Clinical trial · Interventional
Machine Learning for Predicting and Managing Quality of Life in Lung Cancer Immunotherapy Patients
Development of a Machine Learning-Based Risk Prediction Model and Stratified Management Strategies for Quality of Life in Lung Cancer Patients Undergoing Immunotherapy
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The goal of this study is to explore whether health-related quality of life (HRQoL) can be used as a predictive indicator for lung cancer patients and to implement clinical interventions. The study addresses two main objectives: Analyzing HRQoL data of lung cancer patients undergoing immunotherapy using machine learning clustering methods to explore data patterns and build an HRQoL early warning model (already developed). Validating this HRQoL early warning model in real-world settings by classifying patients with different HRQoL characteristics and assessing the clinical value of the model
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Cancer Patients | Lung Neoplasm | ONTOLOGY_EXACT | 0.90 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Conventional care intervention | Behavioral | — | UNRESOLVED |
| Symptom cluster-based care intervention | Behavioral | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- PLACEBO_COMPARATOR
- label
- The group with milder symptoms and better quality of life
- description
- the group uses unsupervised machine learning to identify patients with severe symptoms and poor functionality who are receiving immunotherapy for non-small cell lung cancer, and implements a symptom cluster care intervention.
- interventionNames
- Behavioral: Conventional care intervention
- type
- ACTIVE_COMPARATOR
- label
- The group with more severe symptoms and poorer quality of life
- interventionNames
- Behavioral: Symptom cluster-based care intervention
Primary outcomes (2)
- measure
- EORTC QLQ C30
- timeFrame
- Two weeks after the intervention
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 1. Histologically diagnosed with lung cancer 2. Age over 18 years 3. Currently receiving immunotherapy for lung cancer 4. Good verbal communication ability 5. Informed consent signed by the patient or family member Exclusion Criteria: 1. Cognitive impairment or mental illness 2. Other severe diseases
References
Publications (0)
Data not yet available