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Related Concept Videos

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Methods of Documentation VI: Case Management Model01:15

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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Formulating and Validating Nursing Diagnosis II01:25

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Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
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Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

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A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains...
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Sensitivity, Specificity, and Predicted Value01:13

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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Receiver Operating Characteristic Plot01:15

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Retrieval-Based Diagnostic Decision Support: Mixed Methods Study.

Tassallah Abdullahi1, Laura Mercurio2, Ritambhara Singh1,3

  • 1Department of Computer Science, Brown University, Providence, RI, United States.

JMIR Medical Informatics
|June 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces CliniqIR, an information retrieval (IR) framework that enhances diagnostic decision support, especially for rare diseases with limited data. The developed ensemble model achieves state-of-the-art diagnostic predictions by combining retrieval and supervised methods.

Keywords:
EHRRAGclinical decision supportdata sparsityelectronic health recordelectronic health recordsensemble learninginformation retrievalmachine learningnatural language processingrare diseasesretrieval augmented generationretrieval-augmented learning

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Diagnostic errors significantly impact patient safety and mortality.
  • Machine learning (ML) holds promise for improving diagnostic accuracy using electronic health records.
  • Existing ML models often neglect diseases with limited available training data, hindering broad diagnostic support.

Purpose of the Study:

  • To develop an information retrieval (IR)-based framework, CliniqIR, to address data sparsity in diagnostics.
  • To facilitate broader diagnostic decision support, particularly for rare or underrepresented conditions.
  • To create a system adaptable to various IR frameworks, including dense and sparse retrieval methods.

Main Methods:

  • Developed CliniqIR using clinical text, UMLS Metathesaurus, and PubMed abstracts for broad diagnosis classification.
  • Implemented CliniqIR with both dense and sparse retrieval techniques.
  • Compared CliniqIR against ClinicalBERT (Clinical Bidirectional Encoder Representations from Transformers) in supervised and zero-shot settings.
  • Created an ensemble framework combining supervised ClinicalBERT and CliniqIR for superior performance.

Main Results:

  • CliniqIR identified correct diagnoses within the top 3 predictions on the DC3 dataset without training data.
  • CliniqIR outperformed ClinicalBERT on the MIMIC-III dataset for diagnoses with fewer than 5 training samples (avg. MRR difference of 0.10).
  • In zero-shot evaluations, CliniqIR surpassed pretrained transformer models with a mean reciprocal rank (MRR) of at least 0.10.
  • The ensemble framework demonstrated enhanced diagnostic prediction accuracy compared to individual components.

Conclusions:

  • Information retrieval (IR) is crucial for utilizing unstructured data to diagnose rare diseases.
  • The ensemble framework effectively combines supervised and retrieval-based models for comprehensive diagnostic capabilities.
  • This approach broadens the scope of diagnostic decision support systems to include infrequently encountered conditions.