[HTML][HTML] Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review
Background While artificial intelligence (AI) offers possibilities of advanced clinical
prediction and decision-making in healthcare, models trained on relatively homogeneous …
prediction and decision-making in healthcare, models trained on relatively homogeneous …
[HTML][HTML] Advancing artificial intelligence in health settings outside the hospital and clinic
The health care ecosystem is witnessing a surge of artificial intelligence (AI)-driven
technologies and products that can potentially augment care delivery outside of hospital and …
technologies and products that can potentially augment care delivery outside of hospital and …
Identifying prediction mistakes in observational data
A Rambachan - The Quarterly Journal of Economics, 2024 - academic.oup.com
Decision makers, such as doctors, judges, and managers, make consequential choices
based on predictions of unknown outcomes. Do these decision makers make systematic …
based on predictions of unknown outcomes. Do these decision makers make systematic …
E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database
N Safaei, B Safaei, S Seyedekrami, M Talafidaryani… - Plos one, 2022 - journals.plos.org
Improving the Intensive Care Unit (ICU) management network and building cost-effective
and well-managed healthcare systems are high priorities for healthcare units. Creating …
and well-managed healthcare systems are high priorities for healthcare units. Creating …
Use of steroid profiling combined with machine learning for identification and subtype classification in primary aldosteronism
Importance Most patients with primary aldosteronism, a major cause of secondary
hypertension, are not identified or appropriately treated because of difficulties in diagnosis …
hypertension, are not identified or appropriately treated because of difficulties in diagnosis …
A framework for making predictive models useful in practice
Objective To analyze the impact of factors in healthcare delivery on the net benefit of
triggering an Advanced Care Planning (ACP) workflow based on predictions of 12-month …
triggering an Advanced Care Planning (ACP) workflow based on predictions of 12-month …
Evaluation of machine learning solutions in medicine
T Antoniou, M Mamdani - Cmaj, 2021 - Can Med Assoc
• Evaluation of machine-learned systems is a multifaceted process that encompasses
internal validation, clinical validation, clinical outcomes evaluation, implementation research …
internal validation, clinical validation, clinical outcomes evaluation, implementation research …
Clairvoyance: A pipeline toolkit for medical time series
Time-series learning is the bread and butter of data-driven* clinical decision support*, and
the recent explosion in ML research has demonstrated great potential in various healthcare …
the recent explosion in ML research has demonstrated great potential in various healthcare …
Mining electronic health records using artificial intelligence: Bibliometric and content analyses for current research status and product conversion
J Liang, Y He, J Xie, X Fan, Y Liu, Q Wen… - Journal of Biomedical …, 2023 - Elsevier
Abstract Background The use of Electronic Health Records is the most important milestone
in the digitization and intelligence of the entire medical industry. AI can effectively mine the …
in the digitization and intelligence of the entire medical industry. AI can effectively mine the …
[HTML][HTML] Retinal scans and data sharing: the privacy and scientific development equilibrium
LF Nakayama, JCRG de Matos, IU Stewart… - Mayo Clinic …, 2023 - Elsevier
In ophthalmology, extensive use of ancillary imaging has enabled the development of
artificial intelligence models, for which data are crucial. A data-sharing environment …
artificial intelligence models, for which data are crucial. A data-sharing environment …