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Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach

NCT06042595 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 2023

Last updated 2023-09-18

No results posted yet for this study

Summary

The study aims to develop a prediction model of premature treatment termination in psychosomatic hospitals using a machine learning approach.

Conditions

  • Premature Treatment Termination
  • Dropout Prediction
  • Inpatient Psychotherapy
  • Machine Learning

Interventions

BEHAVIORAL

Psychotherapy

Patients treated at the inpatient psychotherapy unit of the University Hospital receive 8 to 10 weeks of multimodal psychotherapeutic treatment. Treatment consists of individual as well as group psychodynamic therapy. Additionally, patients receive an individual combination of music, art, relaxation and body-oriented group therapy. Therapeutic treatment is provided by a multiprofessional, interdisciplinary team of psychotherapists with either a medical or psychology degree, art and music therapists, specialist nurses, social workers, and physiotherapists.

Sponsors & Collaborators

  • University Hospital Heidelberg

    lead OTHER

Principal Investigators

  • Ulrike Dinger-Ehrenthal, Prof. Dr. · Department of Psychosomatic Medicine and Psychotherapy, Medical Faculty, Heinrich-Heine University Düsseldorf

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2015-01-31
Primary Completion
2022-01-31
Completion
2022-01-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06042595 on ClinicalTrials.gov