Multistage analysis method for detection of effective herb prescription from clinical data / 医学前沿
Frontiers of Medicine
;
(4): 206-217, 2018.
Artigo
em Inglês
| WPRIM
| ID: wpr-772763
ABSTRACT
Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb-symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Medicamentos de Ervas Chinesas
/
Estudos de Casos e Controles
/
China
/
Usos Terapêuticos
/
Tratamento Farmacológico
/
Pontuação de Propensão
/
Distúrbios do Início e da Manutenção do Sono
/
Medicina Tradicional Chinesa
Tipo de estudo:
Estudo diagnóstico
/
Estudo observacional
/
Estudo prognóstico
Limite:
Adolescente
/
Adulto
/
Idoso
/
Aged80
/
Criança
/
Feminino
/
Humanos
/
Masculino
País/Região como assunto:
Ásia
Idioma:
Inglês
Revista:
Frontiers of Medicine
Ano de publicação:
2018
Tipo de documento:
Artigo
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