Clustering of biomedical documents using ontology-based TF-IGM enriched semantic smoothing model for telemedicine applications

Sandhiya, R.; Sundarambal, M.

doi:10.1007/s10586-018-2023-4

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…The first model is the ontology based TF-IGM enriched clustering model [13] which is a hybrid of concept based approach & improved semantic smoothing model. This model utilizes TF-IGM factor and improved background elimination which improves the density handling of general words while also TF-IGM incorporates a new statistical model to precisely measure the class distinguishing power of a term across different classes of text in documents.…”

Section: Ontology Enabled Clinical Document Clustering Algorithmsmentioning

confidence: 99%

“…The proposed RRHT application is evaluated in a constrained simulation environment in MATLAB to verify the theoretical expertise. The simulation settings are utilized as in [13]. The proposed RRHT model utilized biomedical document clustering algorithms in the form of CSO based clustering and GCSO clustering.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Transmitted data are collected and fed to the Processor containing the decision support system and clustering module where the ontology enabled clustering algorithms cluster and analyses the data with the previous patient data to determine the changes in health condition. The clustering algorithms namely ontology based TF-IGM enriched semantic smoothing based clustering [13], chicken swarm optimization (CSO) based model and genetic-chicken swarm optimization (GCSO) based models for biomedical and clinical document clustering are utilized for this purpose. The clinical results analyzed in this stage along with suggestion for medications are forwarded to the e-health centers and/or physicians for approval before transmitting them to the patient care or medical staff attending the patients.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Remote Healthcare and Telemedicine Application Model using Ontology Enabled Clustering of Biomedical & Clinical Documents

Sandhiya*,

Sundarambal

2020

IJITEE

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Remote health monitoring has become a hot topic research due to its multi-dimensional benefits to the society. This paper is aimed at developing a novel remote health monitoring model through wireless sensor networks to ensure efficient telemedicine process. The proposed model, Real-time Remote Healthcare and Telemedicine (RRHT) utilizes the concept of model based design to provide low cost and time saving efficiency. First the low power consuming sensor nodes are placed at specified body points with facility to monitor and reduce the power consumption at each stage of the designed model. These nodes collect the patient data and transmit them in wireless medium through the gateway where the data are combined to form documents/notes. Autonomous optimized routing algorithm is employed at this stage for transmission through efficient wireless paths to the processor connected at the hospitals or health centers. At the processor, the transmitted patient data documents are clustered using ontology enabled clustering models using chicken swarm optimization (CSO) and genetic chicken swarm optimization (GCSO). The clustered results are comparatively analyzed with the previous patient database and to determine the change in health readings. Based on these findings, the suitable medication details along with advice on hospital visits are suggested by the decision module and are sent to the physicians or medical experts for approval and further diagnosis. The performance analysis shows that the proposed RRHT system with GCSO clustering is highly reliable and accurate with better speed and lower cost. These results also prove that the RRHT significantly improved the healthcare application through the utilization of better strategies in document clustering of patient data.

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Section: Ontology Enabled Clinical Document Clustering Algorithmsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Remote Healthcare and Telemedicine Application Model using Ontology Enabled Clustering of Biomedical & Clinical Documents

Sandhiya*,

Sundarambal

2020

IJITEE

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“…Document clustering uses algorithms to partition collections of documents into groups with semantically similar information to make analysis of documents more manageable (Subakti et al, 2022 ). Document clustering has been applied to text in many contexts, for example social media (Curiskis et al, 2020 ), medicine (Sandhiya & Sundarambal, 2019 ), law (Bhattacharya et al, 2022 ; Dhanani et al, 2021 ), hospitality (Kaya et al, 2022 ), patents (Choi & Jun, 2014 ; Kim et al, 2020 ), regulatory data (Levine et al, 2022 ), and engineering documents (Arnarsson et al, 2021 ). There are many unique challenges associated with document clustering.…”

Section: Introductionmentioning

confidence: 99%

Research proposal content extraction using natural language processing and semi-supervised clustering: A demonstration and comparative analysis

Knisely

Pavliscsak

2023

Scientometrics

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Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised document clustering is introduced to partially automate classification of research proposals based on thematic areas of interest. The methodology consists of three stages: (1) manual annotation of a document sample; (2) semi-supervised clustering of documents; (3) evaluation of cluster results using quantitative metrics and qualitative ratings (coherence, relevance, distinctiveness) by experts. The methodology is described in detail to encourage replication and is demonstrated on a real-world data set. This demonstration sought to categorize proposals submitted to the US Army Telemedicine and Advanced Technology Research Center (TATRC) related to technological innovations in military medicine. A comparative analysis of method features was performed, including unsupervised vs. semi-supervised clustering, several document vectorization techniques, and several cluster result selection strategies. Outcomes suggest that pretrained Bidirectional Encoder Representations from Transformers (BERT) embeddings were better suited for the task than older text embedding techniques. When comparing expert ratings between algorithms, semi-supervised clustering produced coherence ratings ~ 25% better on average compared to standard unsupervised clustering with negligible differences in cluster distinctiveness. Last, it was shown that a cluster result selection strategy that balances internal and external validity produced ideal results. With further refinement, this methodological framework shows promise as a useful analytical tool for institutions to unlock hidden insights from untapped archives and similar administrative document repositories. Supplementary Information The online version contains supplementary material available at 10.1007/s11192-023-04689-3.

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“…Another experiments based on ANN is conducted by Saini et.al [4], since they formulating Self Organizing Map (SOM) for generating various genetic operation to achieve the best clusters during the iteration of the algorithms. Another approach of document clustering is by employing pre-constructed ontology network which have condicted by Rupashinga & Park [5], Kang et.al [6], and Sandhiya & Sudarambal [7]. By using ANN and ontology, document clustering reach a promising accuracy.…”

Section: Introductionmentioning

confidence: 99%

Social-Child-Case Document Clustering based on Topic Modeling using Latent Dirichlet Allocation

Tresnasari¹,

Adji²,

Permanasari³

2020

Indonesian J. Comput. Cybern. Syst.

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Children are the future of the nation. All treatment and learning they get would affect their future. Nowadays, there are various kinds of social problems related to children. To ensure the right solution to their problem, social workers usually refer to the social-child-case (SCC) documents to find similar cases in the past and adapting the solution of the cases. Nevertheless, to read a bunch of documents to find similar cases is a tedious task and needs much time. Hence, this work aims to categorize those documents into several groups according to the case type. We use topic modeling with Latent Dirichlet Allocation (LDA) approach to extract topics from the documents and classify them based on their similarities. The Coherence Score and Perplexity graph are used in determining the best model. The result obtains a model with 5 topics that match the targeted case types. The result supports the process of reusing knowledge about SCC handling that ease the finding of documents with similar cases

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Clustering of biomedical documents using ontology-based TF-IGM enriched semantic smoothing model for telemedicine applications

Cited by 8 publications

References 36 publications

Real-Time Remote Healthcare and Telemedicine Application Model using Ontology Enabled Clustering of Biomedical & Clinical Documents

Real-Time Remote Healthcare and Telemedicine Application Model using Ontology Enabled Clustering of Biomedical & Clinical Documents

Research proposal content extraction using natural language processing and semi-supervised clustering: A demonstration and comparative analysis

Social-Child-Case Document Clustering based on Topic Modeling using Latent Dirichlet Allocation

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