[HTML][HTML] The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and …
Artificial intelligence (AI) has huge potential to improve the health and well-being of people,
but adoption in clinical practice is still limited. Lack of transparency is identified as one of the …
but adoption in clinical practice is still limited. Lack of transparency is identified as one of the …
[HTML][HTML] Guidelines for artificial intelligence in medicine: literature review and content analysis of frameworks
Background: Artificial intelligence (AI) is rapidly expanding in medicine despite a lack of
consensus on its application and evaluation. Objective: We sought to identify current …
consensus on its application and evaluation. Objective: We sought to identify current …
The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment
Abstract Objective Coronavirus disease 2019 (COVID-19) poses societal challenges that
require expeditious data and knowledge sharing. Though organizational clinical data are …
require expeditious data and knowledge sharing. Though organizational clinical data are …
Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic …
Background Many mortality prediction models have been developed for patients in intensive
care units (ICUs); most are based on data available at ICU admission. We investigated …
care units (ICUs); most are based on data available at ICU admission. We investigated …
[HTML][HTML] Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury
Objective We aimed to explore the added value of common machine learning (ML)
algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study …
algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study …
Predictive analytics in health care: how can we know it works?
There is increasing awareness that the methodology and findings of research should be
transparent. This includes studies using artificial intelligence to develop predictive …
transparent. This includes studies using artificial intelligence to develop predictive …
Big data hurdles in precision medicine and precision public health
Background Nowadays, trendy research in biomedical sciences juxtaposes the term
'precision'to medicine and public health with companion words like big data, data science …
'precision'to medicine and public health with companion words like big data, data science …
An empirical characterization of fair machine learning for clinical risk prediction
The use of machine learning to guide clinical decision making has the potential to worsen
existing health disparities. Several recent works frame the problem as that of algorithmic …
existing health disparities. Several recent works frame the problem as that of algorithmic …
Use of unstructured text in prognostic clinical prediction models: a systematic review
Objective This systematic review aims to assess how information from unstructured text is
used to develop and validate clinical prognostic prediction models. We summarize the …
used to develop and validate clinical prognostic prediction models. We summarize the …
Increasing trust in real-world evidence through evaluation of observational data quality
Objective Advances in standardization of observational healthcare data have enabled
methodological breakthroughs, rapid global collaboration, and generation of real-world …
methodological breakthroughs, rapid global collaboration, and generation of real-world …