Evaluating the state of the art in missing data imputation for clinical data
Y Luo - Briefings in Bioinformatics, 2022 - academic.oup.com
Clinical data are increasingly being mined to derive new medical knowledge with a goal of
enabling greater diagnostic precision, better-personalized therapeutic regimens, improved …
enabling greater diagnostic precision, better-personalized therapeutic regimens, improved …
Missing data: An update on the state of the art.
CK Enders - Psychological Methods, 2023 - psycnet.apa.org
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
[图书][B] The effect: An introduction to research design and causality
N Huntington-Klein - 2021 - taylorfrancis.com
The Effect: An Introduction to Research Design and Causality is about research design,
specifically concerning research that uses observational data to make a causal inference. It …
specifically concerning research that uses observational data to make a causal inference. It …
Gain: Missing data imputation using generative adversarial nets
We propose a novel method for imputing missing data by adapting the well-known
Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative …
Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative …
Estimating missing data in temporal data streams using multi-directional recurrent neural networks
Missing data is a ubiquitous problem. It is especially challenging in medical settings
because many streams of measurements are collected at different-and often irregular-times …
because many streams of measurements are collected at different-and often irregular-times …
Evidential reasoning for preprocessing uncertain categorical data for trustworthy decisions: An application on healthcare and finance
The uncertainty attributed by discrepant data in AI-enabled decisions is a critical challenge
in highly regulated domains such as health care and finance. Ambiguity and incompleteness …
in highly regulated domains such as health care and finance. Ambiguity and incompleteness …
Predicting missing values in medical data via XGBoost regression
The data in a patient's laboratory test result is a notable resource to support clinical
investigation and enhance medical research. However, for a variety of reasons, this type of …
investigation and enhance medical research. However, for a variety of reasons, this type of …
[HTML][HTML] Evaluation of multiple imputation with large proportions of missing data: how much is too much?
JH Lee, JC Huber Jr - Iranian journal of public health, 2021 - ncbi.nlm.nih.gov
Background: Multiple Imputation (MI) is known as an effective method for handling missing
data in public health research. However, it is not clear that the method will be effective when …
data in public health research. However, it is not clear that the method will be effective when …
Miracle: Causally-aware imputation via learning missing data mechanisms
Missing data is an important problem in machine learning practice. Starting from the premise
that imputation methods should preserve the causal structure of the data, we develop a …
that imputation methods should preserve the causal structure of the data, we develop a …
Multiple imputation using nearest neighbor methods
S Faisal, G Tutz - Information Sciences, 2021 - Elsevier
Missing values are a major problem in medical research. As the complete case analysis
discards useful information, estimation and inference may suffer strongly. Multiple imputation …
discards useful information, estimation and inference may suffer strongly. Multiple imputation …