An Initial Investigation of the Charlson Comorbidity Index Regression Based on Clinical Notes

Santos, Henrique D. P. dos; Ulbrich, Ana Helena D. P. S.; Woloszyn, Vinicius; Vieira, Renata

doi:10.1109/cbms.2018.00009

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…In this research, Santos et al [30] proposed using densitydistance-centrality (DDC) to identify potential outlier prescriptions. Using data on 563,000 medications, they compared the suggested solution to other cutting-edge ways to find outliers.…”

Section: Literature Reviewsmentioning

confidence: 99%

An Integrated Scalable Framework for Cloud and IoT Based Green Healthcare System

Islam

Bhuiyan

2023

IEEE Access

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Recent developments in IoT-enabled cloud computing and interactive applications have made researchers rethink how healthcare services are currently provided. The IoT-cloud-based systems facilitate remote monitoring and support for patients. However, in the existing area, much emphasis has not been given to making the healthcare systems green. So, in this paper, we present an integrated framework for green healthcare and use cutting-edge technology to make an interactive user interface. We have also ensured the system's scalability and performance ratio. This system interface has been designed and developed for patients and doctors, where patients can send their healthcare data using wearable sensors, and doctors can receive those data in real-time. For data identification and analysis, we have adopted Hierarchical Clustering Algorithms. Finally, we have come up with a solution for how to make the interactive healthcare experience better for everyone.

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Section: Literature Reviewsmentioning

confidence: 99%

An Integrated Scalable Framework for Cloud and IoT Based Green Healthcare System

Islam

Bhuiyan

2023

IEEE Access

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“…Nevertheless, it was shown that information could be efficiently obtained from clinical notes using Natural Language Processing (NLP) algorithms instead, those algorithms relying even more on machine learning (ML) techniques such as language models. [12][13][14][15][16][17][18][19] Developing tools to this end remains challenging, and many difficulties are yet to be overcome for a wide community to benefit from them. [4,7,[19][20][21][22][23][24] First, the optimal NLP technologies are still debated.…”

Section: Introductionmentioning

confidence: 99%

Collaborative and privacy-preserving workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions

Petit-Jean,

Gérardin,

Berthelot

et al. 2023

Preprint

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ObjectiveTo develop and validate advanced natural language processing pipelines that detect 18 conditions in clinical notes written in French, among which 16 comorbidities of the Charlson index, while exploring a collaborative and privacy-preserving workflow.Materials and methodsThe detection pipelines relied both on rule-based and machine learning algorithms for named entity recognition and entity qualification, respectively. We used a large language model pre-trained on millions of clinical notes along with clinical notes annotated in the context of three cohort studies related to oncology, cardiology and rheumatology, respectively. The overall workflow was conceived to foster collaboration between studies while complying to the privacy constraints of the data warehouse. We estimated the added values of both the advanced technologies and the collaborative setting.ResultsThe 18 pipelines reached macro-averaged F1-score positive predictive value, sensitivity and specificity of 95.7 (95%CI 94.5 - 96.3), 95.4 (95%CI 94.0 - 96.3), 96.0 (95%CI 94.0 - 96.7) and 99.2 (95%CI 99.0 - 99.4), respectively. F1-scores were superior to those observed using either alternative technologies or non-collaborative settings. The models were shared through a secured registry.ConclusionsWe demonstrated that a community of investigators working on a common clinical data warehouse could efficiently and securely collaborate to develop, validate and use sensitive artificial intelligence models. In particular, we provided efficient and robust natural language processing pipelines that detect conditions mentioned in clinical notes.

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“…The data produced by the Internet of Things (IoT) devices can be integrated [2] with other systems, and Recommender Systems (RS) are an alternative to automatically integrate data with predictive models. Several studies have adopted predictive approaches to diagnosis and ways of treatment [3], risk and detection of chronic diseases [1], [4]- [6], detection of the possibility of heart disease [7]- [9], and the use of medical notes to make predictions in health centers [10] and to estimate the likelihood of adverse events in postoperative cases [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare

et al. 2020

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An Initial Investigation of the Charlson Comorbidity Index Regression Based on Clinical Notes

Cited by 8 publications

References 22 publications

An Integrated Scalable Framework for Cloud and IoT Based Green Healthcare System

An Integrated Scalable Framework for Cloud and IoT Based Green Healthcare System

Collaborative and privacy-preserving workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions

Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare

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