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Related Concept Videos

Clinical Trials01:16

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
Clinical Trials: Overview01:11

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.

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Related Experiment Video

Updated: May 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

[Mining medical knowledge from time-oriented clinical data using existing clinical knowledge].

Y Shahar1, R Moskovitich, D Klimov

  • 1Medical Informatics Research Center, The Department of Information Systems Engineering, Faculty of Engineering, Ben Gurion University of the Negev, Beer Sheva. yshaharo1@gmail.com

Harefuah
|July 27, 2013
PubMed
Summary
This summary is machine-generated.

Analyzing patient data over time reveals temporal patterns and predicts health outcomes. This research introduces computational tools for exploring clinical data, aiding healthcare professionals and researchers.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Related Experiment Videos

Last Updated: May 9, 2026

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

Published on: September 20, 2018

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Clinical Informatics
  • Data Science
  • Computational Medicine

Context:

  • Clinical data accumulates from multiple sources over time for patient groups.
  • Analysis of temporal data, including time-stamped points and intervals, offers deeper insights.
  • Integrating medical knowledge bases enhances the interpretation of complex clinical data.

Purpose:

  • To discover repeating temporal patterns and patient behavior clusters.
  • To identify temporal patterns that predict future clinical outcomes, like renal dysfunction in diabetes patients.
  • To introduce innovative computational methods for multivariate analysis of time-oriented clinical data.

Summary:

  • Presents computational methods for analyzing time-oriented clinical data, focusing on temporal pattern discovery and outcome prediction.
  • Highlights the use of time intervals and abstract interpretations alongside raw data for more efficient analysis.
  • Demonstrates tools for clinical data exploration and knowledge discovery.

Impact:

  • Enables identification of patient sub-group behaviors and prediction of significant clinical events.
  • Facilitates the use of advanced computational tools by clinicians at the point of care.
  • Supports clinical researchers and healthcare policymakers with data-driven insights for improved healthcare strategies.