ArticleEvidence-based complementary and alternative medicine : eCAM2015
A Novel Classification Method for Syndrome Differentiation of Patients with AIDS.
Article in Evidence-based complementary and alternative medicine : eCAM, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cluster analysis for syndromes of real-world coronary heart disease with angina pectoris.Frontiers of medicine · 2018Trial
- Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.Scientific reports · 2025Article
- Sterile alpha motif and histidine-aspartic acid domain-containing protein 1 expression and its relationship with T cell activation in human immunodeficiency virus/acquired immune deficiency syndrome patients with lung-spleen deficiency syndrome pattern.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2022Article
- SDTM: A Novel Topic Model Framework for Syndrome Differentiation in Traditional Chinese Medicine.Journal of healthcare engineering · 2022Article
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Authors and funding
11 authors.
Funding
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Abstract
We consider the analysis of an AIDS dataset where each patient is characterized by a list of symptoms and is labeled with one or more TCM syndromes. The task is to build a classifier that maps symptoms to TCM syndromes. We use the minimum reference set-based multiple instance learning (MRS-MIL) method. The method identifies a list of representative symptoms for each syndrome and builds a Gaussian mixture model based on them. The models for all syndromes are then used for classification via Bayes rule. By relying on a subset of key symptoms for classification, MRS-MIL can produce reliable and high quality classification rules even on datasets with small sample size. On the AIDS dataset, it achieves average precision and recall 0.7736 and 0.7111, respectively. Those are superior to results achieved by alternative methods.
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Registered trials
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