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Related Concept Videos

Review and Preview01:13

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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...
Data: Types and Distribution01:19

Data: Types and Distribution

In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
Data Collection by Experiments01:13

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Clinical data mining: a review.

J Iavindrasana1, G Cohen, A Depeursinge

  • 1Division of Medical Informatics, University Hospitals and University of Geneva, Rue Gabrielle-Perret-Gentil 4, CH-1211Geneva 14, Switzerland. jimison.iavindrasana@sim.hcuge.ch

Yearbook of Medical Informatics
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PubMed
Summary

Clinical data mining extracts valuable knowledge from patient data. Further development is needed for wider acceptance and reproducible methods in healthcare.

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

  • Medical Informatics
  • Data Science
  • Health Services Research

Background:

  • Clinical data mining applies data mining techniques to clinical data.
  • This review provides an overview of the current practice and future challenges in the field.

Purpose of the Study:

  • To identify the status-of-practice in clinical data mining.
  • To outline the challenges and future directions for clinical data mining.

Main Methods:

  • Literature review based on Fayyad's nine data mining steps.
  • MEDLINE database search, retaining 84 relevant papers.

Main Results:

  • Key objectives include understanding data, assisting professionals, and developing methodologies.
  • Classification, particularly Bayesian classifiers, neural networks, and Support Vector Machines (SVMs), is the most common function.
  • Accuracy, sensitivity, specificity, and ROC curves are predominant performance measures.

Conclusions:

  • Clinical data mining successfully extracts novel knowledge from clinical databases.
  • Wider acceptance requires better variable description, systematic parameter reporting, understandable models, and comparative efficiency studies.
  • Developing new methodologies and infrastructures is crucial for analyzing complex medical data.