Natural Language Processing for Smart Healthcare

Zhou, Binggui; Yang, Guanghua; Shi, Zheng; Ma, Shaodan

doi:10.48550/arxiv.2110.15803

Cited by 2 publications

(2 citation statements)

References 206 publications

(244 reference statements)

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“…Most of these approaches are very domain and language specific and thus not very useful for the healthcare domain in Indian languages. B) Entity and Intents in Health Care: Zhou et al (2021) solve different tasks in smart healthcare. Bao et al (2020) build a chat-bot framework using user intents.…”

Section: Related Workmentioning

confidence: 99%

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Mullick¹,

Mondal²,

Ray³

et al. 2023

Preprint

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Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current status of various state-of-the-art language models in healthcare, this paper studies the problem by initially proposing two different Healthcare datasets, Indian Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real world Indian hospital query data in English and multiple Indic languages (Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with the query intents as well as entities. Our aim is to detect query intents and extract corresponding entities. We perform extensive experiments on a set of models in various realistic settings and explore two scenarios based on the access to English data only (less costly) and access to target language data (more expensive). We analyze context specific practical relevancy through empirical analysis. The results, expressed in terms of overall F1 score show that our approach is practically useful to identify intents and entities.

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Section: Related Workmentioning

confidence: 99%

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Mullick¹,

Mondal²,

Ray³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Predictive analytics improves healthcare by analyzing past data to predict illness progression and patient reactions to therapies, allowing for the development of individualized treatment plans that improve healthcare outcomes and reduce costs [9]. NLP plays a crucial role in elevating patient care by meticulously examining extensive medical data, extracting valuable insights, and enhancing clinical decision-making processes [10]. Computer vision transforms medical imaging by automating the interpretation of X-rays, MRIs, and CT images, considerably enhancing abnormality identification and speeding up the diagnostic procedure [11].…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Healthcare in France

2024

IJFMR

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This integrative literature review (ILR) looks into the use of artificial intelligence (AI) technology in the French healthcare system, emphasizing personalized medicine and predictive health analytics. The study subject is the problematic integration of AI technologies, which is hampered by significant challenges, including data privacy concerns, system interoperability, ethical and legal issues, resistance to technological change, and the need for extensive training of healthcare professionals. These issues affect French healthcare professionals and politicians, who must overcome these challenges to utilize AI's potential fully. This research aims to investigate and assess the integration of AI into the French healthcare system, particularly in personalized medicine and predictive health analytics, to identify and address the obstacles and leverage the opportunities that improve patient care and operational efficiency. The ILR's guiding conceptual framework is based on three essential concepts: artificial intelligence, personalized medicine, and predictive healthcare analytics. The research methodology consists in thoroughly examining current literature, qualitative study of case studies, and interviews with industry professionals. The study's findings show that AI has tremendous potential to increase diagnostic precision and treatment accuracy in the French healthcare system. They highlight initiatives such as establishing specialized departments like "Intelligent Healthcare Services" and creating positions like "Intelligent Doctor". The conclusions address the potential implications of the findings for enhancing patient care and operational efficiency, as well as recommendations for further study and practice. These include performing longitudinal studies, looking into emerging data integrity solutions such as blockchain, and improving healthcare professional training programs. Ultimately, breaking down current obstacles and ensuring that AI technologies improve healthcare delivery and patient outcomes would make France a leader in AI-driven medical innovation.

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Natural Language Processing for Smart Healthcare

Cited by 2 publications

References 206 publications

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

AI-Driven Healthcare in France

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