Machine Learning Applied to EHRs Data of Patients With Sarcoma
AMLAS
Computational Analysis Using Machine Learning Algorithms of Electronic Medical Record Data From Patients With Osteosarcoma or Ewing's Sarcoma
1 other identifier
observational
700
0 countries
N/A
Brief Summary
Application of computational statistics and machine learning methods to data derived from electronic health records of patients diagnosed with sarcoma.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jan 2003
Longer than P75 for all trials
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
January 1, 2003
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 31, 2012
CompletedStudy Completion
Last participant's last visit for all outcomes
December 31, 2012
CompletedFirst Submitted
Initial submission to the registry
October 2, 2025
CompletedFirst Posted
Study publicly available on registry
October 10, 2025
CompletedOctober 10, 2025
September 1, 2025
10 years
October 2, 2025
October 2, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Survival
Survival of patients during the follow-up
6 months
Study Arms (2)
Osteosarcoma
Data of patients diagnosed with osteosarcoma
Ewing sarcoma
Data of patients diagnosed with Ewing sarcoma
Interventions
Eligibility Criteria
Information unavailable.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Related Publications (5)
Fernandes K, Chicco D, Cardoso JS, Fernandes J. Supervised deep learning embeddings for the prediction of cervical cancer diagnosis. PeerJ Comput Sci. 2018 May 14;4:e154. doi: 10.7717/peerj-cs.154. eCollection 2018.
PMID: 33816808BACKGROUNDChicco D, Oneto L. Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma. Health Informatics J. 2021 Jan-Mar;27(1):1460458220984205. doi: 10.1177/1460458220984205.
PMID: 33504243BACKGROUNDChicco D, Haupt R, Garaventa A, Uva P, Luksch R, Cangelosi D. Computational intelligence analysis of high-risk neuroblastoma patient health records reveals time to maximum response as one of the most relevant factors for outcome prediction. Eur J Cancer. 2023 Nov;193:113291. doi: 10.1016/j.ejca.2023.113291. Epub 2023 Aug 19.
PMID: 37708628BACKGROUNDCerono G, Melaiu O, Chicco D. Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme. J Healthc Inform Res. 2023 Sep 20;8(1):1-18. doi: 10.1007/s41666-023-00138-1. eCollection 2024 Mar.
PMID: 38273986BACKGROUNDChicco D, Oneto L, Cangelosi D. DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma. BioData Min. 2025 Jun 12;18(1):40. doi: 10.1186/s13040-025-00455-8.
PMID: 40506780BACKGROUND
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
October 2, 2025
First Posted
October 10, 2025
Study Start
January 1, 2003
Primary Completion
December 31, 2012
Study Completion
December 31, 2012
Last Updated
October 10, 2025
Record last verified: 2025-09