A Semantic Extraction and Sentimental Assessment of Risk Factors (SESARF): An NLP Approach for Precision Medicine: A Medical Decision Support Tool for Early Diagnosis from Clinical Notes

Sabra, Susan; Mahmood, Khalid; Alobaidi, Mazen

doi:10.1109/compsac.2017.34

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…The data collection has been proposes with a framework semantic extractor to analyse the hidden risk factors in the clinical decision with feature vector using machine learning and risk weight enrichment in the analysis model. The model analysis has a collection mechanism that follows a health status prediction n the patient analysis [16].…”

Section: Glove Word Vectormentioning

confidence: 99%

Retracted: Software based sentiment analysis of clinical data for healthcare sector

Vimal¹,

Albuquerque

Barbosa

et al. 2023

IET Software

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Aiming at the simple neural network's lack of the ability to capture the contextual semantics of text and extract meaningful information in the text, a sentiment analysis model FAGB iAGRU is a fusion of attention mechanism and gated recurrent unit (GRU), is designed. First, the text is preprocessed, and passed GloVe carries out word vectorisation to reduce the dimension of the vector space; then, the attention mechanism is merged with the update gate of the gating unit to form a hybrid model so that it can extract meaningful information in the text features; finally, by forcing forward attention The mechanism further removes text features, and then classifies them by the softmax classifier. Experiments on public data sets show that the algorithm can effectively improve the performance of sentiment analysis. K E Y W O R D Sbelief networks, client-server systems, contracts, data models, data privacy, data protectionThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Glove Word Vectormentioning

confidence: 99%

Retracted: Software based sentiment analysis of clinical data for healthcare sector

Vimal¹,

Albuquerque

Barbosa

et al. 2023

IET Software

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show abstract

“…Sabra, Mahmood, and Alobaidi () proposed a clinical decision support tool entitled Semantic Extraction and Sentimental Assessment of Risk Factors (SESARF). SESARF provides an early diagnosis by analysing the clinical notes of electronic health records (EHR) in order to detect hidden risk factors.…”

Section: Key Phrs Applications and Case Studies In Literaturementioning

confidence: 99%

A survey on semanticized and personalized health recommender systems

Ertuğrul

Elçi

2019

Expert Systems

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Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre‐based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi‐dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web‐enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems.

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Sentiment analysis of clinical narratives: A scoping review

Denecke

Reichenpfader

2023

Journal of Biomedical Informatics

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A Semantic Extraction and Sentimental Assessment of Risk Factors (SESARF): An NLP Approach for Precision Medicine: A Medical Decision Support Tool for Early Diagnosis from Clinical Notes

Cited by 8 publications

References 9 publications

Retracted: Software based sentiment analysis of clinical data for healthcare sector

Retracted: Software based sentiment analysis of clinical data for healthcare sector

A survey on semanticized and personalized health recommender systems

Sentiment analysis of clinical narratives: A scoping review

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