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Associative Learning01:27

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Heuristics01:21

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
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Predicting Products: Substitution vs. Elimination02:52

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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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Contingency Table01:29

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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

Chun-An Chou1, Qingtao Cao1, Shao-Jen Weng2

  • 1Department of Mechanical & Industrial Engineering, Northeastern University, USA.

Artificial Intelligence in Medicine
|March 8, 2020
PubMed
Summary

Predicting unplanned intensive care unit (ICU) transfers for critically ill patients in the emergency department (ED) is crucial. This study developed a novel decision tool identifying patient subgroups and diagnostic features linked to high-risk outcomes, improving critical care quality.

Keywords:
Association ruleCritical careEmergency departmentMixed-integer optimizationUnplanned ICU transfer

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Area of Science:

  • Critical Care Medicine
  • Medical Informatics
  • Health Services Research

Background:

  • Unplanned transfers from the emergency department (ED) to the intensive care unit (ICU) signify clinical deterioration in critically ill patients.
  • Accurate identification of patients at high risk for unplanned ICU transfer is vital for improving care quality and reducing mortality.
  • Existing prediction models often lack the nuance to address patient-specific conditions, utilizing simplistic analytical approaches.

Purpose of the Study:

  • To develop and validate a novel decision tool for predicting unplanned ICU transfers.
  • To identify diagnostic features associated with high-risk outcomes within distinct patient subgroups.
  • To provide interpretable rules for early intervention and improved critical care management.

Main Methods:

  • A mathematical optimization approach was employed to discover predictive rules.
  • Patients were stratified into four mutually exclusive subgroups based on primary ED visit reasons: infections, cardiovascular/respiratory diseases, gastrointestinal diseases, and neurological/other diseases.
  • The tool was evaluated for its ability to associate diagnostic features with unplanned transfer outcomes.

Main Results:

  • Significant rules linking diagnostic features to unplanned ICU transfer outcomes were identified for each patient subgroup.
  • The developed decision tool demonstrated prediction accuracy comparable to state-of-the-art machine learning methods, exceeding 70%.
  • The approach provides easily interpretable information regarding symptom-outcome relationships.

Conclusions:

  • The novel decision tool effectively identifies patients at high risk for unplanned ICU transfer across different clinical scenarios.
  • This interpretable, subgroup-specific approach enhances the understanding of critical care deterioration pathways.
  • The findings support the integration of this tool into clinical practice to optimize patient management and outcomes.