Accuracy of using natural language processing methods for identifying healthcare-associated infections

Tvardik, Nastassia; Kergourlay, Ivan; Bittar, André; Segond, Frédérique; Darmoni, Stéfan Jacques; Metzger, M.-H.

doi:10.1016/j.ijmedinf.2018.06.002

Cited by 36 publications

(33 citation statements)

References 25 publications

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“…MetaMap handicapped itself in the generalization of outcomes due to a small sample size and tend to acquire long processing time which makes it a hard choice for real-time large datasets. NLP was used in another research for Healthcare-Associated Infections (HAI) monitoring [61]. The major objectives were sensitivity and specificity.…”

Section: A Natural Language Processing (Nlp)mentioning

confidence: 99%

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Raja¹,

Asghar²

2020

IJACSA

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Clinical advancements are one of the major outcomes of the technological phase shift of data sciences. The significance of information technology in medical sciences by utilizing the Clinical Decision Support System (CDSS) has opened the spillways of exponentially improved predictive models. Utilizing the latest norms of classification algorithms on clinical data are widely incorporated for prognostic assessments. Medical experts have to make decisions that are crucial in nature and if the research can develop a mechanism that assists them in evolving solid reasoning, infer the knowledge and clearly express their clinical decision by justifying their assertions made, it will be a win-win situation. However, this field of science is still an unknown world for clinicians despite the fact that the enormous amount of medical data cannot be exploited to its maximum without invoking the technological support. The objective of this research is to introduce the clinicians and policymakers of the medical domain with the renowned computer-based methodologies employed to construct a clinical decision support system. We expect that gaining the technical insight into the medical domain by the stakeholders will ensure commissioning the accurate and effective CDSS for improved healthcare delivery.

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Section: A Natural Language Processing (Nlp)mentioning

confidence: 99%

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Raja¹,

Asghar²

2020

IJACSA

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“…Concerning a similar issue, the work by Tvardik et al (54) analyzed EMRs in order to identify HAI (healthcare-associated infections). Actually, they focused on the implementation of semantic algorithms and expert rules.…”

Section: Nlp Applications In Clinical Contextmentioning

confidence: 99%

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

et al. 2019

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Clinical practice requires the production of a time-and resource-consuming great amount of notes. They contain relevant information, but their secondary use is almost impossible, due to their unstructured nature. Researchers are trying to address this problems, with traditional and promising novel techniques. Application in real hospital settings seems not to be possible yet, though, both because of relatively small and dirty dataset, and for the lack of language-specific pre-trained models. Aim: Our aim is to demonstrate the potential of the above techniques, but also raise awareness of the still open challenges that the scientific communities of IT and medical practitioners must jointly address to realize the full potential of unstructured content that is daily produced and digitized in hospital settings, both to improve its data quality and leverage the insights from data-driven predictive models. Methods: To this extent, we present a narrative literature review of the most recent and relevant contributions to leverage the application of Natural Language Processing techniques to the free-text content electronic patient records. In particular, we focused on four selected application domains, namely: data quality, information extraction, sentiment analysis and predictive models, and automated patient cohort selection. Then, we will present a few empirical studies that we undertook at a major teaching hospital specializing in musculoskeletal diseases. Results: We provide the reader with some simple and affordable pipelines, which demonstrate the feasibility of reaching literature performance levels with a single institution non-English dataset. In such a way, we bridged literature and real world needs, performing a step further toward the revival of notes fields.

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“…• A cross-lingual terminology server, HeTOP, which contains 75 terminologies and ontologies in 32 languages [1] • A semantic annotator based on NLP bag-of-word methods (ECMT) [2] • A semantic multilingual search engine [3] To improve the semantic annotator, it is possible to implement deep learning techniques to the already existent one. To do so, a new text representation, which keeps the most semantic similarities existing between words, has to be designed to fit the input of neural networks algorithms (text embedding).…”

Section: Contextmentioning

confidence: 99%

Word embedding for French natural language in healthcare: a comparative study (Preprint)

Dynomant¹,

Lelong²,

Dahamna³

et al. 2018

Preprint

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BACKGROUND Word embedding technologies are now used in a wide range of applications. However, no formal evaluation and comparison have been made on models produced by the three most famous implementations (Word2Vec, GloVe and FastText). OBJECTIVE The goal of this study is to compare embedding implementations on a corpus of documents produced in a working context, by health professionals. METHODS Models have been trained on documents coming from the Rouen university hospital. This data is not structured and cover a wide range of documents produced in a clinic (discharge summary, prescriptions ...). Four evaluation tasks have been defined (cosine similarity, odd one, mathematical operations and human formal evaluation) and applied on each model. RESULTS Word2Vec had the highest score for three of the four tasks (mathematical operations, odd one similarity and human validation), particularly regarding the Skip-Gram architecture. CONCLUSIONS Even if this implementation had the best rate, each model has its own qualities and defects, like the training time which is very short for GloVe or morphosyntaxic similarity conservation observed with FastText. Models and test sets produced by this study will be the first publicly available through a graphical interface to help advance French biomedical research.

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Accuracy of using natural language processing methods for identifying healthcare-associated infections

Cited by 36 publications

References 25 publications

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

Word embedding for French natural language in healthcare: a comparative study (Preprint)

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