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Related Concept Videos

Dosage Regimens: Designs and Approaches01:28

Dosage Regimens: Designs and Approaches

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Designing a dosage regimen, which refers to the manner of drug administration, is a complex process involving the selection of drug dose, route, and frequency. This process is underpinned by pharmacokinetic parameters derived from tests and population averages. These parameters are then tailored to patient-specific variables such as diagnosis, demographics, and allergy status. Once therapy commences, therapeutic response monitoring is critical and achieved through clinical and physical...
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Dosage Regimens: Partial Pharmacokinetic Parameters01:01

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It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
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Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
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Dosage Regimen: Individualization01:24

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Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
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A drug dosage regimen describes the specific instructions and schedule for administering a drug to a patient. It considers factors such as drug dosage, frequency, route of administration, and duration of treatment. Designing an appropriate dosage regimen for a patient aims to achieve a target drug concentration at the site of action.
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Understanding how a drug's concentration fluctuates within the body over time is crucial in pharmacokinetics, particularly with multiple oral doses. A graphical representation of multiple oral dosages provides insight into these dynamics. Typical accumulation curves of a drug's concentration in the body reveal a sawtooth pattern, indicating periodic peaks and troughs correlating with each dose administration and the drug's subsequent elimination.The plasma concentration at any time during an...
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Related Experiment Video

Updated: Oct 4, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Optimal dynamic treatment regime estimation using information extraction from unstructured clinical text.

Nina Zhou1,2, Robert D Brook3, Ivo D Dinov2

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.

Biometrical Journal. Biometrische Zeitschrift
|February 3, 2022
PubMed
Summary

Leveraging clinical text information extraction enhances precision medicine by improving dynamic treatment regime estimation. This approach boosts accuracy in personalizing healthcare by utilizing unstructured electronic health record data.

Keywords:
causal inferenceclinical decision makingelectronic health recordprecision medicinetext mining

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Area of Science:

  • Biomedical Informatics
  • Health Services Research
  • Computational Biology

Background:

  • Electronic health records (EHRs) offer vast data for personalized healthcare.
  • Structured EHR data can be incomplete or contain errors, limiting its utility.
  • Free-text clinical notes contain valuable patient information often missed in structured data.

Purpose of the Study:

  • To develop and evaluate a method combining information extraction (IE) from free-text EHRs with dynamic treatment regime (DTR) estimation.
  • To improve the accuracy and scope of personalized treatment strategies using comprehensive patient data.
  • To identify optimal treatment sequences for conditions like severe acute hypertension.

Main Methods:

  • Utilized named entity recognition, boundary detection, negation annotation, and regular expressions for clinical IE.
  • Developed a joint estimation approach integrating IE-derived patient characteristics with optimal DTR estimation.
  • Employed tree-based reinforcement learning (T-RL) for multistage DTR estimation.
  • Validated the method through simulations and a real-world application in blood pressure control.

Main Results:

  • Information extraction significantly improved counterfactual outcome models compared to structured EHR data alone.
  • The joint estimation approach increased the accuracy of identifying optimal treatment sequences by 14-24% in simulations.
  • Successfully identified significant blood pressure predictors from clinical notes that were missing or incomplete in structured EHRs.
  • Expanded study cohorts and candidate tailoring variables by incorporating free-text data.

Conclusions:

  • Integrating free-text clinical information extraction with dynamic treatment regime estimation enhances precision medicine.
  • This approach improves the accuracy of personalized treatment strategies and expands the available patient data.
  • The method shows promise for guiding clinical decision-making in complex conditions, improving patient outcomes.