ArticleSensors (Basel, Switzerland)2025
Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Did you miss me? Making the most of digital phenotyping data by imputing missingness with point process models: observational study.BMJ health & care informatics · 2026Observational
- Letter to the Editor: 'Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians'.Clinical medicine (London, England) · 2026Article
- Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians.Clinical medicine (London, England) · 2026Review
- Preoperative identification of the risk factors of cervical lymph node metastasis in medullary thyroid carcinoma.Frontiers in endocrinology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In geriatric healthcare, missing data pose significant challenges, especially in systems used for frailty monitoring in elderly individuals. This study explores advanced imputation techniques used to enhance data quality and maintain model performance in a system designed to detect frailty insights. We introduce missing data mechanisms-Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR)-into a dataset collected from smart bracelets, simulating real-world conditions. Imputation methods, including Expectation-Maximization (EM), matrix completion, Bayesian networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Generative Adversarial Imputation Networks (GAINs), Variational Autoencoder (VAE), and GRU-D, were evaluated based on normalized Mean Squared Error (MSE), Mean Absolute Error (MAE), and R
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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.