Collaborative Filtering for the Imputation of Patient Reported Outcomes

Eric Ababio Anyimadu; Clifton David Fuller; Xinhua Zhang; G Elisabeta Marai; Guadalupe Canahuate

doi:10.1007/978-3-031-68309-1_20

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Book chapter

Collaborative Filtering for the Imputation of Patient Reported Outcomes

Eric Ababio Anyimadu, Clifton David Fuller, Xinhua Zhang, G Elisabeta Marai and Guadalupe Canahuate

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), Vol.14910, pp.231-248

Lecture Notes in Computer Science, v. 14910, Springer

08/2024

DOI: 10.1007/978-3-031-68309-1_20

PMCID: PMC11503500

PMID: 39463781

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https://pmc.ncbi.nlm.nih.gov/articles/PMC11503500/pdf/nihms-2028711.pdfView

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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.

Collaborative Filtering

Head and Neck Cancer

Imputation

Details

Title: Subtitle: Collaborative Filtering for the Imputation of Patient Reported Outcomes
Creators: Eric Ababio Anyimadu - Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA
Clifton David Fuller - The University of Texas MD Anderson Cancer Center
Xinhua Zhang - University of Illinois Urbana-Champaign
G Elisabeta Marai - University of Illinois Chicago
Guadalupe Canahuate - University of Iowa
Resource Type: Book chapter
Publication Details: 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), Vol.14910, pp.231-248
Series: Lecture Notes in Computer Science; v. 14910
DOI: 10.1007/978-3-031-68309-1_20
PMID: 39463781
PMCID: PMC11503500
ISSN: 0302-9743
eISSN: 1611-3349
Publisher: Springer; Cham
Grant note: National Institutes of Health (NIH) National Cancer Institute: R01CA258827
This work was supported directly or in part by funding/resources from the National Institutes of Health (NIH) National Cancer Institute (R01CA258827); received infrastructure support from the MD Anderson Cancer Center Support Grant Head and Neck Cancer Program and Image-Driven Biologically-informed Therapy (IDBT) Program, with programmatic support from the University of Texas MD Anderson Cancer Center Charles and Daneen Stiefel Center for Head and Neck Cancer Oropharyngeal Cancer Research Program; and the MD Anderson Image-guided Cancer Therapy Program.
Language: English
Date published: 08/2024
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984740958102771

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