Results 201 to 210 of about 216,838 (308)

Missing Data Gap Imputation Methods in Electroencephalogram (EEG) Signals: A Systematic Scoping Review. [PDF]

Sensors (Basel)
Bergmann T, Movshovich M, Shao Y, Ryznar J, Nemoga-Stout X, Marquez I, Herath I, Sainbhi AS, Vakitbilir N, Silvaggio N, Hasan R, Stein KY, Shaheen H, Moon J, Zeiler FA.   +14 more
europepmc   +1 more source

Compensating for Missing Data from Longitudinal Studies Using WinBUGS

Missing data is a common problem in survey based research. There are many packages that compensate for missing data but few can easily compensate for missing longitudinal data.
Gita Mishra, Gretchen Carrigan, Adrian G. Barnett, Annette J. Dobson   +3 more
core  

Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

Annals of Gastroenterological Surgery, EarlyView.
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males, Marko Kumric, Zvonimir Boban, Josip Vrdoljak, Dario Pecenkovic, Matej Ivanda, Marko Grahovac, Zenon Pogorelic, Josko Bozic   +8 more
wiley   +1 more source

When patients' voices aren't heard: estimands and statistical methods for handling missing patient-reported outcomes in oncology studies. [PDF]

BMC Med Res Methodol
Martin EC, Lawrance R, Hind A, Cro S.
europepmc   +1 more source

The Challenge of Handling Structured Missingness in Integrated Data Sources

Advanced Intelligent Discovery, EarlyView.
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson, Marius Hofert, Eleni‐Rosalina Andrinopoulou, Niels Hagenbuch, Sarah McGough, Chris Harbron, Robin Mitra   +6 more
wiley   +1 more source

Benchmarking imputation accuracy in the presence or absence of a reference panel. [PDF]

Mol Biol Evol
Topaloudis A, Cumer T, Lavanchy E, Hewett A, Bachmann Salvy M, Ducrest AL, Simon C, Almasi B, Roulin A, Delaneau O, Goudet J.   +10 more
europepmc   +1 more source

Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

Advanced Intelligent Discovery, EarlyView.
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah, Muhammad Zulqarnain Zeb, Muhammad Husnain Zeb, Babar Shabbir   +3 more
wiley   +1 more source

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

Advanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto, Gabriel Villarrubia Gonzalez, Ademir Nied, Laio Oriel Seman, Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon   +5 more
wiley   +1 more source

scTACL: a multitask topology-aware contrastive learning approach for single-cell transcriptomics analysis. [PDF]

Bioinformatics
Zhou M, Lu X, Liang Y, Kow AWC, Wang G, Liu Q, Zhao Y.   +6 more
europepmc   +1 more source

EVALUATING ALTERNATIVE METHODS OF DEALING WITH MISSING OBSERVATIONS - AN ECONOMIC APPLICATION

This paper compares methods to remedy missing value problems in survey data. The commonly used methods to deal with this issue are to delete observations that have missing values (case-deletion), replace missing values with sample mean (mean imputation),
Onozaka, Yuko
core