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10.1093/jamia/ocab077 http://scihub22266oqcxt.onion/10.1093/jamia/ocab077 33956981!8135980!33956981     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2021 ; 28 (8): 1703-1711 Nephropedia Template TP {{tp|p=33956981|t=2021. A neuro-symbolic method for understanding free-text medical evidence |pdf=|usr=}}{{33956981}} gab.com Text A neuro-symbolic method for understanding free-text medical evidence JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33956981 #FOAvip OBJECTIVE: We introduce Medical evidence Dependency (MD)-informed attention, a novel neuro-symbolic model for understanding free-text clinical trial publications with generalizability and interpretability. MATERIALS AND METHODS: We trained one head in the multi-head self-attention model to attend to the Medical evidence Ddependency (MD) and to pass linguistic and domain knowledge on to later layers (MD informed). This MD-informed attention model was integrated into BioBERT and tested on 2 public machine reading comprehension benchmarks for clinical trial publications: Evidence Inference 2.0 and PubMedQA. We also curated a small set of recently published articles reporting randomized controlled trials on COVID-19 (coronavirus disease 2019) following the Evidence Inference 2.0 guidelines to evaluate the model's robustness to unseen data. RESULTS: The integration of MD-informed attention head improves BioBERT substantially in both benchmark tasks-as large as an increase of +30% in the F1 score-and achieves the new state-of-the-art performance on the Evidence Inference 2.0. It achieves 84% and 82% in overall accuracy and F1 score, respectively, on the unseen COVID-19 data. CONCLUSIONS: MD-informed attention empowers neural reading comprehension models with interpretability and generalizability via reusable domain knowledge. Its compositionality can benefit any transformer-based architecture for machine reading comprehension of free-text medical evidence. Twit Text FOAVip A neuro-symbolic method for understanding free-text medical evidence ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33956981 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33956981 #FOAvip English Wikipedia <ref>{{Cite pmid|33956981}}</ref> A neuro-symbolic method for understanding free-text medical evidence #MMPMID33956981 Kang T; Turfah A; Kim J; Perotte A; Weng C J Am Med Inform Assoc 2021[Jul]; 28 (8): 1703-1711 PMID33956981show ga OBJECTIVE: We introduce Medical evidence Dependency (MD)-informed attention, a novel neuro-symbolic model for understanding free-text clinical trial publications with generalizability and interpretability. MATERIALS AND METHODS: We trained one head in the multi-head self-attention model to attend to the Medical evidence Ddependency (MD) and to pass linguistic and domain knowledge on to later layers (MD informed). This MD-informed attention model was integrated into BioBERT and tested on 2 public machine reading comprehension benchmarks for clinical trial publications: Evidence Inference 2.0 and PubMedQA. We also curated a small set of recently published articles reporting randomized controlled trials on COVID-19 (coronavirus disease 2019) following the Evidence Inference 2.0 guidelines to evaluate the model's robustness to unseen data. RESULTS: The integration of MD-informed attention head improves BioBERT substantially in both benchmark tasks-as large as an increase of +30% in the F1 score-and achieves the new state-of-the-art performance on the Evidence Inference 2.0. It achieves 84% and 82% in overall accuracy and F1 score, respectively, on the unseen COVID-19 data. CONCLUSIONS: MD-informed attention empowers neural reading comprehension models with interpretability and generalizability via reusable domain knowledge. Its compositionality can benefit any transformer-based architecture for machine reading comprehension of free-text medical evidence. |*Artificial Intelligence[MESH] |*Clinical Trials as Topic[MESH] |*Models, Neurological[MESH] |*Natural Language Processing[MESH] |COVID-19[MESH] |Computer Simulation[MESH] |Data Mining[MESH] |Humans[MESH] |Information Storage and Retrieval/*methods[MESH]A neuro-symbolic method for understanding free-text medical evidence (epmc).pdf DeepDyve Pubget Overpricing