ArticleJournal of healthcare informatics research2020
A Combined Interpolation and Weighted
Article in Journal of healthcare informatics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Machine learning and Mendelian randomization identify key lifestyle factors in coronary heart disease: A NHANES-Based study.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- SysML: adaptive recommendation system for heterogeneous biomedical data preprocessing and modeling workflows.Briefings in bioinformatics · 2025Article
- Historical dataset of reservoir construction in arid regions.Scientific data · 2025Article
- Predicting 28-Day Mortality in Critically Ill Patients Receiving Continuous Renal Replacement Therapy: A Novel Interpretable Machine Learning Approach.Journal of multidisciplinary healthcare · 2025Article
- A dynamic probabilistic model of the onset and interaction of cardio-metabolic comorbidities on an ageing adult population.Scientific reports · 2024Article
- Deep imputation of missing values in time series health data: A review with benchmarking.Journal of biomedical informatics · 2023Review
- Varieties of corona news: a cross-national study on the foundations of online misinformation production during the COVID-19 pandemic.Journal of computational social science · 2023Article
- Eleven quick tips for data cleaning and feature engineering.PLoS computational biology · 2022Article
- Evaluating the state of the art in missing data imputation for clinical data.Briefings in bioinformatics · 2022Article
- Predicting Mortality of COVID-19 Patients based on Data Mining Techniques.Journal of biomedical physics & engineering · 2021Article
- Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presence of missing data is a common problem that affects almost all clinical datasets. Since most available data mining and machine learning algorithms require complete datasets, accurately imputing (i.e. "filling in") the missing data is an essential step. This paper presents a methodology for the missing data imputation of longitudinal clinical data based on the integration of linear interpolation and a weighted
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Identifiers
What OpenQuestion holds
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.