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	531	Impact of EU non-financial reporting regulation on Spanish companies’ environmental disclosure: a cutting-edge natural language processing approach Enthalten in Environmental sciences Europe Bd. 37, 21.2.2025, Nr. 1, date:12.2025: 1-33
	532	Improving ascertainment of suicidal ideation and suicide attempt with natural language processing Enthalten in Scientific reports Bd. 12, 7.9.2022, Nr. 1, date:12.2022: 1-11
	533	Improving early diagnosis of rare diseases using Natural Language Processing in unstructured medical records: an illustration from Dravet syndrome Enthalten in Orphanet journal of rare diseases Bd. 16, 13.7.2021, Nr. 1, date:12.2021: 1-12
	534	Improving Neural Models for Natural Language Processing in Russian with Synonyms Enthalten in Journal of mathematical sciences Bd. 273, 24.6.2023, Nr. 4, date:7.2023: 583-594
	535	Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review Enthalten in BMC emergency medicine Bd. 24, 18.11.2024, Nr. 1, date:12.2024: 1-29
	536	In patients’ words: natural language processing of reports from patients experiencing orofacial pain and dysfunction Enthalten in The journal of headache and pain Bd. 26, 30.7.2025, Nr. 1, date:12.2025: 1-13
	537	Informing the development of an outcome set and banks of items to measure mobility among individuals with acquired brain injury using natural language processing Enthalten in BMC neurology Bd. 22, 9.12.2022, Nr. 1, date:12.2022: 1-15
	538	Integrating natural language processing with image document analysis: what we learned from two real-world applications Enthalten in International journal on document analysis and recognition Bd. 18, 28.5.2015, Nr. 3, date:9.2015: 235-247
	539	Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models Enthalten in BMC medical informatics and decision making Bd. 24, 5.12.2024, Nr. 1, date:12.2024: 1-13
	540	Integration of large-scale community-developed causal loop diagrams: a Natural Language Processing approach to merging factors based on semantic similarity Enthalten in BMC public health Bd. 25, 8.3.2025, Nr. 1, date:12.2025: 1-9

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