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      Analisis Jejaring Penyakit untuk Identifikasi Penyakit Komorbid Dominan Terkait Covid-19

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      Date
      2021
      Author
      Putra, Ryvan Arnandha
      Kusuma, Wisnu Ananta
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      Abstract
      Komorbid merupakan penyakit tambahan baik fisik maupun psikis selain dari kondisi utama pasien yang dapat memperburuk kondisi pasien. Penyakit komorbid pada pasien covid-19 menjadi salah satu penyebab meningkatnya angka kematian. Pencarian penyakit komorbid covid-19 bertujuan untuk membantu tenaga medis khususnya dalam menangani pasien positif covid-19. Tentunya akan berbeda dalam penanganan apabila pasien dengan penyakit komorbid dengan yang tidak. Penyakit komorbid yang dominan dicari dengan melakukan ekstraksi anotasi abstrak Pubmed terkait covid-19 lalu ditampilkan dalam word cloud berdasarkan frekuensi kemunculannya. Hasil analisis istilah penyakit comorbid dalam word cloud akan dihitung skor Disease Ontology-nya untuk kemudian dibuatkan suatu network. Nilai centrality dalam network yang terbesar akan menunjukkan penyakit komorbid dominan pada covid-19.
       
      Comorbid is a coexisting disease that can be both physical and psychological apart from the patient's condition which can worsen the medical status. Comorbidity in covid-19 can increase the mortality rate of the patients. The research on covid-19 with comorbidity aims to help medical personnel in dealing with positive covid-19 patients, due to its different treatment for those who have comorbid and those who are not. The present comorbid disease was searched by extracting annotation abstracts regarding covid-19 in Pubmed and then displayed in the Word Cloud based on the frequency appearance. The results of the term comorbid analysis using word-cloud were calculated based on the Disease Ontology and then made the network. The largest value of centrality in network will indicate the dominant comorbid disease in COVID-19.
       
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      http://repository.ipb.ac.id/handle/123456789/110427
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      Copyright © 2020 Library of IPB University
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      Indonesia DSpace Group 
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