ArticleJournal of biomedical informatics2015
Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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.
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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
22 citing papers in PubMed.
- Quantifying the impact of great coaches on Olympic medal predictions: A CPD-D3 model analysis.PloS one · 2026Article
- Benchmarking Missing Data Imputation Methods for Time Series Using Real-World Test Cases.Proceedings of machine learning research · 2025Article
- Integration of sentinel surveillance and climate factors to accelerate malaria elimination in a changing climate of Senegal.Science in One Health · 2025Article
- Inflammatory burden index: associations between osteoarthritis and all-cause mortality among individuals with osteoarthritis.BMC public health · 2024Article
- Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning.Frontiers in oncology · 2024Article
- Classification of Level of Consciousness in a Neurological ICU Using Physiological Data.Neurocritical care · 2023Observational
- Binned Data Provide Better Imputation of Missing Time Series Data from Wearables.Sensors (Basel, Switzerland) · 2023Article
- Machine learning modeling practices to support the principles of AI and ethics in nutrition research.Nutrition & diabetes · 2022Article
- Comparative assessment and novel strategy on methods for imputing proteomics data.Scientific reports · 2022Article
- Determination of Reactivity Ratios from Binary Copolymerization Using the k-Nearest Neighbor Non-Parametric Regression.Polymers · 2021Article
- Automated meal detection from continuous glucose monitor data through simulation and explanation.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Metaheuristic approaches in biopharmaceutical process development data analysis.Bioprocess and biosystems engineering · 2019Review
- Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series.Proceedings of machine learning research · 2019Article
- A new approach for processing climate missing databases applied to daily rainfall data in Soummam watershed, Algeria.Heliyon · 2019Article
- Lagged Correlations among Physiological Variables as Indicators of Consciousness in Stroke Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Automated Identification of Causal Moderators in Time-Series Data.Proceedings of machine learning research · 2018Article
- Predicting adverse hemodynamic events in critically ill patients.Current opinion in critical care · 2018Review
- Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices.Sensors (Basel, Switzerland) · 2018Article
- Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning.Journal of medical Internet research · 2018Article
- Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.Journal of biomedical informatics · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Most clinical and biomedical data contain missing values. A patient's record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined. Further, the data are not simply missing at random. Instead the measurement of a variable such as blood glucose may depend on its prior values as well as that of other variables. These dependencies exist across time as well, but current methods have yet to incorporate these temporal relationships as well as multiple types of missingness. To address this, we propose an imputation method (FLk-NN) that incorporates time lagged correlations both within and across variables by combining two imputation methods, based on an extension to k-NN and the Fourier transform. This enables imputation of missing values even when all data at a time point is missing and when there are different types of missingness both within and across variables. In comparison to other approaches on three biological datasets (simulated and actual Type 1 diabetes datasets, and multi-modality neurological ICU monitoring) the proposed method has the highest imputation accuracy. This was true for up to half the data being missing and when consecutive missing values are a significant fraction of the overall time series length.
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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.