2021
DOI: 10.1109/jbhi.2021.3099755
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An Accurate Deep Learning Model for Clinical Entity Recognition From Clinical Notes

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Cited by 23 publications
(11 citation statements)
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References 26 publications
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“…designed privacy-preserving model using CNN A 2020 Lopez, et al [6] designed pattern matching, dictionaries, and ML-powered web tool for auto-detection of PHI A 2020 Iwendi, et al [12] proposed semantic privacy framework to effectively sanitize sensitive terms in healthcare documents D 2021 Yue, et al [73] proposed two token-wise sanitization methods for text sanitization B 2021 Fattahi, et al [46] proposed a tool for spam detection B 2021 Igamberdiev, et al [47] applied differentially private stochastic gradient descent to GCNs to maintain strict privacy guarantees C 2021 Amaral, el al. [64] proposed an AI-based automation system for the completeness checking of privacy policies A 2021 Catelli, et al [22] combined contextualized word representation and sub-document level analysis for clinical de-identification A 2021 Catelli, et al [25] cross-lingual transfer learning to de-identify medical records A 2021 Moqurrab, et al [28] proposed model uses local and global context to extract clinical entities…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…designed privacy-preserving model using CNN A 2020 Lopez, et al [6] designed pattern matching, dictionaries, and ML-powered web tool for auto-detection of PHI A 2020 Iwendi, et al [12] proposed semantic privacy framework to effectively sanitize sensitive terms in healthcare documents D 2021 Yue, et al [73] proposed two token-wise sanitization methods for text sanitization B 2021 Fattahi, et al [46] proposed a tool for spam detection B 2021 Igamberdiev, et al [47] applied differentially private stochastic gradient descent to GCNs to maintain strict privacy guarantees C 2021 Amaral, el al. [64] proposed an AI-based automation system for the completeness checking of privacy policies A 2021 Catelli, et al [22] combined contextualized word representation and sub-document level analysis for clinical de-identification A 2021 Catelli, et al [25] cross-lingual transfer learning to de-identify medical records A 2021 Moqurrab, et al [28] proposed model uses local and global context to extract clinical entities…”
Section: Discussionmentioning
confidence: 99%
“…Most of the proposed Bi-LSTM based models utilized only the global context to detect clinical entities and PHIs, not the local context. Therefore, Moqurrab, et al [28] proposed a combination of CNN, Bi-LSTM, and CRF with non-complex embeddings to utilize both local and global context. Here, CNN was used to capture local context, while Bi-LSTM was used to capture global context.…”
Section: Deep Learning-based Systemsmentioning
confidence: 99%
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“…The hybrid model is presented based on hybrid Long Short-Term Memory (LSTM) A bidirectional gated recurrent unit is implemented to reduce the human effort in www.ijacsa.thesai.org modelling data and feature selection. Moqurrab et al [26] combined CNN, BI-LSTM and discriminant model for the extraction of the clinical entities from the medical notes. Detecting clinical entities accurately can be helpful in maintaining the confidentiality of medical data, which increases trust between users and medical organizations.…”
Section: Related Workmentioning
confidence: 99%
“…For example, it is used in hospitals to process health care claims; to group medical records by patients’ symptoms [ 16 , 17 ] or to predict the number of hospital admissions at an emergency department to avoid overcrowding [ 18 ]. A more recent study showed an automated method for extracting clinical entities, such as treatment, tests, drugs and genes, from clinical notes [ 19 ]. Another study discussed how text mining can be used to assess the sentiment in tweets towards the COVID 19 pandemic [ 20 ].…”
Section: Introductionmentioning
confidence: 99%