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Related Concept Videos

Data Collection I01:30

Data Collection I

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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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Data Collection II01:29

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Data Collection by Survey01:07

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Data Collection by Observations01:08

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
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Document retrieval on repetitive string collections.

Travis Gagie1, Aleksi Hartikainen2, Kalle Karhu3

  • 1CeBiB - Center of Biotechnology and Bioengineering, School of Computer Science and Telecommunications, Diego Portales University, Santiago, Chile.

Information Retrieval
|June 10, 2017
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Summary
This summary is machine-generated.

This study introduces novel indexing techniques for repetitive string collections, significantly reducing space usage and enabling efficient document retrieval operations like listing, top-k retrieval, and counting.

Keywords:
Document retrieval on stringsRepetitive string collectionsSuffix trees and arrays

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Data Compression

Background:

  • Fast-growing string collections are often repetitive, with many similar documents.
  • Existing document retrieval techniques are less developed for generic and repetitive string collections.
  • Exploiting repetitiveness can drastically reduce space usage in large collections.

Purpose of the Study:

  • To develop efficient indexing methods for repetitive string collections.
  • To enable fast document retrieval operations on these compressed collections.
  • To address the challenges posed by the increasing size and repetitiveness of data.

Main Methods:

  • Development of two novel techniques: interleaved Longest Common Prefixes (LCPs) and precomputed document lists.
  • Creation of highly compressed indexes for document listing, top-k retrieval, and document counting.
  • Adaptation of classical data structures for compressibility on repetitive data.

Main Results:

  • Achieved highly compressed indexes for efficient document retrieval.
  • Demonstrated the compressibility of classical data structures on repetitive data.
  • Successfully combined developed tools to solve ranked multi-term queries.

Conclusions:

  • Novel indexing techniques significantly improve space efficiency and retrieval performance for repetitive string collections.
  • The developed methods provide effective solutions for document listing, top-k retrieval, and counting.
  • The approach offers a robust framework for handling large, repetitive datasets in information retrieval.