SmartHealth Simulation Representing a Hybrid Architecture Over Cloud Integrated with IoT

Shafqat, Sarah; Abbasi, Almas; Amjad, Tehmina; Ahmad, Hafiz Farooq

doi:10.1007/978-3-030-03405-4_31

Cited by 13 publications

(2 citation statements)

References 15 publications

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“…There have been growing platforms for NLP processing on text in clinical notes to form interoperable data models but the first one was MedLEE (Medical Language Extraction and Encoding). Mayo Clinic [14], [17], while working on Learning Healthcare System also devised an NLP pipeline cTAKES (clinical text analysis and knowledge extraction system) that is open source to get clinical rules for symptoms, diagnosis, medication, etc. Research has led to the development of a large corpus of clinical text taken from Mayo Clinic in syntactic form.…”

Section: Related Workmentioning

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: Related Workmentioning

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

“…The realization [ 3 , 6 ] to conquer problems associated with traditional healthcare through computation is a complex task especially in diagnosing [ 4 , 7 ]. Advances are still being made with help of deep learning neural net techniques [ 8 ] under the umbrella of artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics

Shafqat

Fayyaz

Khattak

et al. 2021

Neural Process Lett

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Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.

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SmartHealth: IoT-Enabled Context-Aware 5G Ambient Cloud Platform

Shafqat

Khan

Shafqat

2021

IoT in Healthcare and Ambient Assisted Living

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SmartHealth Simulation Representing a Hybrid Architecture Over Cloud Integrated with IoT

Cited by 13 publications

References 15 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

Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics

SmartHealth: IoT-Enabled Context-Aware 5G Ambient Cloud Platform

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