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Related Concept Videos

Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Components of Language01:24

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Identifying symptom etiologies using syntactic patterns and large language models.

Hillel Taub-Tabib1, Yosi Shamay2, Micah Shlain1

  • 1Allen Institute for AI, Seattle, USA.

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This study introduces two novel methods for extracting medical symptom causes from literature. Combining a traditional NLP approach with advanced GPT-4 models improves diagnostic accuracy and reliability.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Differential diagnosis is essential for accurate medical practice.
  • Current resources for identifying symptom causes are limited by manual curation.
  • Novel methods are needed to extract comprehensive etiological information from scientific literature.

Purpose of the Study:

  • To introduce and analyze two novel methods for mining symptom etiologies from scientific literature.
  • To compare the coverage and precision of traditional NLP versus generative models for etiology extraction.
  • To evaluate the synergistic benefits of combining both approaches.

Main Methods:

  • A traditional Natural Language Processing (NLP) method using human-guided pattern bootstrapping for syntactic pattern extraction.
  • A novel method employing generative models (GPT-4) with a fact verification pipeline.
  • Comparative analysis of the coverage and precision of each method and their combined application.

Main Results:

  • The NLP method achieved significant coverage in extracting symptom etiologies.
  • The GPT-4 method demonstrated high precision but lower coverage compared to the NLP approach.
  • Combining both methodologies resulted in enhanced depth and reliability of etiology mining.

Conclusions:

  • Two novel methods for mining medical symptom etiologies from scientific literature have been presented.
  • A hybrid approach combining NLP and generative AI offers superior depth and reliability for differential diagnosis support.
  • These methods have the potential to improve the identification of novel or less common disease causes.