Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by
Same author

When AI Writes Back: Ethical Considerations by Physicians on AI-Drafted Patient Message Replies.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

Large Language Model-Based Chatbots and Agentic AI for Mental Health Counseling: Systematic Review of Methodologies, Evaluation Frameworks, and Ethical Safeguards.

JMIR AI·2026
Same author

Clinical Efficiency and Radiation Safety of Fluoroscopy-Based 2D Intraoperative Computer Navigation in Biportal Spinal Endoscopy.

International journal of spine surgery·2025
Same author

Barriers to Digital Mental Health Services Among College Students.

Studies in health technology and informatics·2025
Same author

SurgeryLSTM: a time-aware neural model for accurate and explainable length of stay prediction after spine surgery.

JAMIA open·2025
Same author

Clinical outcomes of posterior cervical fusion in the setting of increasing age and medical complexity: an American national database analysis from 2012 to 2022.

Asian spine journal·2025
Same journal

Use of Artificial Intelligence in Screening for Adolescent Idiopathic Scoliosis: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Movement Related Biomechanics in Adolescent Idiopathic Scoliosis: A Review of Reviews.

Studies in health technology and informatics·2026
Same journal

The Impact of Surgical Correction of Adolescent Idiopathic Scoliosis Using Posterior Spinal Fusion on Selected Radiological Parameters and Respiratory Function.

Studies in health technology and informatics·2026
Same journal

Acute Effect of Physio-logic® Exercises on Muscle Tone and Stiffness in Adolescent Idiopathic Scoliosis Patients: A Preliminary Study.

Studies in health technology and informatics·2026
Same journal

Effects of Integrated Music and Occupational Therapy on Motor and Autonomic Function in Children with Neurogenic Scoliosis.

Studies in health technology and informatics·2026
Same journal

Post-Operative Pain Patterns and Trunk Alignment in Patients Following Surgery for Adolescent Idiopathic Scoliosis (AIS): From Pathway to Patterns.

Studies in health technology and informatics·2026
See all related articles
  1. Home
  3. Resource-conscious Modeling For Next-day Discharge Prediction Using Clinical Notes.
  1. Home
  3. Resource-conscious Modeling For Next-day Discharge Prediction Using Clinical Notes.

Related Experiment Videos

Resource-Conscious Modeling for Next-Day Discharge Prediction Using Clinical Notes.

Ha Na Cho1, Sairam Sutari1, Alexander Lopez2

  • 1University of California, Irvine, Donald Bren School of Information, Department of Informatics.

Studies in Health Technology and Informatics
|May 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Predicting surgical patient discharge is crucial. Simple TF-IDF models outperformed complex language models for next-day discharge prediction, showing better performance in imbalanced datasets.

Keywords:
Discharge predictionresource-efficient AIsmall language models

Related Experiment Videos

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Healthcare Operations

Background:

  • Accurate and timely discharge prediction is essential for efficient surgical unit management.
  • Predicting patient discharge facilitates resource allocation and reduces patient length of stay.
  • Imbalanced datasets, common in healthcare, pose challenges for predictive modeling.

Purpose of the Study:

  • To compare the effectiveness of different text analysis models for predicting next-day patient discharge.
  • To evaluate traditional methods like TF-IDF against modern approaches such as sentence embeddings and fine-tuned small language models (SLMs).

Main Methods:

  • Utilized a dataset of 3,928 postoperative patient notes.
  • Compared Term Frequency-Inverse Document Frequency (TF-IDF) with LightGBM, sentence embeddings, and LoRA-fine-tuned SLMs.
  • Assessed model performance using F1-score and Area Under the Curve (AUC) metrics on an imbalanced dataset.

    • Main Results:

    • TF-IDF models combined with LightGBM achieved the highest performance (F1 = 0.47, AUC = 0.80).
    • Sentence embeddings and SLMs demonstrated limited sensitivity for this specific task.
    • LoRA fine-tuning offered only marginal improvements in recall for the transformer-based models.

    Conclusions:

    • Lightweight and interpretable text models, such as TF-IDF, are more effective than complex transformer-based models for imbalanced next-day discharge prediction.
    • Simpler models offer a practical and efficient solution for surgical unit operations.
    • Further research could explore hybrid approaches or feature engineering for improved performance.