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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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To determine the electron configuration for any particular atom, we can build the structures in the order of atomic numbers. Beginning with hydrogen, and continuing across the periods of the periodic table, we add one proton at a time to the nucleus and one electron to the proper subshell until we have described the electron configurations of all the elements. This procedure is called the aufbau principle, from the German word aufbau (“to build up”). Each added electron occupies the...
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The quotient rule is a fundamental differentiation technique in calculus used to differentiate functions expressed as a ratio of two differentiable functions. Given a function of the form:Where g(x) and h(x) are both differentiable and h(x) ≠ 0, the derivative of f(x) is given by:Example:The quotient rule is beneficial when differentiating rational functions, trigonometric ratios, and exponential functions. For example, given:applying the quotient rule,This rule is essential in solving...
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Machine learning methods for developing precision treatment rules with observational data.

Ronald C Kessler1, Robert M Bossarte2, Alex Luedtke3

  • 1Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.

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|June 25, 2019
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Summary
This summary is machine-generated.

Developing composite precision treatment rules (PTRs) is crucial for personalized medicine. This study proposes using large electronic health records and ensemble machine learning to create more robust PTRs, overcoming limitations of small clinical trials.

Keywords:
Clinical decision supportEnsemble machine learningPersonalized treatmentPrecision treatmentSuper learner

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Area of Science:

  • Biomedical Informatics
  • Clinical Epidemiology
  • Machine Learning in Healthcare

Background:

  • Precision treatment protocols rely on diverse predictor variables, but individual predictors lack sufficient power for optimal treatment selection.
  • Composite Precision Treatment Rules (PTRs) are gaining interest but are hindered by small sample sizes in clinical trials and suboptimal analysis methods.

Purpose of the Study:

  • To propose a novel approach for developing robust composite Precision Treatment Rules (PTRs).
  • To address the limitations of small sample sizes and suboptimal analysis methods in current PTR development.
  • To leverage large observational electronic medical record databases and advanced machine learning techniques.

Main Methods:

  • Utilizing large observational electronic medical record (EHR) databases for preliminary PTR development.
  • Employing ensemble machine learning methods for statistical analyses to construct PTRs.
  • Proposing a tiered case-cohort design with innovative covariate balancing and PTR estimation methods.

Main Results:

  • The proposed approach aims to overcome challenges of non-randomized treatment assignment and missing data in observational databases.
  • Validation of preliminary PTRs in subsequent pragmatic trials is a key component of the methodology.
  • The study introduces methods for measuring and balancing baseline covariates and estimating PTRs.

Conclusions:

  • The proposed methodology offers a promising strategy to enhance the development and validation of composite Precision Treatment Rules (PTRs).
  • This approach seeks to improve personalized treatment selection by integrating data from large observational studies and advanced analytics.
  • Addressing the inherent challenges in observational data is critical for advancing precision medicine through robust PTRs.