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Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
Factors Influencing Drug Absorption: Disease States and Pharmacology01:25

Factors Influencing Drug Absorption: Disease States and Pharmacology

Multiple disease states can significantly influence the oral drug absorption process by affecting blood flow and the functionality of the gastrointestinal (GI) system. Various GI diseases, including conditions that alter GI motility, such as diarrhea, decreased acid secretions (achlorhydria), and infections, have been associated with reduced drug absorption.
Substances such as alcohol and specific drugs, including antineoplastics, can also negatively impact drug absorption. For instance,...
Drug Elimination: Non-Renal Routes01:23

Drug Elimination: Non-Renal Routes

The liver plays a pivotal role in eliminating drugs and their metabolites, primarily through a process known as biliary excretion. This process involves the hepatocytes, the primary cells in the liver that generate bile. A range of transporters actively expels polar drugs or hydrophilic drug metabolites into the bile, which transports the drugs and metabolites into the small intestine. From here, they are eventually expelled from the body through feces. In some instances, the original drug or a...

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Related Experiment Video

Updated: Jun 9, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Improving textual medication extraction using combined conditional random fields and rule-based systems.

Domonkos Tikk1, Illés Solt

  • 1Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary.

Journal of the American Medical Informatics Association : JAMIA
|September 8, 2010
PubMed
Summary
This summary is machine-generated.

A rule-based method achieved high performance in extracting medication details from discharge summaries, comparable to complex machine learning models. Conditional random fields (CRF) models benefit from rule-based outputs for training data augmentation.

Related Experiment Videos

Last Updated: Jun 9, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

  • Natural Language Processing
  • Clinical Informatics

Background:

  • The i2b2 Medication Extraction Challenge aimed to extract medication information from clinical notes.
  • Accurate medication data extraction is crucial for patient safety and clinical research.

Purpose of the Study:

  • To evaluate rule-based (RB) and conditional random fields (CRF) approaches for named entity identification (NEI) in medication extraction.
  • To assess the impact of different NEI methods on downstream relation extraction performance.

Main Methods:

  • A three-component pipeline was developed: NEI, context-aware filtering, and relation extraction.
  • Two NEI methods were investigated: a rule-based system and a CRF model.
  • CRF models were trained on limited ground truth data and augmented data from the rule-based system.

Main Results:

  • The rule-based NEI component achieved F(1)-scores of 80% (exact) and 81% (inexact).
  • The CRF model, when trained on augmented data, outperformed the rule-based model with F(1)-scores of 81% (exact) and 82% (inexact).

Conclusions:

  • A simple rule-based method can be as effective as complex machine learning models for medication extraction.
  • CRF models can be improved by using potentially inaccurate training data generated by rule-based methods, especially with limited ground truth.
  • This highlights a cost-effective strategy for enhancing clinical NLP model performance.