[HTML][HTML] Bias in artificial intelligence algorithms and recommendations for mitigation
LH Nazer, R Zatarah, S Waldrip, JXC Ke… - PLOS Digital …, 2023 - journals.plos.org
The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such
algorithms may be shaped by various factors such as social determinants of health that can …
algorithms may be shaped by various factors such as social determinants of health that can …
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 …
Time-aware missing healthcare data prediction based on ARIMA model
L Kong, G Li, W Rafique, S Shen, Q He… - … ACM transactions on …, 2022 - ieeexplore.ieee.org
Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose
meters, electrocardiographs), which results in the generation of large amounts of data …
meters, electrocardiographs), which results in the generation of large amounts of data …
[HTML][HTML] Machine learning in nutrition research
Data currently generated in the field of nutrition are becoming increasingly complex and
high-dimensional, bringing with them new methods of data analysis. The characteristics of …
high-dimensional, bringing with them new methods of data analysis. The characteristics of …
[HTML][HTML] Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models
Abstract Background and Objectives We sought to summarize the study design, modelling
strategies, and performance measures reported in studies on clinical prediction models …
strategies, and performance measures reported in studies on clinical prediction models …
[HTML][HTML] Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning tasks. When data
are 'missing at random'there exist a range of tools and techniques to deal with the issue …
are 'missing at random'there exist a range of tools and techniques to deal with the issue …
Missing value imputation methods for electronic health records
Electronic health records (EHR) are patient-level information, eg, laboratory tests and
questionnaires, stored in electronic format. Compared to physical records, the EHR …
questionnaires, stored in electronic format. Compared to physical records, the EHR …
[HTML][HTML] Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare
G Kleinberg, MJ Diaz, S Batchu… - Journal of biomed …, 2022 - ncbi.nlm.nih.gov
Objective: Clinical applications of machine learning are promising as a tool to improve
patient outcomes through assisting diagnoses, treatment, and analyzing risk factors for …
patient outcomes through assisting diagnoses, treatment, and analyzing risk factors for …
REFORMS: Consensus-based Recommendations for Machine-learning-based Science
Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …
adoption of these methods has been accompanied by failures of validity, reproducibility, and …
Development of a bedside tool to predict the diagnosis of cerebral palsy in term-born neonates
A Rouabhi, N Husein, D Dewey, N Letourneau… - JAMA …, 2023 - jamanetwork.com
Importance Cerebral palsy (CP) is the most common abnormality of motor development and
causes lifelong impairment. Early diagnosis and therapy can improve outcomes, but early …
causes lifelong impairment. Early diagnosis and therapy can improve outcomes, but early …