ArticleJournal of surgical oncology2021
Predicting post-discharge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data.
Article in Journal of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
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Who cites it
20 citing papers in PubMed.
- Randomized Controlled Trial of Perioperative Telemonitoring of Patient Generated Health Data in Gastrointestinal Oncologic Surgery: Assessing Overall Feasibility and Acceptability.World journal of surgery · 2023Trial
- Sharing Patient-Generated Health Data: The Role of Healthcare Quality, Mistrust, and Discrimination Across Race-Gender Identities.Journal of racial and ethnic health disparities · 2026Article
- A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making.BMC medical informatics and decision making · 2025Review
- Patient-Reported Outcomes During Neoadjuvant Therapy for Gastrointestinal Cancer and Their Association with Postoperative Complications.Journal of gastrointestinal cancer · 2025Article
- Application of Patient-Generated Health Data Among Older Adults With Cancer: Scoping Review.Journal of medical Internet research · 2025Article
- Use of Patient-Reported Outcomes in Risk Prediction Model Development to Support Cancer Care Delivery: A Scoping Review.JCO clinical cancer informatics · 2024Article
- Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.JMIR mHealth and uHealth · 2024Article
- Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.Online journal of public health informatics · 2024Article
- Effects on Quality of Life of a Telemonitoring Platform amongst Patients with Cancer (EQUALITE): A Randomized Trial Protocol.Methods and protocols · 2024Article
- Wearable Sensors as a Preoperative Assessment Tool: A Review.Sensors (Basel, Switzerland) · 2024Review
- Association of patient activity bio-profiles with health-related quality of life in patients with newly diagnosed multiple myeloma: a prospective observational cohort study.EClinicalMedicine · 2023Article
- Mobile Postoperative Symptom Intervention Tool and Biometric Monitoring After Radical Cystectomy: Pilot Study Evaluating Feasibility, Usability, and Potential Utility.The Journal of urology · 2023Article
- Article
- Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning.The American surgeon · 2023Review
- Postoperative Patient-Reported Outcomes after Uniportal Video-Assisted Thoracoscopic Surgery Using the Perioperative Symptom Assessment for Lung Surgery Scale.Current oncology (Toronto, Ont.) · 2022Observational
- Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2022Review
- Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets.Cancers · 2022Review
- Article
- The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer.Frontiers in radiology · 2022Review
- Current Status of Out-of-Hospital Management of Cancer Patients and Awareness of Internet Medical Treatment: A Questionnaire Survey.Frontiers in public health · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
BACKGROUND AND
objectivesPost-discharge oncologic surgical complications are costly for patients, families, and healthcare systems. The capacity to predict complications and early intervention can improve postoperative outcomes. In this proof-of-concept study, we used a machine learning approach to explore the potential added value of patient-reported outcomes (PROs) and patient-generated health data (PGHD) in predicting post-discharge complications for gastrointestinal (GI) and lung cancer surgery patients.
methodsWe formulated post-discharge complication prediction as a binary classification task. Features were extracted from clinical variables, PROs (MD Anderson Symptom Inventory [MDASI]), and PGHD (VivoFit) from a cohort of 52 patients with 134 temporal observation points pre- and post-discharge that were collected from two pilot studies. We trained and evaluated supervised learning classifiers via nested cross-validation.
resultsA logistic regression model with L
conclusionsPROs and PGHDs captured through remote patient telemonitoring approaches have the potential to improve prediction performance for postoperative complications.
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Registered trials
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