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281 |
Gani A.Z., Zahra P.K., Soedarsono N., Yunaini L., Auerkari E.I. |
57222626782;57226565453;14049161500;57192911515;10139113000; |
Vitamin D receptor TaqI (rs731236) gene polymorphism in caries patients |
2021 |
Journal of Physics: Conference Series |
1943 |
1 |
012093 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112014710&doi=10.1088%2f1742-6596%2f1943%2f1%2f012093&partnerID=40&md5=5891ab3c26dbd9bd14c0be24643600b3 |
Department of Oral Biology, Faculty of Dentistry, University of Indonesia, Jakarta, Indonesia; Department of Medical Biology, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia |
Gani, A.Z., Department of Oral Biology, Faculty of Dentistry, University of Indonesia, Jakarta, Indonesia; Zahra, P.K., Department of Oral Biology, Faculty of Dentistry, University of Indonesia, Jakarta, Indonesia; Soedarsono, N., Department of Oral Biology, Faculty of Dentistry, University of Indonesia, Jakarta, Indonesia; Yunaini, L., Department of Medical Biology, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia; Auerkari, E.I., Department of Oral Biology, Faculty of Dentistry, University of Indonesia, Jakarta, Indonesia |
Vitamin D receptor (VDR) is included in the type of protein that serves as the biological function regulator of vitamin D. Tooth formation, especially in enamel and dentin calcification, as well as maintaining the balance of phosphate and calcium ions which is an important factor in protecting teeth requires support from vitamin D. The VDR gene will regulate the activity of VDR proteins. Caries is a multifactorial disease in which genetic factors can affect the host susceptibility to caries. Polymorphism in the VDR gene is suspected to affect the host susceptibility to caries through changes in calcium metabolism. This study aims to discover the VDR gene polymorphism and its association with caries patients in Indonesia. 100 DNA samples from 100 blood samples, including 50 dental caries patients and 50 healthy controls, were analyzed using PCR-RFLP technique. PCR products were digested with the TaqI restrictive enzyme, then assessed with statistical analysis using Fisher's exact test and Continuity correction test. In the caries group, there were no samples with CC genotype, 4 samples with CT genotype, and 46 samples with TT genotype. There were also 4 C alleles and 96 T alleles. Polymorphic genotypes and alleles were found higher in the caries group (100% and 96%) than healthy controls (88% and 84%). These results conclude that the polymorphism of VDR TaqI (rs731236) gene was found in patients with dental caries. The distribution of genotypes and allele distributions of VDR TaqI (rs731236) gene between caries and healthy controls significantly differs noticeable (p <0.05). © Published under licence by IOP Publishing Ltd. |
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Biomineralization; Calcium; Polymerase chain reaction; Polymorphism; Proteins; Vitamins; Biological functions; Calcium metabolism; Continuity corrections; Gene polymorphism; Healthy controls; Host susceptibility; Multifactorial disease; Vitamin D receptor; Genes |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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492 |
Maulidina F., Rustam Z., Hartini S., Wibowo V.V.P., Wirasati I., Sadewo W. |
57221906584;26422482100;57211529061;57221911837;57221806240;55014544900; |
Feature optimization using Backward Elimination and Support Vector Machines (SVM) algorithm for diabetes classification |
2021 |
Journal of Physics: Conference Series |
1821 |
1 |
012006 |
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1 |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103897338&doi=10.1088%2f1742-6596%2f1821%2f1%2f012006&partnerID=40&md5=26c4c2f598bd765549c0283978c63185 |
Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Department of Neurosurgery, Faculty of Medicine, University of Indonesia, Dr. Cipto Mangunkusumo National General Hospital, Indonesia |
Maulidina, F., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Rustam, Z., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Hartini, S., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Wibowo, V.V.P., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Wirasati, I., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Sadewo, W., Department of Neurosurgery, Faculty of Medicine, University of Indonesia, Dr. Cipto Mangunkusumo National General Hospital, Indonesia |
Diabetes is a disease that occurs when the blood glucose level is higher than normal and also leads to health problems. Early and accurate diagnosis needs to be carried out on individuals affected by this disease. Furthermore, excellent treatment needs to be provided to prevent worse situations. Some studies have used several machine learning methods to diagnose diabetes. Furthermore, in this study, the Backward Elimination and Support Vector Machine (SVM) algorithm was used to classify the PIMA Indians diabetes dataset. It consisted of 268 diabetic and 500 non-diabetic patients with eight attributes. Backward Elimination is a feature selection method used to remove irrelevant features based on the linear regression model. Using this method, the right features for the model was expected. This method has some advantages which include increasing training time, decreasing complexity and improving performance and accuracy. Therefore, the performance of SVM improved. Based on the experiments, it was discovered that by combining feature selection algorithm (backward elimination) and SVM, the highest accuracy obtained was 85.71% using 90% data training. Therefore, it was concluded that Backward Elimination combined with SVM algorithm is an excellent method to classify diabetes by using the PIMA Indians diabetes dataset. © Published under licence by IOP Publishing Ltd. |
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Classification (of information); Diagnosis; Experimental mineralogy; Feature extraction; Learning systems; Regression analysis; Feature optimizations; Feature selection algorithm; Feature selection methods; Improving performance; Linear regression models; Machine learning methods; Support vector machine algorithm; Support vector machines algorithms; Support vector machines |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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493 |
Wibowo V.V.P., Rustam Z., Hartini S., Maulidina F., Wirasati I., Sadewo W. |
