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All Journal Berkala Ilmu Kesehatan Kulit dan Kelamin Journal Of Widya Medika Junior Jurnal Penyakit Dalam Indonesia
Djaputra, Edith Maria
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Immunology & microbiology Medicine & Pharmacology Public Health

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Short Stature and Motor Development in Children Aged 2-5 Years Old Hilarius, Sancha M; Pangemanan, Lisa; Djaputra, Edith Maria
JOURNAL OF WIDYA MEDIKA JUNIOR Vol 2, No 3 (2020): July
Publisher : FAKULTAS KEDOKTERAN UNIVERSITAS KATOLIK WIDYA MANDALA SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33508/jwmj.v2i3.2642

Abstract

Background: Short stature is defined as a child’s height below < –2 SD based on age. Epidemiologic data shows a high percentage of Indonesian toddlers with short stature. There is fast motoric development in children above two years old. If there is a disruption in motor development, the impact will have a long-term effect on growth and development. Aim: This research aimed to determine the relationship between short stature and motor development in children aged 2-5 years. Method: This study used an observational analytic method with cross-sectional design and consecutive sampling techniques. There were 236 subjects in this study. Height was measured by stadiometer and short stature assessed by the WHO growth Chart, while the motor development was assessed using KPSP (Kuesioner Pra Skrining Perkembangan). ChiSquare test was used to analyze the data (α= 0.05). Results: Relationship between short stature and motor development variables showed a significant weak correlation (P = 0,000; phi = 0,377) in children aged 2-5 years. Conclusion: Short stature has a weak significant relationship with the motor development in children aged 2-5 years old.
The Relationship between the Severity of Atopic Dermatitis and Sleep Quality in Widya Mandala Surabaya Catholic University Teaching Hospital Patients Caecilia Elva; Oenarta, Dave Gerald; Djaputra, Edith Maria
Berkala Ilmu Kesehatan Kulit dan Kelamin Vol. 36 No. 3 (2024): DECEMBER
Publisher : Faculty of Medicine, Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/bikk.V36.3.2024.202-206

Abstract

Background: Atopic dermatitis is a chronic inflammatory skin disease that commonly begins during childhood. Most atopic dermatitis patients may experience symptoms that continue into adulthood. Itching is the characteristic symptom of atopic dermatitis. Over time, patients’ itching can lead to sleep disturbances. In fact, every human being requires sleep to maintain the balance of metabolism, calories, temperature, and immunity. If a person often lacks sleep, the risk of obesity, type 2 diabetes mellitus, hypertension, heart disease, stroke, deterioration of mental health, and premature death will increase. Purpose: To analyze the relationship between the severity of atopic dermatitis and sleep quality in Widya Mandala Surabaya Catholic University (WMSCU) teaching hospital patients. Methods: This is an analytic study with a cross-sectional research design. This study used the purposive sampling technique as its sampling method. A dermatologist used the Eczema Area and Severity Index (EASI) instrument, to assess the severity of atopic dermatitis in the samples, and Pittsburgh Sleep Quality Index (PSQI) questionnaire to assess their sleep quality. Result: The Mann-Whitney test showed a p value of 0.348 (p> 0.05). Conclusion: There is no significant relationship between the severity of atopic dermatitis and sleep quality in WMSCU teaching hospital patients.
Analisis Prediktif Mutasi EGFR pada Adenokarsinoma Paru Menggunakan Pendekatan Pembelajaran Mesin Njoto, Edwin Nugroho; Pamungkas, Yuri; Putri, Atina I.W.; Haykal, Muhammad. Najib; Eljatin, Dwinka Syafira; Djaputra, Edith Maria
Jurnal Penyakit Dalam Indonesia
Publisher : UI Scholars Hub

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Abstract

Introduction. Lung adenocarcinoma is a prevalent form of lung cancer, and mutations in the epidermal growth factor receptor (EGFR) gene are known to play a crucial role in its pathogenesis. This study aimed to develop a machine-learning model to predict EGFR mutations in lung adenocarcinoma patients using clinical and radiological features. Methods. A case-control study was conducted using a dataset comprising 160 patients with lung adenocarcinoma. Several machine learning algorithms, including decision tree, linear regression, Naive Bayes, support vector machine, K-nearest neighbor, and random forest, were employed to predict EGFR mutations based on variables such as smoking status, tumor diameter, tumor location, bubble-like appearance on CT-scan, air-bronchogram on CT-scan, and tumor distribution. Results. Most study subjects were over 50 years old (83.75%) and female (53.13%). The analysis results indicated that the random forest model demonstrated the best performance, achieving an accuracy of 83.33%, precision of 86.96%, recall of 80.00%, and an Area Under the Curve (AUC) of 90.0. The Naive Bayes model also performed well, with an accuracy of 85.42%, precision of 82.61%, recall of 86.36%, and an AUC of 91.0. Conclusions. The study highlights the potential of machine learning techniques, particularly random forest and Naive Bayes, in accurately predicting EGFR mutations in lung adenocarcinoma patients based on readily available clinical and radiological features. These findings could contribute to the development of non-invasive, cost-effective, and efficient tools for EGFR mutation detection, ultimately facilitating personalized treatment approaches for lung adenocarcinoma patients.
Co-Authors Caecilia Elva Dwinka Syafira Eljatin Haykal, Muhammad. Najib Hilarius, Sancha M Lisa Pangemanan Njoto, Edwin Nugroho Oenarta, Dave Gerald Pamungkas, Yuri Putri, Atina I.W.