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All Journal Belitung Nursing Journal Journals of Ners Community Journal of Telenursing (JOTING) Indonesian Journal of Global Health research Jurnal Minfo Polgan (JMP) Jurnal Keperawatan Portal Riset dan Inovasi Sistem Perangkat Lunak
Chodidjah, Siti
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Computer Science & IT Decision Sciences, Operations Research & Management Education Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Nursing Public Health Social Sciences

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Diagnostic Accuracy of Delirium Assessment Tools Among Critically Ill Infant : A Systematic Review Rahmadhani, Dewi Astika; Ningsih, Risna; Setiawati, Atik; Chodidjah, Siti; Agustini, Nur; Huda, Mega Hasanul
Indonesian Journal of Global Health Research Vol 7 No 3 (2025): Indonesian Journal of Global Health Research
Publisher : GLOBAL HEALTH SCIENCE GROUP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37287/ijghr.v7i3.6214

Abstract

Delirium is an acute change in neurologic function that can potentially lead to longterm impacts on children’s cognitive development and the quality of life. Infants under 12 months are particularly vulnerable because their cognitive and language abilities are not fully developed. Therefore, healthcare professionals need to enhance their knowledge of delirium symptoms, child development stages, and how to identify it in this age group to better detection and management. This study aims to evaluate the diagnostic accuracy of delirium assessment tools, namely the Cornell Assessment of Pediatric Delirium (CAPD), the Preschool Confusion Assessment Method for the ICU (psCAM-ICU), and the Sophia Observation Withdrawal Symptoms Pediatric Delirium (SOSPD), in detecting delirium in critically ill infants. This systematic review follows the PRISMA 2020 guidelines and includes a literature search in PubMed, Scopus, ProQuest, ScienceDirect, and Taylor & Francis from 2013 to 2023. Inclusion criteria consist of observational studies involving infants aged 0-11 months in ICU settings that utilized CAPD, psCAM-ICU, or SOSPD for delirium detection. The quality of the studies was assessed using the JBI Critical Appraisal Checklist for Studies Reporting Diagnostic Test Accuracy. Result : The analysis indicates that the SOSPD tool has a sensitivity ranging from 76.9% to 96.8% and specificity between 92% and 96.4%. The CAPD shows sensitivity from 87% to 94.1% and specificity from 88% to 98%. The psCAM exhibits sensitivity from 75% to 95% and specificity from 81% to 91%. The results demonstrate variability in accuracy depending on the age group and clinical condition of the children. Based on the research findings, psCAM is recommended as the most effective tool for detecting delirium in the infant population due to its ease of use and high accuracy. Early detection of delirium is crucial for enhancing clinical management and improving outcomes in critically ill infants.
Perbandingan Metode Algoritma Decision Tree C4.5 Dan Naïve Bayes Untuk Memprediksi Penyakit Tiroid Safitri, Leli; Cahayani Murtiwiyati, Krista; Chodidjah, Siti; Indayanti, Deasy
Journals of Ners Community Vol 13 No 5 (2022): Jurnal of Ners Community
Publisher : Fakultas Ilmu Kesehatan Universitas Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55129/jnerscommunity.v13i5.2121

Abstract

Penyakit tiroid adalah kelenjar endokrin murni terbesar di tubuh manusia, terletak di leher bagian depan. Gangguan fungsi tiroid seringkali sulit dikenali karena gejalanya tidak spesifik, dan sering diabaikan karena gejala penyakit tiroid sangat mirip dengan banyak penyakit gaya hidup modern. pasien seringkali tidak menyadari ada masalah pada dirinya dan tidak memeriksakan diri ke dokter. Oleh karena itu, penelitian dibidang kesehatan dilakukan untuk pengobatan lebih dini, guna mencegah kematian akibat terlambatnya penanganan. Penelitian ini menggunakan metode klasifikasi data mining Algoritma Decision Tree C4.5 dan Naïve Bayes dengan tujuan agar algoritma terpilih merupakan algoritma yang menghasilkan nilai akurasi dan nilai Area Under Curve (AUC) yang lebih baik. Data penelitian menggunakan Thyroid Disease Dataset UCI (University of California, Irvine) Machine Learning Repository. Hasil pengujian menunjukkan bahwa akurasi lebih baik diperoleh dari Algoritma Decision Tree C4.5 sebesar 97,12% sedangkan nilai akurasi Algoritma Naïve Bayes sebesar 76,02%. Nilai Area Under Curve (AUC) pada kurva Receiver Operating Characteristic (ROC) menunjukkan Algoritma Decision Tree C4.5 memiliki nilai lebih tinggi dari Algoritma Naïve Bayes dengan hasil klasifikasi Good Classification.
Co-Authors Abraar, Arinda Adawiyah, Robiyatul Allenidekania Allenidekania Arnoldy, Vincentia Exandy Salvera Astuti Astuti Athallah, Muhammad Hanif Herdianto Cahayani Murtiwiyati, Krista Deasy Indayanti Etty Rekawati Garis, Ariq Finkhan Imami Nur Rachmawati Mega Hasanul Huda Nani Nurhaeni Ningsih, Risna Novia Pratiwi, Novia Noviyanti, Mita Nur Agustini Rahmadhani, Dewi Astika Safitri, Leli sari, endang puspita Setiawati, Atik Sitinjak, Eva Perawaty