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A system for identifying and investigating unexpected response to treatment.

Ozery-Flato M, Ein-Dor L, Neuvirth H, Parush N, Kohn MS, Hu J, Aharonov R - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: The new system was developed to help researchers uncover new features associated with reduced response to treatment, and to aid physicians in identifying patients that are not responding to treatment as expected and hence deserve more attention.The results provide comprehensive visualizations, both at the cohort and the individual patient levels.We demonstrate the utility of this system in a population of diabetic patients.

View Article: PubMed Central - PubMed

Affiliation: IBM Research - Haifa, Israel.

ABSTRACT
The availability of electronic health records creates fertile ground for developing computational models for various medical conditions. Using machine learning, we can detect patients with unexpected responses to treatment and provide statistical testing and visualization tools to help further analysis. The new system was developed to help researchers uncover new features associated with reduced response to treatment, and to aid physicians in identifying patients that are not responding to treatment as expected and hence deserve more attention. The solution computes a statistical score for the deviation of a given patient's response from responses observed individuals with similar characteristics and medication regimens. Statistical tests are then applied to identify clinical features that correlate with cohorts of patients showing deviant responses. The results provide comprehensive visualizations, both at the cohort and the individual patient levels. We demonstrate the utility of this system in a population of diabetic patients.

No MeSH data available.


Patient view
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f2-2089372: Patient view

Mentions: We developed a tool that implements the methods described above and offers a user interface for applying these methods, as well as visualizing the data and analysis results. It allows users to define and manage different patient cohorts, and analyze the differences between them. Cohorts are represented in a tree-like structure allowing a series of nested definitions of cohorts. For each cohort, users can view summary statistics, review patients, and flag and add comments to specific individuals. At the level of specific patients, the tool offers a comprehensive and configurable visualization of the longitudinal clinical data. See Figure 2 for an example of the system’s patient view.


A system for identifying and investigating unexpected response to treatment.

Ozery-Flato M, Ein-Dor L, Neuvirth H, Parush N, Kohn MS, Hu J, Aharonov R - AMIA Jt Summits Transl Sci Proc (2015)

Patient view
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525242&req=5

f2-2089372: Patient view
Mentions: We developed a tool that implements the methods described above and offers a user interface for applying these methods, as well as visualizing the data and analysis results. It allows users to define and manage different patient cohorts, and analyze the differences between them. Cohorts are represented in a tree-like structure allowing a series of nested definitions of cohorts. For each cohort, users can view summary statistics, review patients, and flag and add comments to specific individuals. At the level of specific patients, the tool offers a comprehensive and configurable visualization of the longitudinal clinical data. See Figure 2 for an example of the system’s patient view.

Bottom Line: The new system was developed to help researchers uncover new features associated with reduced response to treatment, and to aid physicians in identifying patients that are not responding to treatment as expected and hence deserve more attention.The results provide comprehensive visualizations, both at the cohort and the individual patient levels.We demonstrate the utility of this system in a population of diabetic patients.

View Article: PubMed Central - PubMed

Affiliation: IBM Research - Haifa, Israel.

ABSTRACT
The availability of electronic health records creates fertile ground for developing computational models for various medical conditions. Using machine learning, we can detect patients with unexpected responses to treatment and provide statistical testing and visualization tools to help further analysis. The new system was developed to help researchers uncover new features associated with reduced response to treatment, and to aid physicians in identifying patients that are not responding to treatment as expected and hence deserve more attention. The solution computes a statistical score for the deviation of a given patient's response from responses observed individuals with similar characteristics and medication regimens. Statistical tests are then applied to identify clinical features that correlate with cohorts of patients showing deviant responses. The results provide comprehensive visualizations, both at the cohort and the individual patient levels. We demonstrate the utility of this system in a population of diabetic patients.

No MeSH data available.