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

Psychological and Sociocultural Causes of Schizophrenia01:29

Psychological and Sociocultural Causes of Schizophrenia

71
Schizophrenia, a complex psychiatric disorder, has been historically misunderstood. Early psychological theories attributed its origins to childhood trauma and unresponsive parenting. However, contemporary research largely rejects these notions, favoring the vulnerability-stress hypothesis. This model proposes that individuals with a genetic predisposition to schizophrenia may develop the disorder following exposure to significant environmental stressors. Notably, studies on high-risk...
71
Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

44
The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
44
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
  1. Home
  3. Research Domains
  5. Health Sciences
  7. Health Services And Systems
  9. Family Care
  11. Ability Of Clinical Data To Predict Readmission In Child And Adolescent Mental Health Services.
  1. Home
  3. Research Domains
  5. Health Sciences
  7. Health Services And Systems
  9. Family Care
  11. Ability Of Clinical Data To Predict Readmission In Child And Adolescent Mental Health Services.

Related Experiment Video

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K

Ability of clinical data to predict readmission in Child and Adolescent Mental Health Services.

Kaban Koochakpour1, Dipendra Pant1,2, Odd Sverre Westbye2,3

  • 1Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.

Peerj. Computer Science
|December 9, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Predicting patient readmissions in Child and Adolescent Mental Health Services (CAMHS) is challenging. Analysis of 35 years of health records showed limited predictability, highlighting the need for advanced analytical models for better patient care outcomes.

Keywords:
CAMHSClassificationClusteringMachine learning

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis
05:52

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Published on: November 21, 2013

14.9K

Related Experiment Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis
05:52

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Published on: November 21, 2013

14.9K

Area of Science:

  • Health Informatics
  • Clinical Psychology
  • Data Science

Background:

  • Child and Adolescent Mental Health Services (CAMHS) face challenges in predicting patient readmissions.
  • Accurate readmission prediction is crucial for optimizing patient care pathways and resource allocation.

Purpose of the Study:

  • To analyze the predictability of readmissions over short, medium, and long term periods within CAMHS.
  • To evaluate various predictive models using electronic health records (EHR) data.
  • To explore episode similarities using clustering techniques.

Main Methods:

  • Utilized 35 years of health records (22,643 patients, 30,938 episodes).
  • Employed data pre-processing, including handling missing values and resolving data inconsistencies.
Mental health
Patient readmission
Prediction
  • Developed binary and multi-class classifiers (Oversampled Gradient Boosting, Oversampled Random Forest) and K-prototype clustering.

    • Main Results:

    • Optimal binary classifier achieved an AUC of 0.7005; multi-class classifier achieved an AUC of 0.6368.
    • K-prototype clustering identified three optimal clusters.
    • Relationships between care intensity, case complexity, and readmission risk were observed but difficult to generalize.

    Conclusions:

    • Predicting readmissions in CAMHS remains a significant challenge.
    • Current models show limited generalizability, potentially due to clinical practices influencing discharge decisions.
    • Future research requires enhanced analytical models incorporating patient development and disease progression.