ArticleDatabase and expert systems applications : 35th International Conference, DEXA 2024, Naples, Italy, August 26-28, 2024, Proceedings. Part I. Database and Expert Systems Applications Conference (35th : 2024 : Naples, Italy)2024
Collaborative Filtering for the Imputation of Patient Reported Outcomes.
Article in Database and expert systems applications : 35th International Conference, DEXA 2024, Naples, Italy, August 26-28, 2024, Proceedings. Part I. Database and Expert Systems Applications Conference (35th : 2024 : Naples, Italy), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Improving Survival Prediction of Head and Neck Cancer Patients by Modeling Long-term Symptom Burden.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Advancing Normal Tissue Complication Probability Modeling with Supervised Contrastive Learning for Predicting Osteoradionecrosis.Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing · 2026Article
- FlavorCharter: Development of a Smartphone App for Quantifying Food Flavor.Proceedings ... ICDM workshops. IEEE International Conference on Data Mining · 2025Article
- Dual-Attention BiLSTM for Interpretable Forecasting of Treatment Toxicities.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2025Article
- Evaluating Dimensionality Reduction for Patient-Reported Outcome-Based Survival Modeling in Patients With Head and Neck Cancer.JCO clinical cancer informatics · 2025Article
- mEnergy: Development of a Novel mHealth Multi-device Platform for Near Real-time Remote Assessment of Energy Disorders.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- PRO-Based Stratification Improves Model Prediction for Toxicity and Survival of Head and Neck Cancer Patients.IEEE journal of biomedical and health informatics · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This study addresses the prevalent issue of missing data in patient-reported outcome datasets, particularly focusing on head and neck cancer patient symptom ratings sourced from the MD Anderson Symptom Inventory. Given that many data mining and machine learning algorithms necessitate complete datasets, the accurate imputation of missing data as an initial step becomes crucial. In this study we propose, for the first time, the use of collaborative filtering for imputing missing head and neck cancer patient symptom ratings. Two configurations of collaborative filtering, namely patient-based and symptom-based, leverage known ratings to infer the missing ones. Additionally, this study compares the performance of collaborative filtering with alternative imputation methods such as Multiple Imputation by Chained Equations, Nearest Neighbor Imputation, and Linear interpolation. Performance is compared using Root Mean Squared Error and Mean Absolute Error metrics. Findings demonstrate that collaborative filtering is a viable and comparatively superior approach for imputing missing patient symptom data.
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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.