AIMC Journal:
Journal of the American Medical Informatics Association : JAMIA

Showing 241 to 250 of 493 articles

Towards gender equity in artificial intelligence and machine learning applications in dermatology.

Journal of the American Medical Informatics Association : JAMIA
There has been increased excitement around the use of machine learning (ML) and artificial intelligence (AI) in dermatology for the diagnosis of skin cancers and assessment of other dermatologic conditions. As these technologies continue to expand, i...

Natural language inference for curation of structured clinical registries from unstructured text.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Clinical registries-structured databases of demographic, diagnosis, and treatment information-play vital roles in retrospective studies, operational planning, and assessment of patient eligibility for research, including clinical trials. R...

Trust in AI: why we should be designing for APPROPRIATE reliance.

Journal of the American Medical Informatics Association : JAMIA
Use of artificial intelligence in healthcare, such as machine learning-based predictive algorithms, holds promise for advancing outcomes, but few systems are used in routine clinical practice. Trust has been cited as an important challenge to meaning...

A systematic review on natural language processing systems for eligibility prescreening in clinical research.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We conducted a systematic review to assess the effect of natural language processing (NLP) systems in improving the accuracy and efficiency of eligibility prescreening during the clinical research recruitment process.

Extracting social determinants of health from electronic health records using natural language processing: a systematic review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcom...

Transferability of neural network clinical deidentification systems.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Neural network deidentification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-world...

Use of machine learning to transform complex standardized nursing care plan data into meaningful research variables: a palliative care exemplar.

Journal of the American Medical Informatics Association : JAMIA
The aim of this article was to describe a novel methodology for transforming complex nursing care plan data into meaningful variables to assess the impact of nursing care. We extracted standardized care plan data for older adults from the electronic ...

Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem o...

GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.

A survey of extant organizational and computational setups for deploying predictive models in health systems.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now feasible for many health systems, yet little is known about effective strategies of system architecture and governance mechanisms for implementation. Our obje...