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Review and Preview01:13

Review and Preview

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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...
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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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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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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...
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Data Collection III01:05

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain.

Mohcine Madkour1, Driss Benhaddou2, Cui Tao1

  • 1School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX 77030, United States.

Computer Methods and Programs in Biomedicine
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PubMed
Summary

Processing temporal information from clinical narratives is crucial for building accurate clinical timelines and decision support systems. Advanced medical NLP and semantic web technologies can overcome current challenges in time extraction and reasoning.

Keywords:
Clinical temporal informationMedical NLPOntologies of timeTemporal extractionTemporal representation

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Area of Science:

  • Clinical Informatics
  • Natural Language Processing
  • Temporal Data Analysis

Background:

  • Clinical data, particularly from Electronic Health Records (EHRs), is abundant but temporal information is often implicit and complex.
  • Accurate recognition and utilization of temporal data are essential for clinical practice and research.
  • Existing infrastructure enables vast data collection, highlighting the need for effective temporal data processing.

Purpose of the Study:

  • To provide an overview of challenges in constructing clinical timelines from point-of-care data.
  • To summarize the state-of-the-art in processing temporal information within clinical narratives.
  • To identify gaps and potential advancements in temporal data handling.

Main Methods:

  • Surveying methods for time modeling and representation in clinical data.
  • Reviewing medical Natural Language Processing (NLP) techniques for temporal information extraction.
  • Examining methods for time reasoning and processing, focusing on semantic web integration.

Main Results:

  • Time processing is vital for constructing clinical timelines and decision support systems.
  • Effective temporal data handling is a critical component of Electronic Health Records (EHR) data models and operations.
  • Current methods show a gap in fully leveraging semantic web technologies.

Conclusions:

  • Extracting temporal information from clinical narratives presents significant challenges.
  • Integrating ontologies and semantic web technologies can improve annotation and resolve issues like granularity and co-reference.
  • Medical NLP techniques are key to addressing these temporal data complexities.