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Development and validation of interpretable machine learning models for inpatient fall events and electronic medical record integration
Soyun Shim, Jae Yong Yu, Seyong Jekal, Yee Jun Song, Ki Tae Moon, Ju Hee Lee, Kyung Mi Yeom, Sook Hyun Park, In Sook Cho, Mi Ra Song, Sejin Heo, Jeong Hee Hong
Clin Exp Emerg Med. 2022;9(4):345-353.   Published online 2022 September 21    DOI: https://doi.org/10.15441/ceem.22.354

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Development and validation of interpretable machine learning models for inpatient fall events and electronic medical record integration
Clinical and Experimental Emergency Medicine. 2022;9(4):345-353   Crossref logo
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Development and Validation of Interpretable Machine Learning for Stroke Occurrence in Older, Community Chinese Dwellers
. 2021;   Crossref logo
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Considerations for Evaluation and Generalization in Interpretable Machine Learning
Explainable and Interpretable Models in Computer Vision and Machine Learning. 2018;3-17   Crossref logo
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Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study
Journal of General Internal Medicine. 2018;33(6):921-928   Crossref logo
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Learning Interpretable Rules for Multi-Label Classification
Explainable and Interpretable Models in Computer Vision and Machine Learning. 2018;81-113   Crossref logo
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Interpretable deep-learning models to help achieve the Sustainable Development Goals
Nature Machine Intelligence. 2021;3(11):926-926   Crossref logo
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Discovering Interpretable Machine Learning Models in Parallel Coordinates
2021 25th International Conference Information Visualisation (IV). 2021;   Crossref logo
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Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay
Sensors. 2022;22(22):8757   Crossref logo
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Nature Machine Intelligence. 2019;1(5):206-215   Crossref logo
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Active Sampling for Learning Interpretable Surrogate Machine Learning Models
2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). 2020;   Crossref logo
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This metadata service is kindly provided by CrossRef from May 29, 2014. Clin Exp Emerg Med has participated in CrossRef Text and Data Mining service since October 29, 2014.