Generating Accurate Electronic Health Assessment from Medical Graph. Yang, Z. & Yu, H. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3764–3773, Online, November, 2020. Association for Computational Linguistics. NIHMSID: NIHMS1658452![Generating Accurate Electronic Health Assessment from Medical Graph [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex One of the fundamental goals of artificial intelligence is to build computer-based expert systems. Inferring clinical diagnoses to generate a clinical assessment during a patient encounter is a crucial step towards building a medical diagnostic system. Previous works were mainly based on either medical domain-specific knowledge, or patients' prior diagnoses and clinical encounters. In this paper, we propose a novel model for automated clinical assessment generation (MCAG). MCAG is built on an innovative graph neural network, where rich clinical knowledge is incorporated into an end-to-end corpus-learning system. Our evaluation results against physician generated gold standard show that MCAG significantly improves the BLEU and rouge score compared with competitive baseline models. Further, physicians' evaluation showed that MCAG could generate high-quality assessments.
@inproceedings{yang_generating_2020,
address = {Online},
title = {Generating {Accurate} {Electronic} {Health} {Assessment} from {Medical} {Graph}},
url = {https://www.aclweb.org/anthology/2020.findings-emnlp.336},
abstract = {One of the fundamental goals of artificial intelligence is to build computer-based expert systems. Inferring clinical diagnoses to generate a clinical assessment during a patient encounter is a crucial step towards building a medical diagnostic system. Previous works were mainly based on either medical domain-specific knowledge, or patients' prior diagnoses and clinical encounters. In this paper, we propose a novel model for automated clinical assessment generation (MCAG). MCAG is built on an innovative graph neural network, where rich clinical knowledge is incorporated into an end-to-end corpus-learning system. Our evaluation results against physician generated gold standard show that MCAG significantly improves the BLEU and rouge score compared with competitive baseline models. Further, physicians' evaluation showed that MCAG could generate high-quality assessments.},
urldate = {2020-11-29},
booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {EMNLP} 2020},
publisher = {Association for Computational Linguistics},
author = {Yang, Zhichao and Yu, Hong},
month = nov,
year = {2020},
pmcid = {PMC7821471},
pmid = {33491009},
note = {NIHMSID: NIHMS1658452},
pages = {3764--3773},
}
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