Search Articles

View query in Help articles search
e.g. mhealth

Search Results (1 to 3 of 3 Results)

Download search results: CSV END BibTex RIS

A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study

A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study

As the contextual text representation is generated from transformer-based neural network, which has a black box nature, we used the local interpretable model-agnostic explanations (LIME) technique to analyze the top-performing ML classifier trained with the contextual text representation. LIME is a post hoc, local perturbation technique that provides the explanation for a single prediction.

Hongbo Chen, Eldan Cohen, Dulaney Wilson, Myrtede Alfred

JMIR Hum Factors 2024;11:e53378

Download Citation: END BibTex RIS

Download PDF Download XML
Article has an altmetric score of 2

Visualizing the Interpretation of a Criteria-Driven System That Automatically Evaluates the Quality of Health News: Exploratory Study of 2 Approaches

Visualizing the Interpretation of a Criteria-Driven System That Automatically Evaluates the Quality of Health News: Exploratory Study of 2 Approaches

The hybrid approach combined the interpretable AI technique, LIME, rule-based systems, and supervised document classification. LIME, proposed in 2016 by Ribeiro et al [74], belonged to a family of local model-agnostic methods, a type of interpretable AI method. It is used to explain the individual predictions of black-box ML based on a surrogate model, which is trained to approximate the predictions of the underlying black-box model [74,75].

Xiaoyu Liu, Hiba Alsghaier, Ling Tong, Amna Ataullah, Susan McRoy

JMIR AI 2022;1(1):e37751

Download Citation: END BibTex RIS

Download PDF Download XML
Article has an altmetric score of 3

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

Moreover, to improve the interpretability of the black box model, we also used SHapley Additive ex Planations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to explain the prediction model; therefore, the prediction model not only predicts prognostic outcomes but also gives a reasonable explanation for the prediction, which can greatly enhance users’ trust of the model.

Pan Pan, Yichao Li, Yongjiu Xiao, Bingchao Han, Longxiang Su, Mingliang Su, Yansheng Li, Siqi Zhang, Dapeng Jiang, Xia Chen, Fuquan Zhou, Ling Ma, Pengtao Bao, Lixin Xie

J Med Internet Res 2020;22(11):e23128

Download Citation: END BibTex RIS

Download PDF Download XML
Article has an altmetric score of 22