A computational system based on ontologies to automate the mapping process of medical reports into structured databases

Oliva, Jefferson Tales; Lee, Huei; Spolaôr, Newton; Takaki, Weber Shoity Resende; Coy, Cláudio Saddy Rodrigues; Fagundes, João José; Wu, Feng

doi:10.1016/j.eswa.2018.08.004

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…A piece of text is a sequence of words which can be correlated. Before the text preprocessing the LSTM, BiLSTM and CNN will first learn the correlated words and then classify the sequence data depending on the degree of the correlations dependencies [20][21][22][23][24][25][26][27][28][29]. In this study, "Text Analytics" (The Mathworks ©) has been used, which classify texts using NLP algorithms [22].…”

Section: Text Preprocessingmentioning

confidence: 99%

Epilepsy Radiology Reports Classification Using Deep Learning Networks

Bayrak¹,

Yücel²,

Takçı³

2022

Computers, Materials &Amp; Continua

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The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given text report our systems first cleans HTML/XML tags, tokenize, erase punctuation, normalize text, (ii) then it converts into MRI text reports numeric sequences by using indexbased word encoding, (iii) then we applied the deep learning models that are uni-directional long short-term memory (LSTM) network, bidirectional long short-term memory (BiLSTM) network and convolutional neural network (CNN) for the classifying comparison of the data, (iv) finally, we used 70% of used for training, 15% for validation, and 15% for test observations. Unlike previous methods, this study encompasses the following objectives: (a) to extract significant text features from radiologic reports of epilepsy disease; (b) to ensure successful classifying accuracy performance to enhance epilepsy data attributes. Therefore, our study is a comprehensive comparative study with the epilepsy dataset obtained from numeric sequences by using index-based word encoding method applied for the deep learning models. The traditional method is numeric sequences by using index-based word encoding which has been made for the first time in the literature, is successful feature descriptor in the epilepsy data set. The BiLSTM network has shown a promising performance regarding the accuracy rates. We show that the larger sized medical text reports can be analyzed by our proposed method.

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Section: Text Preprocessingmentioning

confidence: 99%

Epilepsy Radiology Reports Classification Using Deep Learning Networks

Bayrak¹,

Yücel²,

Takçı³

2022

Computers, Materials &Amp; Continua

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“…The experimental results demonstrated that using an ontology reduced the total number of triples, thereby pruning the search space. Recently, Oliva et al 37 presented a technique to transform medical reports written in natural language with the assistance of an ontology model. Natural language processing techniques were used to extract significant terms from the medical reports, and the extracted terms were mapped to concepts in an ontology to determine whether those terms were attributes or instances of attributes.…”

Section: Knowledge‐based and Data‐mining Processesmentioning

confidence: 99%

Semantic data mining in the information age: A systematic review

Chanmee

Kesorn

2021

Int J Intell Syst

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Data mining is the discovery of meaningful information or unrevealed patterns in data. Traditional data‐mining approaches, using statistical calculations, machine learning, artificial intelligence, and database technology, cannot interpret data on a conceptual or semantic level and fail to reveal the meanings within the data. This results in a user not being analyzed and determines its signification and implications. Several semantic data‐mining approaches have been proposed in the past decade that overcome these limitations by using a domain ontology as background knowledge to enable and enhance data‐mining performance. The main contributions of this literature survey include organizing the surveyed articles in a new way that provides ease of understanding for interested researchers, and the provision of a critical analysis and summary of the surveyed articles, identifying the contribution of these papers to the field, and the limitations of the analysis methods and approaches discussed in this corpus, with the intention of informing researchers in this growing field in their innovative approaches to new research. Finally, we identify the future trends and challenges in this study track that will be of concern to future researchers, such as dynamic knowledge‐based methods or big‐data tool collaboration. This survey article provides a comprehensive overview of the literature on domain ontologies as used in the various semantic data‐mining tasks, such as preprocessing, modeling, and postprocessing. We investigated the role of semantic data mining in the field of data science and the processes and methods of applying semantic data mining to a data resource description framework.

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“…It can provide crucial, although shallow, semantic information for tasks such as question answering (Abujabal et al, 2018;Blanco-Fernández et al, 2020), topic disambiguation (Fernández, N. et al 2012) or detection (Krasnashchok et al, 2018;Lo et al, 2017, Al-Nabki et al, 2019 and revealment of elements relationships (Sarica et al, 2020;Amal et al, 2019). Furthermore, NER has proved to be effective in broader applications, such as user profiling (Nicoletti et al, 2013) and ontology development on unconventional domains (Oliva et al, 2019;Rodrigues et al, 2019). Anyway, since NER is a classification task, for using the most advanced approach in terms of accuracy (corpus-based NER uses deep neural networks) (Devlin et al, 2018), a training set is needed, i.e.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

SkillNER: Mining and Mapping Soft Skills from any Text

Fareri,

Melluso,

Chiarello

et al. 2021

Preprint

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In today's digital world there is an increasing focus on soft skills. The reasons are many, however the main ones can be traced down to the increased complexity of labor market dynamics and the shift towards digitalisation. Digitalisation has also increased the focus on soft skills, since such competencies are hardly acquired by Artificial Intelligence Systems. Despite this growing interest, researchers struggle in accurately defining the soft skill concept and in creating a complete and shared list of soft skills. Therefore, the aim of the present paper is the development of an automated tool capable of extracting soft skills from unstructured texts. Starting from an initial seed list of soft skills, we automatically collect a set of possible textual expressions referring to soft skills, thus creating a Soft Skills list. This has been done by applying Named Entity Recognition (NER) on a corpus of scientific papers developing a novel approach and a software application able to perform the automatic extraction of soft skills from text: the SkillNER. We measured the performance of the tools considering different training models and validated our approach comparing our list of soft skills with the skills labelled as transversal in ESCO (European Skills/Competence Qualification and Occupation). Finally we give a first example of how the SkillNer can be used, identifying the relationships among ESCO job profiles based on soft skills shared, and the relationships among soft skills based on job profiles in common. The final map of soft skills-job profiles may help accademia in achieving and sharing a clearer definition of what soft skills are and fuel future quantitative research on the topic.

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A computational system based on ontologies to automate the mapping process of medical reports into structured databases

Cited by 10 publications

References 32 publications

Epilepsy Radiology Reports Classification Using Deep Learning Networks

Epilepsy Radiology Reports Classification Using Deep Learning Networks

Semantic data mining in the information age: A systematic review

SkillNER: Mining and Mapping Soft Skills from any Text

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