Systematic review on missing data imputation techniques with machine learning algorithms for healthcare
Missing data is one of the most common issues encountered in data cleaning process
especially when dealing with medical dataset. A real collected dataset is prone to be …
especially when dealing with medical dataset. A real collected dataset is prone to be …
A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory
J Wu, Z Wang - Water, 2022 - mdpi.com
Clean water is an indispensable essential resource on which humans and other living
beings depend. Therefore, the establishment of a water quality prediction model to predict …
beings depend. Therefore, the establishment of a water quality prediction model to predict …
Bias in Reinforcement Learning: A Review in Healthcare Applications
Reinforcement learning (RL) can assist in medical decision making using patient data
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …
Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites
Integrating real-world data (RWD) from several clinical sites offers great opportunities to
improve estimation with a more general population compared to analyses based on a single …
improve estimation with a more general population compared to analyses based on a single …
Evidential classification of incomplete instance based on K-nearest centroid neighbor
Z Ma, Z Liu, C Luo, L Song - Journal of Intelligent & Fuzzy …, 2021 - content.iospress.com
Classification of incomplete instance is a challenging problem due to the missing features
generally cause uncertainty in the classification result. A new evidential classification …
generally cause uncertainty in the classification result. A new evidential classification …
The impact of imputation quality on machine learning classifiers for datasets with missing values
Background Classifying samples in incomplete datasets is a common aim for machine
learning practitioners, but is non-trivial. Missing data is found in most real-world datasets …
learning practitioners, but is non-trivial. Missing data is found in most real-world datasets …
Why is the electronic health record so challenging for research and clinical care?
Background The electronic health record (EHR) has become increasingly ubiquitous. At the
same time, health professionals have been turning to this resource for access to data that is …
same time, health professionals have been turning to this resource for access to data that is …
Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents
S Kushwaha, R Srivastava, R Jain, V Sagar… - Computer Methods and …, 2022 - Elsevier
Background and objectives Pre-diabetes has been identified as an intermediate diagnosis
and a sign of a relatively high chance of developing diabetes in the future. Diabetes has …
and a sign of a relatively high chance of developing diabetes in the future. Diabetes has …
[PDF][PDF] Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation
In healthcare, machine learning (ML) shows significant potential to augment patient care,
improve population health, and streamline healthcare workflows. Realizing its full potential …
improve population health, and streamline healthcare workflows. Realizing its full potential …
[HTML][HTML] Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?
Background In electronic health records, patterns of missing laboratory test results could
capture patients' course of disease as well as reflect clinician's concerns or worries for …
capture patients' course of disease as well as reflect clinician's concerns or worries for …