Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Observational Learning

Observational Learning

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...

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Impact of molecular multimodality on neural network models for prediction tasks related to drug discovery.

Nature communications·2026

Same author

Precision Engineering of a Loop-Exchanged Variant of Sucrose Phosphorylase from <i>Streptococcus mutans</i> Enables Regioselective 4″-<i>O</i>-Glucosylation of Epigallocatechin Gallate.

Journal of agricultural and food chemistry·2026

Same author

HDAC8 impairs tight junctions in allergic rhinitis through Smad7 deacetylation.

Frontiers in immunology·2026

Same author

Concentration of DNA at the Cell Surface Dictates Transfection Efficacy: A Hyperbranched Poly(β-Amino Ester) Mediated Strategy for Enhanced Lentivirus Production.

Polymers·2026

Same author

Prediction of Osteoporosis at the Sacrum Using Opportunistic CT of the Abdomen and Pelvis: A Retrospective Feasibility Study in 277 Patients Comparing CT and QCT Data.

Journal of clinical medicine·2026

Same author

Mucosal melanoma: clinicopathological, molecular and prognostic features in a retrospective cohort.

Virchows Archiv : an international journal of pathology·2026

Search research articles

Related Experiment Video

Updated: Sep 20, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Learning Insurance Benefit Rules from Policy Texts with Small Labeled Data.

Gabriele Picco¹, Hoang Thanh Lam¹, Vanessa Lopez¹

¹IBM Research Europe, Dublin, Ireland.

Studies in Health Technology and Informatics

|June 8, 2022

Summary

Government healthcare programs can now automatically validate provider claims against complex policies. This approach uses deep learning and ontologies to extract benefit rules, improving accuracy and reducing manual review costs.

Keywords:

deep learning health policy ontology

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Sep 20, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Health Informatics
Artificial Intelligence
Natural Language Processing

Background:

Government healthcare insurance programs require robust claim validation to prevent wasteful spending and ensure adherence to medical practices.
Current claim integrity checks are labor-intensive, costly, and often limited in scope due to complex billing policies and lack of coded rules.

Purpose of the Study:

To develop an automated approach for extracting actionable knowledge on healthcare benefit rules from regulatory policy text.
To enhance the accuracy and efficiency of validating healthcare claims submitted by providers for reimbursement.

Main Methods:

A novel approach combining deep learning (DL) and ontologies was employed to process regulatory healthcare policy text.
The system was designed to extract benefit rules and demonstrated feasibility with limited labeled data from policy investigators.
The methodology leverages DL and ontological information to enable learning from human corrections.

Main Results:

The proposed system successfully extracts actionable knowledge on benefit rules from complex policy documents.
Feasibility was demonstrated even with small amounts of ground truth labeled data.
The approach captures benefit rules more effectively than deterministic methods relying solely on pre-defined patterns.

Conclusions:

Deep learning combined with ontologies offers a powerful solution for automating the extraction of healthcare benefit rules.
This approach significantly improves the ability to maintain healthcare claim integrity, reducing waste and costs.
The system's capacity to learn from human feedback enhances its adaptability and accuracy in complex regulatory environments.