cerebrovascular disorders, hematopoietic system diseases, or psychiatric illnesses; patients complicated with pulmonary heart disease or respiratory failure; pregnant or lactating women, and those with known allergy to any component of the trial medication; patients with poor compliance (inability to undergo treatment as required, or voluntary discontinuation of treatment).
As this was a retrospective observational study based on existing medical records, randomization and blinding were not applicable. The study design focused on identifying prescription patterns rather than evaluating treatment efficacy. Therefore, clinical outcomes such as symptom improvement rate, hospitalization duration, predefined endpoints and were not analyzed in this data-mining study.
Standardization of Chinese Herbal Medicine Nomenclature
The nomenclature of herbal medicines was standardized according to the Chinese Pharmacopoeia (2015 Edition)17 and the second edition of Chinese Materia Medica (New Century Edition)18. Each herb was converted into a unified coded format before statistical analysis. Different synonyms, processing forms, and regional naming variations referring to the same medicinal material were merged into a single standardized entry. The final standardized dataset was independently reviewed by two researchers before being imported into the analysis platform. Medicinal materials without specified processing methods-such as white peony root, white atractylodes rhizome, and Ephedra-were recorded in their raw form. For herbs with multiple names that do not affect their efficacy or indications, a unified naming standard was applied.
Prescription entry and verification
The screened medications were entered into a computerize traditional chinese medicine inheritance and prescription analysis platform for subsequent data-mining analysis. To ensure completeness and accuracy, a standardized data management procedure was implemented. Two independent researchers performed data extraction and entry separately, and discrepancies were reviewed and resolved by a senior clinician with more than 15 years of experience in respiratory medicine and TCM practice. The extracted prescription information was cross-checked against the original electronic medical records. Records with incomplete prescription components, unclear herb names, or inconsistent medication information were excluded before analysis. Medication prescribing patterns were explored using the medical case analysis and prescription analysis functions of the data-mining platform.
Data mining workflow and outcome assessment
The overall analytical workflow consisted of four sequential steps. First, standardized prescription data were imported into the computerized traditional Chinese medicine data-mining platform. Second, frequency analysis was performed to identify commonly used herbs, with herb frequency and cumulative application frequency considered the primary outcome measures. Third, association rule analysis based on the Apriori algorithm was conducted to identify core herb combinations, using support and confidence values as quantitative evaluation indicators. Finally, entropy-based clustering analysis was performed to generate potential novel formulas, and the extracted combinations were interpreted according to TCM theory and previous pharmacological evidence.
Because this study aimed to identify prescription patterns rather than compare intervention effects, conventional hypothesis-testing statistics were not applicable. Descriptive statistical analyses were performed to summarize herb frequencies and therapeutic categories. The robustness of association rules was assessed using multiple quantitative indicators, including support, confidence, and lift. Support reflected the frequency of occurrence of herb combinations, confidence represented the conditional probability between associated herbs, and lift values greater than 1 indicated positive correlations beyond random co-occurrence. These quantitative parameters were used to evaluate the strength and reliability of identified prescription patterns. Association strength was quantitatively evaluated using support, confidence, and lift values, while entropy-based clustering parameters were used to identify potential formula combinations.
Herbal frequency analysis
Herb frequency was defined as the cumulative occurrence count of each herb across all 84 prescriptions. Frequency analysis was performed using Excel PivotChart to rank herbs by their total occurrences, and herbs with the highest frequencies were compiled into a high-frequency list. Based on these frequency results, we further analyzed the medicinal properties of the herbs, including the four natures (cold, hot, warm, cool), five flavors (sour, bitter, sweet, pungent, salty), and meridian tropism (e.g., lung, spleen meridians), to summarize the overall prescription characteristics and compatibility principles from the perspective of TCM property theory.
Analysis of prescription medication rules
Association rule analysis was performed using the Apriori algorithm in IBM SPSS Modeler software. Each prescription was treated as a “transaction” and each herb as an “item.” High-frequency drug combinations and their internal relationships were explored through complex network analysis. The core parameters were predefined before analysis. The minimum support threshold was set to 50 prescriptions, ensuring that only frequently observed herb combinations were retained. Because the dataset comprised 84 prescriptions, the absolute support frequency could not exceed 84 occurrences. The confidence threshold was set to 0.8, meaning that when herb A appeared, the probability of herb B co-occurrence exceeded 80%, thereby ensuring the reliability of the extracted association rules. However, because confidence is asymmetric and may be influenced by the baseline frequency of the consequent herb, lift values were additionally calculated to determine whether the observed co-occurrence exceeded random expectations. A lift value greater than 1 indicated a positive association, a value equal to 1 indicated independence, and a value less than 1 indicated a negative association. Rules meeting the criteria of support ≥50, confidence ≥0.80, and lift >1 were considered meaningful and retained for interpretation. The retained rules were exported and ranked by support, confidence, and lift values to systematically summarize the core herb combinations and potential compatibility patterns for AE-IPF.
