Standard NER Tagging Scheme for Big Data Healthcare Analytics Built on Unified Medical Corpora

Shafqat, Sarah; Majeed, Hammad; Javaid, Qaisar; Ahmad, Hafiz Farooq

doi:10.37965/jait.2022.0127

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…These features relate to the patient's current condition with which he/she visited the doctor for consultation. A sequential model [79] based on the Tensor-Flow Keras library was used to hold a stack of embedding of bidirectional long short-term memory (Bi-LSTM) [80] and dense layers of varying sizes built on the recurrent neural networks architecture. TensorFlow.Keras [78], [81] was adopted as a neural networks interface to preprocess the finalized sequential columns: 'test', 'examine', 'result', 'condition', 'disease', 'medicine' and 'diagnosed' in our 'DM_Comorbid_EHR_ICD10' corpora.…”

Section: Automated Annotationmentioning

confidence: 99%

NER Sequence Embedding of Unified Medical Corpora to Incorporate Semantic Intelligence in Big Data Healthcare Diagnostics

Shafqat,

Anwar,

Javaid

et al. 2024

Preprint

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Clinical diagnosis is a challenging task for which high expertise is required at the doctors’ end. It is recognized that technology integration with the clinical domain would facilitate the diagnostic process. A semantic understanding of the medical domain and clinical context is needed to make intelligent analytics. These analytics need to learn the medical context for different purposes of diagnosing and treating patients. Traditional diagnoses are made through phenotype features from patients’ profiles. It is also a known fact that diabetes mellitus (DM) is widely affecting the population and is a chronic disease that requires timely diagnosis. The motivation for this research comes from the gap found in discovering the common ground for medical context learning in analytics to diagnose DM and its comorbidity diseases. Therefore, a unified medical knowledge base is found significantly important to learning contextual Named Entity Recognition (NER) embedding for semantic intelligence. Researchers in this paper have searched for possible solutions for medical context learning and found that unified corpora tagged with medical terms were missing to train the analytics for diagnoses of DM and its comorbidities. Hence, effort was put into collecting endocrine diagnostic electronic health records (EHR) corpora for clinical purposes that are manually labeled with ICD-10-CM international coding scheme to minimise chances of error. International Codes for Diseases (ICD) by the World Health Organization (WHO) is a known schema to represent medical codes for diagnoses. The complete endocrine EHR corpora make DM-Comorbid-EHR-ICD-10 Corpora. DM-Comorbid-EHR-ICD-10 Corpora is tagged for understanding the medical context with uniformity. In this research experiments were run with different NER sequence embedding approaches using advanced ML integrated with NLP techniques. These experiments used common frameworks like; Spacy, Flair, and TensorFlow, Keras. These experiments led to yield albeit label sets in the form of (instance, label) pair for diagnoses that were tagged with the Sequential() model found in TensorFlow.Keras using Bi-LSTM and dense layers. The maximum accuracy achieved was 0.9 for Corpus14407_DM_pts_33185 with a maximum number of diagnostic features taken as input. The sequential DNN NER model diagnostic accuracy increased as the size of the corpus grew from 100 to 14407 DM patients suffering from comorbidity diseases. The significance of clinical notes and practitioner comments available as free text is clearly seen in the diagnostic accuracy.

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Section: Automated Annotationmentioning

confidence: 99%

NER Sequence Embedding of Unified Medical Corpora to Incorporate Semantic Intelligence in Big Data Healthcare Diagnostics

Shafqat,

Anwar,

Javaid

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…(iii) Medical application is one of the emerging domains of artificial intelligence and robotics. The third paper presents a mechanism to come up with uniform Named Entity Recognition (NER) tagged medical corpora that is fed and trained with 14,407 endocrine patients' dataset in CSV format diagnosed with diabetes mellitus and comorbidity diseases [5]. (iv) The fourth paper discusses the small inspection robots that explore different types of environments and collect data from dangerous or difficult to access areas in an environment.…”

Section: Preview Of the Studies In This Issuementioning

confidence: 99%

Latest Advances on AI and Robotics Applications

Silva

2022

JAIT

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The applications of artificial intelligence and robotics, their associated technologies, and their integration continue to grow and are seen in more and more diverse fields. With these considerations in mind, this issue of the Journal of Artificial Intelligence and Technology presents five state-of-the-art peer-reviewed contributions, ranging from theory to prototyping, and covering several distinct application topics.

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“…However, the accumulation of diverse pollutants in industrial waste disposal has resulted in a scarcity of clean water [4,5]. As a consequence, environmental regulations have become increasingly stringent, and researchers have shifted their focus to developing effective treatments for liquid effluents, safeguarding public health [6][7][8]. Among these pollutants, heavy metals are a significant concern due to their potent toxicity and profound impact on human wellbeing [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Innovative Solutions for Water Treatment: Unveiling the Potential of Polyoxazoline Polymer Activated Carbon Composite for Efficient Elimination of Lead Ions

Amari,

Boujelbene,

Sami

et al. 2024

Water

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Heavy metal pollution is a global problem that necessitates the development of innovative and environmentally friendly water treatment technologies. Polyoxazoline polymers, known for their biocompatibility, are explored for lead ion removal in water treatment. Poly 2-Methoxycarbonylpropyl-2-oxazoline is integrated into activated carbon via in situ polymer growth, optimizing loading through live polymerization. This study investigates intricate interactions between lead ions and functional groups, such as amide moieties and ester functionalities, in the resulting polyoxazoline-modified activated carbon composite (POZ-AC). This pioneering research opens avenues for the application of polyoxazoline polymers in water treatment, leveraging their established success in biomedical fields. The removal of lead ions by POZ-ACs followed the Langmuir isotherm and pseudo-second-order kinetic model. The results showed that POZ-AC-20 had excellent adsorption capacity of 365 mg/g, achieved in a relatively short time of 37 min. Furthermore, the adsorbent maintained its performance for seven cycles, demonstrating its high reusability potential. However, the adsorption performance of POZ-ACs after seven adsorption–desorption cycles was gradually decreased due to polymer release into the water media because of the high degree of solubility of polyoxazoline polymers in water. This study provides critical insight into the potential use of polyoxazoline polymers, demonstrating their superior potential in water treatment applications, particularly since it is the first time these polymers have been explored for this purpose. Future research should focus on developing polyoxazoline polymers with less solubility in water while maintaining a high removal performance.

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Standard NER Tagging Scheme for Big Data Healthcare Analytics Built on Unified Medical Corpora

Cited by 14 publications

References 13 publications

NER Sequence Embedding of Unified Medical Corpora to Incorporate Semantic Intelligence in Big Data Healthcare Diagnostics

NER Sequence Embedding of Unified Medical Corpora to Incorporate Semantic Intelligence in Big Data Healthcare Diagnostics

Latest Advances on AI and Robotics Applications

Innovative Solutions for Water Treatment: Unveiling the Potential of Polyoxazoline Polymer Activated Carbon Composite for Efficient Elimination of Lead Ions

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