MiNerDoc: a Semantically Enriched Text Mining System to Transform Clinical Text into Knowledge

Luque, Carmen; Luna, José María; Ventura, Sebastián

doi:10.1109/cbms.2019.00142

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…We designed a rule-based approach to automatically normalise a local testing name towards a Logical Observation Identifiers Names and Codes (LONIC) code of MedIMG utilising data from several medical systems. Following tokenization of the input lab testing names, specific items are recognised, and relevant LONIC codes get automatically mapped depending upon that coding rules (24). Figure 2 depicts an overall view of the MedIMG TestNorm system's module, which primarily includes NER as well as LONIC map-based modules, using inputs from lexicons/coding rules.…”

Section: Named Entity Recognition Using Lonicmentioning

confidence: 99%

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Bhatia

Alojail

Sengan

et al. 2022

Front. Public Health

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Handwritten prescriptions and radiological reports: doctors use handwritten prescriptions and radiological reports to give drugs to patients who have illnesses, injuries, or other problems. Clinical text data, like physician prescription visuals and radiology reports, should be labelled with specific information such as disease type, features, and anatomical location for more effective use. The semantic annotation of vast collections of biological and biomedical texts, like scientific papers, medical reports, and general practitioner observations, has lately been examined by doctors and scientists. By identifying and disambiguating references to biomedical concepts in texts, medical semantics annotators could generate such annotations automatically. For Medical Images (MedIMG), we provide a methodology for learning an effective holistic representation (handwritten word pictures as well as radiology reports). Deep Learning (DL) methods have recently gained much interest for their capacity to achieve expert-level accuracy in automated MedIMG analysis. We discovered that tasks requiring significant responsive fields are ideal for downscaled input images that are qualitatively verified by examining functional, responsive areas and class activating maps for training models. This article focuses on the following contributions: (a) Information Extraction from Narrative MedImages, (b) Automatic categorisation on image resolution with an impact on MedIMG, and (c) Hybrid Model to Predictions of Named Entity Recognition utilising RNN + LSTM + GRM that perform admirably in every trainee for every input purpose. At the same time, supplying understandable scale weight implies that such multi-scale structures are also crucial for extracting information from high-resolution MedIMG. A portion of the reports (30%) are manually evaluated by trained physicians, while the rest were automatically categorised using deep supervised training models based on attention mechanisms and supplied with test reports. MetaMapLite proved recall and precision, but also an F1-score equivalent for primary biomedicine text search techniques and medical text examination on many databases of MedIMG. In addition to implementing as well as getting the requirements for MedIMG, the article explores the quality of medical data by using DL techniques for reaching large-scale labelled clinical data and also the significance of their real-time efforts in the biomedical study that have played an instrumental role in its extramural diffusion and global appeal.

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Section: Named Entity Recognition Using Lonicmentioning

confidence: 99%

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Bhatia

Alojail

Sengan

et al. 2022

Front. Public Health

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“…Our findings show that for standard size datasets greater than 150 patients, pre-trained database embeddings superior, whereas local-learned embeddings becoming highly expensive as cohorts size increases. Luque et al, in (IEEE 2019) [9], proposed system to assist the clinical decision-making process by reviewing loads of text based health documents reports in a coherent context, is proposed. In the medical sector, system performs two fundamental functions of great importance like classification of health conditions on the basis of multiple healthcare authorities' understanding and automatic interpretation of structured clinical information.…”

Section: Review Of Literaturementioning

confidence: 99%

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

Puri¹,

Patnaik²

2021

INDJCSE

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Machine learning supervised classification plays major role in large text classification. In health care data it contributes since couple of years for generating the privacy and security. Such kind of electric records might take large data on storage devices, so it needs to optimize with some processing techniques. The Natural Language Processing (NLP) features to represent text as a vector appropriate for use in machine learning algorithms. To describe text as just a vector suitable for use in machine learning algorithms, text classification techniques frequently rely on dictionary counters including bag-of-words as well NLP techniques. As an alternative feature set, editable search queries are proposed in this work. The suggested model utilizes the Smith Waterman (SW) optimization created by Pair -wise alignment to create a set of pattern matching characteristics based on labeled textual information or to train a later part classifier. The Naive Bayes (NB) supervised learning algorithm has used for classification. While a correlation of the special groups and traditional text characteristics shows that a classification algorithm does not make better decisions utilizing generated features, the produced factors can catch patterns that cannot be identified with collective expertise. As a result, it is significantly boost a classifier that combines conventional methods for generated characteristics. In the experimental analysis we evaluate the proposed system results with various existing systems that our system demonstrates effective results than traditional approaches.

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MiNerDoc: a Semantically Enriched Text Mining System to Transform Clinical Text into Knowledge

Cited by 2 publications

References 14 publications

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

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