Related Experiment Video
Updated: Aug 7, 2026
07:41
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Mining a clinical data warehouse to discover disease-finding associations using co-occurrence statistics
Hui Cao1, Marianthi Markatou, Genevieve B Melton
1Department of Biomedical Informatics, Columbia University, New York, NY, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|June 17, 2006
Summary
This study used co-occurrence statistics to identify disease-finding links in clinical data. Both chi2 statistics and the proportion confidence interval (PCI) method proved effective in building knowledge bases for clinical decision support.
Area of Science:
- Medical Informatics
- Clinical Data Mining
- Computational Medicine
Background:
- Clinical data warehouses contain vast amounts of information on diseases and patient findings.
- Identifying reliable associations between diseases and clinical findings is crucial for improving patient care and medical research.
- Automated systems for summarizing patient information often require robust knowledge bases of disease-finding relationships.
Purpose of the Study:
- To apply co-occurrence statistics for discovering disease-finding associations within a clinical data warehouse.
- To evaluate the accuracy of identified associations using both chi2 statistics and the proportion confidence interval (PCI) method.
- To construct knowledge bases (KB-chi2, KB-PCI) from these associations and assess their utility in clinical decision support.
Main Methods:
- Utilized co-occurrence statistics to measure the dependence between pairs of diseases and findings.
- Employed chi2 statistics and the proportion confidence interval (PCI) method for association measurement.
- Applied heuristic cutoff values for selecting significant disease-finding associations.
- Constructed two knowledge bases: KB-chi2 and KB-PCI.
Main Results:
- An intrinsic evaluation demonstrated high accuracy: 94% for chi2 statistics and 76.8% for the PCI method identified true disease-finding associations.
- An extrinsic evaluation confirmed that both KB-chi2 and KB-PCI effectively assisted in removing clinically non-informative and redundant findings.
- The developed knowledge bases proved valuable for enhancing automated problem list summarization systems.
Conclusions:
- Co-occurrence statistics, particularly chi2 and PCI methods, are effective for discovering reliable disease-finding associations in clinical data.
- The constructed knowledge bases (KB-chi2, KB-PCI) can significantly improve the quality of automated clinical summaries.
- This approach offers a valuable tool for enhancing clinical decision support and medical informatics research.
Related Concept Videos
01:11Genome-wide Association Studies-GWAS
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
01:12Statistical Software for Data Analysis and Clinical Trials
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
01:23Investigation of Disease Outbreaks
Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
01:25Statistical Methods for Analyzing Epidemiological Data
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
01:18Steps in Outbreak Investigation
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis: