research-article

A Bayesian Approach to Intervention-Based Clustering

Authors:

Boi FaltingsAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 4

Article No.: 44, Pages 1 - 23

https://doi.org/10.1145/3156683

Published: 30 January 2018 Publication History

Abstract

An important task for intelligent healthcare systems is to predict the effect of a new intervention on individuals. This is especially true for medical treatments. For example, consider patients who do not respond well to a new drug or have adversary reactions. Predicting the likelihood of positive or negative response before trying the drug on the patient can potentially save his or her life. We are therefore interested in identifying distinctive subpopulations that respond differently to a given intervention. For this purpose, we have developed a novel technique, Intervention-based Clustering, based on a Bayesian mixture model. Compared to the baseline techniques, the novelty of our approach lies in its ability to model complex decision boundaries by using soft clustering, thus predicting the effect for individuals more accurately. It can also incorporate prior knowledge, making the method useful even for smaller datasets. We demonstrate how our method works by applying it to both simulated and real data. Results of our evaluation show that our model has strong predictive power and is capable of producing high-quality clusters compared to the baseline methods.

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Cited By

Cheong CYin KCheung WFung BPoon J(2023)Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept EmbeddingACM Transactions on Intelligent Systems and Technology10.1145/362522414:6(1-20)Online publication date: 14-Nov-2023
https://dl.acm.org/doi/10.1145/3625224

Index Terms

A Bayesian Approach to Intervention-Based Clustering
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 4

Research Survey and Regular Papers

July 2018

280 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3183892

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 January 2018

Accepted: 01 October 2017

Revised: 01 September 2017

Received: 01 May 2017

Published in TIST Volume 9, Issue 4

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Cited By

Cheong CYin KCheung WFung BPoon J(2023)Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept EmbeddingACM Transactions on Intelligent Systems and Technology10.1145/362522414:6(1-20)Online publication date: 14-Nov-2023
https://dl.acm.org/doi/10.1145/3625224

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