Trial reportPloS one2018
Learning from data to predict future symptoms of oncology patients.
Trial report in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Development of Machine Learning Systems to Predict Cancer-Related Symptoms With Validation Across a Health Care System.JCO clinical cancer informatics · 2025Article
- Deep Learning Approaches to Forecast Physical and Mental Deterioration During Chemotherapy in Patients with Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis.Journal of cancer survivorship : research and practice · 2025Article
- AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy Management.Bioengineering (Basel, Switzerland) · 2024Article
- Patients' and Clinicians' Perceptions of the Clinical Utility of Predictive Risk Models for Chemotherapy-Related Symptom Management: Qualitative Exploration Using Focus Groups and Interviews.Journal of medical Internet research · 2024Article
- Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review.JMIR cancer · 2024Review
- Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.JCO clinical cancer informatics · 2024Article
- Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study.JMIR medical informatics · 2024Article
- Predicting Chemotherapy-Related Symptom Deterioration Using Hybrid Deep Learning Architecture.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023Article
- Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time.Scientific reports · 2023Article
- Network analysis to identify symptoms clusters and temporal interconnections in oncology patients.Scientific reports · 2022Article
- Fatigue, Stress, and Functional Status are Associated With Taste Changes in Oncology Patients Receiving Chemotherapy.Journal of pain and symptom management · 2021Article
- Artificial neural networks for simultaneously predicting the risk of multiple co-occurring symptoms among patients with cancer.Cancer medicine · 2021Article
- Prediction of evening fatigue severity in outpatients receiving chemotherapy: less may be more.Fatigue : biomedicine, health & behavior · 2021Article
- Health-Related Quality of Life: Longitudinal Analysis From the Time of Breast Biopsy Into the Post-treatment Period.Frontiers in global women's health · 2021Article
- Co-occurring Gastrointestinal Symptoms Are Associated With Taste Changes in Oncology Patients Receiving Chemotherapy.Journal of pain and symptom management · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient's treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.