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

Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

906
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
906
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.8K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.8K
Integrated Healthcare System01:20

Integrated Healthcare System

1.8K
An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
1.8K
Purpose of Health Records II01:19

Purpose of Health Records II

1.0K
Health records serve various essential purposes in the healthcare system. Here are some key purposes:
1.0K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

176
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
176
Purpose of Health Records I01:11

Purpose of Health Records I

1.3K
The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
1.3K

You might also read

Related Articles

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

Sort by
Same author

Community Pharmacist Preferences for Providing a Dose Administration Aid Service in Australia: A Discrete Choice Experiment.

PharmacoEconomics·2026
Same author

Perceived service quality scale for community pharmacies: tool translation of the short form and service quality exploration.

Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society·2026
Same author

Responsible preoperative opioid use for hip or knee arthroplasty (OpioidHALT): a protocol for a randomised clinical trial of pharmacist-partnered opioid tapering prior to hip or knee arthroplasty.

BMJ open·2026
Same author

A Qualitative Study to Explore the Influence of Condition Prioritisation in People With Coexisting Diabetes and Hypertension on Medication Adherence.

Health expectations : an international journal of public participation in health care and health policy·2026
Same author

Priorities for medication management information resources for people with dementia and carers: a community-driven approach using a modified Delphi method.

Age and ageing·2026
Same author

How Are Deprescribing Clinical Practice Guidelines Disseminated and Implemented? A Cross-Sectional Study of International Organisational Stakeholders.

Basic & clinical pharmacology & toxicology·2026

Related Experiment Video

Updated: Sep 1, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

504

Healthcare data integration using machine learning: A case study evaluation with health information-seeking behavior

Ardalan Mirzaei1, Parisa Aslani1, Carl R Schneider1

  • 1The University of Sydney School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, NSW, Australia.

Research in Social & Administrative Pharmacy : RSAP
|August 14, 2022
PubMed
Summary

Machine learning models effectively integrate health information-seeking behavior (HISB) data. Models achieved high accuracy in classifying HISB variables, improving data integration efficiency.

Keywords:
Data integrationDeep learningHealthInformaticsInformation seekingMachine learning

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.1K

Related Experiment Videos

Last Updated: Sep 1, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

504
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.1K

Area of Science:

  • Health Informatics
  • Data Science
  • Machine Learning

Background:

  • Healthcare data is rapidly expanding, generating numerous datasets per individual.
  • Manual data integration and variable mapping become inefficient with increasing data volume.
  • Machine learning offers automated text classification for mapping variables to a schema.

Purpose of the Study:

  • To develop and evaluate machine learning models for integrating data from health information-seeking behavior (HISB) databases.
  • To compare the performance of logistic regression, random forest, and neural network classifiers for dataset mapping.

Main Methods:

  • Selected four relevant online HISB databases for integration.
  • Conducted intra-database and inter-database mapping experiments.
  • Compared logistic regression (LR), random forest classifier (RFC), and neural network (NN) models using F1-scores.
  • Performed an ablation study using all available data to classify HISB variables.

Main Results:

  • LR achieved the highest mean F1 score in intra-database mapping (0.787).
  • Inter-database mapping performance varied by training source, with LR performing best (0.245).
  • Models achieved 90-91% accuracy classifying variables using all databases; removing one dataset improved accuracy to 95-96%.

Conclusions:

  • Machine learning, particularly neural networks, can effectively map dataset variables for data integration.
  • The developed models can accurately classify health information-seeking behavior (HISB) terms within databases.