Natural language processing: an introduction

By Prakash M. Nadkarni, Lucila Ohno-Machado, Wendy W. Chapman. 2011

1,183 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Weinan Zhang
2. Yaodong Yang
3. Ying Wen
4. Ozanan R. Meireles
5. Daniela Rus
6. William R. Hersh
7. Sophia Ananiadou
8. Harold P. Lehmann

In Molecular Biology

1. Daniela Rus
2. William R. Hersh
3. Sophia Ananiadou
4. Harold P. Lehmann
5. Weinan Zhang
6. J. Wang

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, neutral): “tives are often ungrammatical, incomplete with limited context, and contain a large number of abbreviations and acronyms [189], all of which make computational phenotyp-”
• Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, neutral): “This paper assumes that the reader has a passing knowledge of biomedical NLP; we refer the reader to introductions and surveys on the latest applications [15, 16, 17] for more information.”
• Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, neutral): “However, subtle relationships hidden in notes remain difficult to extract due to the complexity of the language used and the lack of explicit semantic resources describing the relationships between clinical concepts [119,120].”
• Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research (2013, positive): “One promising approach for recovering these data for research is natural language processing (NLP).(67) This approach has been most successful when applied to the determination of specific data elements, such as the presence of a diagnosis or treatment.”
• Supporting Information Retrieval from Electronic Health Records: A Report of University of Michigan's Nine-Year Experience in Developing and Using the Electronic Medical Record Search Engine (EMERSE) (2015, neutral): “Further, despite significant research advancements, the precision and recall of such tools are not yet up to par for meeting the requirements of many sophisticated chart abstraction tasks, and existing tools’ lack of generalizability often necessitates customized solutions be built to the specific n”
• A large language model for electronic health records (2022, neutral): “(7) UF Health IDR clinical notes are not open to the”
• Comparing Rule-Based and Deep Learning Models for Patient Phenotyping (2017, neutral): “However, unstructured data in EHRs, such as progress notes or discharge summaries, is typically not amenable to simple text searches because of spelling mistakes, and the use of ambiguous terms.(40) To help address these issues, researchers utilize dictionaries and ontologies for medical terminologi”
• Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, neutral): “However, unstructured data in EHRs, for example progress notes or discharge summaries, are not typically amenable to simple text searches because of spelling mistakes, synonyms, and ambiguous terms [41].”
• From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing (2023, neutral): “were trained on vast text datasets, enabling them to produce highly realistic human-like responses [6].”