Novel formula analysis
Subsequently, a novel formula analysis was conducted using the complex system entropy clustering method. This analysis was performed using the computerized traditional Chinese medicine data-mining platform. First, using the previously constructed formula–herb data matrix, we set the correlation degree to 8 to control the number and cohesiveness of core herb combinations, and the penalty degree to 2 to constrain association strength, thereby ensuring clinical rationality of the extracted combinations. After parameter configuration, the “Extract Combinations” function in the software was applied to automatically identify implicit, highly correlated core combinations among herbs, based on the improved mutual information method and complex entropy clustering algorithm. The platform then further clustered and integrated these core combinations to ultimately generate potential novel formulas for the treatment of AE-IPF. The reliability of the extracted core combinations and candidate formulas was further evaluated through three approaches: consistency assessment with high-frequency herb results; comparison with classical TCM therapeutic principles for AE-IPF; and interpretation based on previously reported pharmacological mechanisms of representative herbs. These procedures were used as internal validation approaches to improve the clinical interpretability of the identified prescription patterns.
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약초 빈도 분석
42명의 AE-IPF 환자로부터 얻은 84가지 TCM 처방으로 구성된 동일한 분석 데이터 세트에서 총 112종의 약재가 확인되었습니다. 한약재 사용의 전반적인 분포와 재현 패턴을 특성화하기 위해 이 데이터 세트에 대해 빈도 분석을 수행했습니다. 확인된 112종의 한약재 중 22종의 약재가 50회 이상 사용되었습니다. Radix Scutellariae, Bulbus Fritillariae Thunbergii Miq., Pinellia ternata, Forsythia suspensa, Radix et Rhizoma Salviae Miltiorrhizae, Magnolia officinalis, Platycodon grandifloras를 포함하여 가장 빈번하게 사용된 7가지 약재는 100회 이상 사용되었습니다. 모든 고빈도 약재의 전체 빈도 분포는 표 1에 제시되어 있습니다.
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IPF는 만성적이고 진행성인 섬유성 폐 질환이자 호흡기계의 난치성 상태입니다. 이는 주로 비정상적인 폐 조직 구조와 폐 내 세포외 기질의 침착으로 인해 발생합니다19. IPF 환자들은 흔히 만성 염증, 폐 기능 저하, 폐 섬유화와 같은 전형적인 특징을 보입니다20. 본 연구에서는 데이터 마이닝 방법을 통해 Xu 박사의 처방 패턴을 분석하여 AE-IPF에 자주 사용되는 약초, 핵심 약초 조합 및 잠재적인 후보 처방을 확인하였습니다.
본 연구에서 분석된 84건의 처방은 치료 효능의 증거가 아니라 실제 임상 처방 기록임을 강조해야 합니다. 이러한 처방의 가치는 AE-IPF 관리 과정에서 숙련된 의료진의 반복적인 임상 의사결정 패턴을 반영한다는 점에 있습니다. 따라서 추출된 약재 조합은 검증된 치료법이 아니라, 잠재적인 처방 특성 및 연구 가설로 해석되어야 합니다.
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저자들은 이해관계의 충돌이 없음을 밝힙니다.
본 연구는 데이터 마이닝 방법을 기반으로 한 Xu Zhiying 교수의 특발성 폐질환성 섬유화 단계별 치료 약물 패턴 연구 프로젝트(프로젝트 번호 2020ZB094)로부터 연구비를 지원받았습니다.
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| 이름 | 회사 | 카탈로그 번호 | 댓글 |
|---|---|---|---|
| 전자 의료 기록 시스템 | Zhejiang Provincial Hospital of Traditional Chinese Medicine | N/A | 병원 정보 시스템; 내부 임상 데이터베이스 |
| IBM SPSS Modeler | IBM Corp., Armonk, NY, USA | Version 18.0 | IBM SPSS Modeler 18.0 |
| Microsoft Excel | Microsoft Corporation, USA | Microsoft Office 2019 | Microsoft Office 패키지 |
| 중의학 전승 및 컴퓨팅 플랫폼 | China Academy of Chinese Medical Sciences | Version 3.5 | 상용 소프트웨어 플랫폼 |
| 중의학 전승 및 컴퓨팅 플랫폼 | China Academy of Chinese Medical Sciences | Version 2.5 | 상용 소프트웨어 플랫폼 |
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