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Related Concept Videos

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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Methods of Classification and Identification01:28

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Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Data Collection by Observations01:08

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model version 5.0.

Shreya Chakrabarti1, Anando Sen1, Vojtech Huser2

  • 1Department of Biomedical Informatics, Columbia University, New York NY 10032.

Journal of Healthcare Informatics Research
|August 5, 2017
PubMed
Summary
This summary is machine-generated.

Automating cohort identification in electronic health records (EHR) using a novel algorithm significantly reduces time and cost. This similarity-based approach, leveraging the OMOP Common Data Model, achieves high accuracy for clinical trial recruitment.

Keywords:
Case-based Reasoning (CBR)Cohort IdentificationElectronic Health Records (EHR)Observational Medical Outcomes Partnership (OMOP)PhenotypeSimilarity-based

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Area of Science:

  • Biomedical Informatics
  • Health Informatics
  • Clinical Research Informatics

Background:

  • Cohort identification for clinical studies is a complex, resource-intensive process.
  • Automated methods are highly sought after to improve efficiency in biomedical research.
  • Electronic Health Records (EHR) contain vast data potential for cohort discovery.

Purpose of the Study:

  • To develop and validate a high-throughput, similarity-based algorithm for automated cohort identification.
  • To implement the algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) for interoperability.
  • To assess the algorithm's performance in retrospective cohort identification for clinical trials.

Main Methods:

  • Application of numerical abstractions on EHR data to create a similarity-based algorithm.
  • Implementation within the OMOP Common Data Model (CDM) framework.
  • Validation on six retrospective clinical trial cohort identification tasks.

Main Results:

  • The algorithm achieved a high average Area Under the Curve (AUC) of 0.966.
  • Excellent average Precision at 5 (P5) of 0.983 was demonstrated.
  • Successful validation across multiple clinical trial datasets.

Conclusions:

  • The proposed interoperable method offers an efficient solution for cohort identification in EHR databases.
  • This approach has broad applicability for streamlining clinical study recruitment.
  • Further work is warranted to explore applications and address limitations.