Human-Computer Interaction, Usability, and Workflow

Patel, Vimla L.; Kaufman, David R.; Kannampallil, Thomas

doi:10.1007/978-3-030-58721-5_5

Cited by 4 publications

(4 citation statements)

References 89 publications

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“…For now this translation problem is very complex but might be treatable through the framework of human-computer-interaction (HCI) for future clinical decision support. Under the HCI framework, related work on this translation problem can motivate research in cognitive science, behavioral science in biomedical informatics, etc., that mainly hinge on users’ intention of usage [53].…”

Section: Discussionmentioning

confidence: 99%

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation

Wang

Stroh

Hripcsak

et al. 2023

Preprint

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Phenotyping methods aiming to produce high-fidelity time-dependent phenotypes in a specific context with personalized interpretation are challenging, especially given the complexity of physiological systems and data quality. We present a three-stage methodological phenotyping pipeline based on a mechanistic physiological model framework applied to the glucose-insulin system of ICU patients from electronic health record data. Balancing flexibility and biological fidelity, the pipeline within the data assimilation (DA) framework is used to compute physiologically-anchored phenotypes that are high-fidelity, personalized, time-sensitive, and interpretable using data present in real-time ICU settings. We construct the computational phenotyping pipeline such that it is generalizable and we demonstrate the pipeline's accuracy and reliability through external data evaluation using electronic health record data and clinical face validation.

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Section: Discussionmentioning

confidence: 99%

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation

Wang

Stroh

Hripcsak

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The disconnect between the language of physiology and language of clinical diagnosis, e.g., the mapping between physiology and SNOMED CT can lead to error and bias. This complex translation problem might be treatable through the framework of human–computer-interaction (HCI) for future clinical decision support, where related work can motivate research in cognitive science, behavioral science in biomedical informatics, etc., that mainly hinge on users’ intention of usage [ 66 ].…”

Section: Discussionmentioning

confidence: 99%

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation

Wang,

Stroh,

Hripcsak

et al. 2023

Journal of Biomedical Informatics

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“…The field of human-computer interaction intersects cognitive-behavioural, computer, and information sciences. As healthcare systems become more sophisticated and intelligent, careful evaluation of these tools, as they are actually leveraged by intended users, becomes necessary 4. Human-machine dyads too often end up on the technology-led rather than the human-led side 5.…”

mentioning

confidence: 99%

“…As healthcare systems become more sophisticated and intelligent, careful evaluation of these tools, as they are actually leveraged by intended users, becomes necessary. 4 Human-machine dyads too often end up on the technology-led rather than the human-led side. 5 The implementations often fail to support physicians in their tasks, highlighting system inadequacies and demonstrating why human-centred approaches to designing and evaluating AI tools are even more critical.…”

mentioning

confidence: 99%

Cognitive science in the evaluation of medical AI systems

Patel

2023

BMJ Health Care Inform

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Human-Computer Interaction, Usability, and Workflow

Cited by 4 publications

References 89 publications

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation

Cognitive science in the evaluation of medical AI systems

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