Terminology Expansion with Prototype Embeddings: Extracting Symptoms of Urinary Tract Infection from Clinical Text

Alam, Mahbub Ul; Henriksson, Aron; Tanushi, Hideyuki; Thiman, Emil; Nauclér, Pontus; Dalianis, Hercules

doi:10.5220/0010190200470057

Cited by 3 publications

(3 citation statements)

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“…Data intelligence is a critical aspect that we have to explore more in the future, as efficient usability largely depends on the quality of the data [ 55 ]. Data can be a wealth of resources if they can be adequately represented to tackle a healthcare problem such as urinary tract infection detection [ 56 ]. The effective surveillance deployment [ 57 ] also depends on the more nuanced representation of it.…”

Section: Discussionmentioning

confidence: 99%

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Alam

Rahmani

2021

Sensors

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Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

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Section: Discussionmentioning

confidence: 99%

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Alam

Rahmani

2021

Sensors

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“…Seed words were inserted to construct representations for UTI symptoms using four-word embedding approaches and phrase detection methods. Prototype embedding can capture semantic information about UTI symptoms, resulting in more symptom words [3].…”

Section: Related Workmentioning

confidence: 99%

“…Conclusion Referred to in [1] The suggested model outperformed the ANN, CNN, and AlexNet models in terms of precision (96%), recall (96.5%), accuracy (97%), and IoU (0.65). Referred to in [3] With a mean accuracy of between 0.51 and 0.86, 142 additional UTI symptom terms were discovered. Referred to in [5] At the time of admittance, 72% (185/256) of patients who developed a HA-UTI had a risk score of less than 0.15.…”

Section: Previous Papermentioning

confidence: 99%

Gynaecological Disease Diagnosis Expert System (GDDES) Based on Machine Learning Algorithm and Natural Language Processing

de,

Goswami,

Faujdar

et al. 2024

IEEE Access

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In this paper, the Gynaecological Disease Diagnosis Expert System (GDDES) is a Graphical User Interface, developed with the Support Vector Classifier (Machine Learning Algorithm) and Natural Language Processing. It is language-independent, allowing women from any state in India to use the system in their own native tongue and have their disorders diagnosed in that language. The diagnosis process is divided into two steps: At first, the user selects their regional language and the system asks some queries in their selected language and submits the reply for each query, then the system uses the Support Vector Classifier (SVC) Model to predict the disease name; and secondly, the user is prompted to record their symptoms in their native tongue and GDDES uses Natural Language Processing to calculate cosine similarities and play the most similar voice recording of disease diagnosis, and displays the sentences of the recording in the user's native language. The system with the SVC Model provides 93% accuracy and precision and 92% recall and f1 score.

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MedLexSp – a medical lexicon for Spanish medical natural language processing

Llanos

2023

J Biomed Semant

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Background Medical lexicons enable the natural language processing (NLP) of health texts. Lexicons gather terms and concepts from thesauri and ontologies, and linguistic data for part-of-speech (PoS) tagging, lemmatization or natural language generation. To date, there is no such type of resource for Spanish. Construction and content This article describes an unified medical lexicon for Medical Natural Language Processing in Spanish. MedLexSp includes terms and inflected word forms with PoS information and Unified Medical Language System$$^{\circledR }$$ ® (UMLS) semantic types, groups and Concept Unique Identifiers (CUIs). To create it, we used NLP techniques and domain corpora (e.g. MedlinePlus). We also collected terms from the Dictionary of Medical Terms from the Spanish Royal Academy of Medicine, the Medical Subject Headings (MeSH), the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT), the Medical Dictionary for Regulatory Activities Terminology (MedDRA), the International Classification of Diseases vs. 10, the Anatomical Therapeutic Chemical Classification, the National Cancer Institute (NCI) Dictionary, the Online Mendelian Inheritance in Man (OMIM) and OrphaData. Terms related to COVID-19 were assembled by applying a similarity-based approach with word embeddings trained on a large corpus. MedLexSp includes 100 887 lemmas, 302 543 inflected forms (conjugated verbs, and number/gender variants), and 42 958 UMLS CUIs. We report two use cases of MedLexSp. First, applying the lexicon to pre-annotate a corpus of 1200 texts related to clinical trials. Second, PoS tagging and lemmatizing texts about clinical cases. MedLexSp improved the scores for PoS tagging and lemmatization compared to the default Spacy and Stanza python libraries. Conclusions The lexicon is distributed in a delimiter-separated value file; an XML file with the Lexical Markup Framework; a lemmatizer module for the Spacy and Stanza libraries; and complementary Lexical Record (LR) files. The embeddings and code to extract COVID-19 terms, and the Spacy and Stanza lemmatizers enriched with medical terms are provided in a public repository.

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Terminology Expansion with Prototype Embeddings: Extracting Symptoms of Urinary Tract Infection from Clinical Text

Cited by 3 publications

References 0 publications

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Gynaecological Disease Diagnosis Expert System (GDDES) Based on Machine Learning Algorithm and Natural Language Processing

MedLexSp – a medical lexicon for Spanish medical natural language processing

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