A Unified Deep Learning Diagnostic Architecture for Big Data Healthcare Analytics

Shafqat, Sarah; Anwar, Zahid; Javaid, Qaisar; Ahmad, Hafiz Farooq

doi:10.1109/isads56919.2023.10092137

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…Finally, the model is executed on a cloud platform to return the primary diagnosis and comorbidity diseases. An abstraction of this figure has recently been presented by researchers at an international conference [43]. Detailed descriptions of the modules comprising our system are described in the subsections below.…”

Section: Architecture and Designmentioning

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

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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: Architecture and Designmentioning

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

“…Finally the model is executed on a cloud platform to return the primary diagnosis and comorbidity diseases. An abstraction of this figure has recently been presented by the researchers in an international conference [40]. Detailed descriptions of its modules comprising our system are described in the subsections below.…”

Section: Architecture and Designmentioning

confidence: 99%

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

Shafqat

Anwar

Javaid

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Clinical diagnoses is a challenging task for which high expertise is required at doctors’ end. It is recognized that technology integration with clinical domain would facilitate the diagnostic process. Semantic understanding of 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’ profile. It is also a known fact that diabetes mellitus (DM) is widely affecting the population and is a chronic disease that requires timely diagnosis. 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 learn contextual Named Entity Recognition (NER) embedding for semantic intelligence. Our search for possible solutions for medical context learning told us that unified corpora tagged with medical terms was missing to train the analytics for diagnoses of DM and its comorbidities. Hence, we put effort in collecting endocrine diagnostic electronic health records (EHR) corpora for clinical purposes that is labeled with ICD-10-CM international coding scheme. International Codes for Diseases (ICD) by World Health Organization (WHO) is a known schema to represent medical codes for diagnoses. The complete endocrine EHR corpuses make DM-Comorbid-EHR-ICD-10 Corpora. DM-Comorbid-EHR-ICD-10 Corpora is tagged for understanding the medical context with uniformity. We experimented with different NER sequence embedding approaches using advanced ML integrated with NLP techniques. Different experiments used common frameworks like; Spacy, Flair, and TensorFlow, Keras. In our experiments albeit label sets in form of (instance, label) pair for diagnoses were tagged with 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 maximum number of diagnostic features taken as input. The sequential DNN NER model diagnostic accuracy increased as the size of 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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Cloud-Integrated Big Data Algorithms for Deep Learning in Healthcare System

Srivastava,

Mahto,

Verma

et al. 2024

Advances in Science, Technology &Amp; Innovation

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A Unified Deep Learning Diagnostic Architecture for Big Data Healthcare Analytics

Cited by 9 publications

References 19 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

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

Cloud-Integrated Big Data Algorithms for Deep Learning in Healthcare System

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