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Obesity01:24

Obesity

The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in adipocytes...
Drug Dosing: Obese Patients01:21

Drug Dosing: Obese Patients

In the United States, obesity is a prominent concern. It is linked to heightened mortality rates due to increased occurrences of conditions such as hypertension, atherosclerosis, coronary artery disease, and diabetes compared to nonobese individuals. A patient is classified as obese if their actual body weight surpasses the ideal or desirable body weight by 20%, based on Metropolitan Life Insurance Company data. Ideal body weights consider average weights and heights for males and females...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Pharmacokinetics in Obese Patients: Drug Absorption and Distribution01:25

Pharmacokinetics in Obese Patients: Drug Absorption and Distribution

Obesity significantly alters the pharmacokinetic processes of drug absorption and distribution, presenting unique challenges in medical treatment. The increased fat tissue and decreased lean muscle in obese individuals can significantly affect how drugs are absorbed into the body and distributed across different tissues. This alteration can lead to variances in the effectiveness and safety of medications, necessitating adjustments in dosing or drug selection for obese patients.One notable...
Pharmacokinetics in Obese Patients: Drug Metabolism and Excretion01:20

Pharmacokinetics in Obese Patients: Drug Metabolism and Excretion

Drug metabolism, a critical process in the liver, involves two primary phases: Phase I reactions and Phase II conjugation. Obesity introduces significant alterations in this metabolic process, primarily due to fatty infiltration of the liver, leading to conditions such as nonalcoholic fatty liver disease (NAFLD). This condition can modify the activities of both Phase I and II enzymes, impacting how drugs are metabolized in obese patients.Phase I metabolism sees variable effects across...

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Related Experiment Video

Updated: Jun 23, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Recognizing obesity and comorbidities in sparse data.

Ozlem Uzuner1

  • 1University at Albany, SUNY, Albany, NY, USA. ouzuner@albany.edu

Journal of the American Medical Informatics Association : JAMIA
|April 25, 2009
PubMed
Summary
This summary is machine-generated.

The Obesity Challenge automated the extraction of obesity and comorbidity information from clinical notes. Best systems filtered narratives, used dictionaries, and applied negation for accurate disease classification.

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Multidisciplinary Approach to Obesity Management: A Case Report
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Multidisciplinary Approach to Obesity Management: A Case Report

Published on: May 30, 2025

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Last Updated: Jun 23, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Multidisciplinary Approach to Obesity Management: A Case Report
05:10

Multidisciplinary Approach to Obesity Management: A Case Report

Published on: May 30, 2025

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • The Informatics for Integrating Biology to the Bedside (i2b2) organized a challenge to advance medical language processing on clinical narratives.
  • The challenge focused on the automated extraction of information related to obesity and its common comorbidities from patient discharge summaries.

Purpose of the Study:

  • To develop and evaluate automated systems for classifying obesity and comorbidities into four classes: Present, Absent, Questionable, or Unmentioned.
  • To address the challenge of classifying less well-represented disease classes within the provided dataset.

Main Methods:

  • Thirty teams participated, submitting 136 runs combining rule-based and machine learning approaches.
  • Systems were evaluated on their ability to classify diseases based on textual and intuitive judgments from discharge summaries.
  • Key techniques included narrative filtering, disease name projection, negation extraction, and rule-based processing.

Main Results:

  • The best performing systems for textual judgments utilized narrative filtering, dictionary projection, negation extraction, and rule-based processing.
  • Systems leveraging disease-related concepts (symptoms, medications) and general medical knowledge improved intuitive judgment classification.
  • Performance on less frequent disease classes was a key focus of the challenge.

Conclusions:

  • Automated systems can effectively classify obesity and comorbidity information from clinical narratives.
  • Advanced NLP techniques, including negation handling and knowledge integration, are crucial for accurate medical information extraction.
  • The Obesity Challenge facilitated significant advancements in applying NLP to clinical data for disease understanding.