57221911837;26422482100;57211529061;57221906584;57221806240;55014544900; |
Ovarian cancer classification using K-Nearest Neighbor and Support Vector Machine |
2021 |
Journal of Physics: Conference Series |
1821 |
1 |
012007 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103891589&doi=10.1088%2f1742-6596%2f1821%2f1%2f012007&partnerID=40&md5=8b428e7f3e510029f71b6c464cefbd2d |
Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Department of Neurosurgery, Faculty of Medicine, University of Indonesia, Dr. Cipto Mangunkusumo National General Hospital, Indonesia |
Wibowo, V.V.P., Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Rustam, Z., Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Hartini, S., Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Maulidina, F., Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Wirasati, I., Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Sadewo, W., Department of Neurosurgery, Faculty of Medicine, University of Indonesia, Dr. Cipto Mangunkusumo National General Hospital, Indonesia |
Ovarian cancer is one of the common malignancies in women and a known cause of death. This condition occurs when a tumor appears from the growth of abnormal cells in the ovary. It causes about 140.000 deaths out of 225.000 cases annually. Most women with ovarian cancer do not have distinctive signs and symptoms even at the late stage. Therefore, diagnosis at an early stage is necessary because it has a significant impact on the survival rate. Machine learning with various methods can be used in the medical field to classify diseases. Among the many methods, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used and analyzed in this study to classify ovarian cancer. The data used were from Al Islam Bandung Hospital consisting of 203 instances with 130 labeled ovarian cancer and 73 as non-ovarian. The results showed that the KNN produced higher results than SVM with 90.47% of accuracy and 94.11% of F1-score, while SVM produced accuracy and F1-score values of 90.47% and 92.30% respectively. © Published under licence by IOP Publishing Ltd. |
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Diagnosis; Diseases; Motion compensation; Nearest neighbor search; F1 scores; K nearest neighbor (KNN); K-nearest neighbors; Late stage; Medical fields; Ovarian cancers; Survival rate; Support vector machines |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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518 |
Apriadi W., Gani H.S., Prayitno P., Ibrahim N., Wijaya S.K. |
57205292872;57202775842;57222538092;56609777400;6506884322; |
Development of multithread acquisition system for high quality EEG signal measurement |
2021 |
Journal of Physics: Conference Series |
1816 |
1 |
012072 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103129496&doi=10.1088%2f1742-6596%2f1816%2f1%2f012072&partnerID=40&md5=8df825ddfea9b100797241af3b95358e |
Department of Physics, FMIPA Universitas Indonesia, Depok, 16424, Indonesia; Department of Physiology, Medical Faculty, Universitas Indonesia, Jakarta, 10430, Indonesia |
Apriadi, W., Department of Physics, FMIPA Universitas Indonesia, Depok, 16424, Indonesia; Gani, H.S., Department of Physics, FMIPA Universitas Indonesia, Depok, 16424, Indonesia; Prayitno, P., Department of Physics, FMIPA Universitas Indonesia, Depok, 16424, Indonesia; Ibrahim, N., Department of Physiology, Medical Faculty, Universitas Indonesia, Jakarta, 10430, Indonesia; Wijaya, S.K., Department of Physics, FMIPA Universitas Indonesia, Depok, 16424, Indonesia |
This work was concerned on development of the EEG acquisition and EEG signal processing by adding active electrodes and implementing multithread techniques. By using active electrodes, mounting them on the scalp surface would be easier to capture low signals of less than 1µV. The active electrodes were used to reduce noise when transfer signals from the electrode to the acquisition systems which equipped 20 gain. The verification was performed by comparing the active and passive electrodes using NETECH MiniSIM EEG Simulator 330. The advantage of this research was to reduce time delay for EEG signal computation on 32 channels. The acquisition system was based on Raspberry Pi and ADS1299 with multithread signal treatment. Signal filtering was performed into different threads and put all the EEG features into the database. A PC was used to process signal calculation such as processing FFT, signal feature extractions, and signal analysis. These calculations were divided into several functionally independent computations. The signals of each channel were calculated into different threads. The results of this work showed the effectiveness of the multithreaded method for processing large amounts of data (32 channels of 24 bits EEG signal) with low noise levels on the active electrodes. © Published under licence by IOP Publishing Ltd. |
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Electrodes; Acquisition systems; Active electrodes; Eeg acquisitions; EEG signal processing; Large amounts of data; Multithread techniques; Signal treatments; Transfer signals; Biomedical signal processing |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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605 |
Unggul D.B., Abdullah S., Rachman A. |
57221961543;57204563168;57217184320; |
Laterality condition analysis on non-arteritic anterior ischemic optic neuropathy patient in one of the hospital in Jakarta with medical data mining |
2021 |
Journal of Physics: Conference Series |
1725 |
1 |
012096 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100803241&doi=10.1088%2f1742-6596%2f1725%2f1%2f012096&partnerID=40&md5=38c1992b179967890ac62988e4c116ad |
Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Division of Hematology and Medical Oncology, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia |
Unggul, D.B., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Abdullah, S., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Rachman, A., Division of Hematology and Medical Oncology, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia |
Non-arteritic Anterior Ischemic Optic Neuropathy (NAION) is a disease caused by blood shortages in the artery that supplies the optic disc. Risk factors for NAION are hypertension, obesity, diabetes, dislipidemia, smoking, hypercoagulable state, cardiovascular disease, and stroke. NAION can result from unilateral or bilateral conditions. This study will focus on the identification of important factors that could distinguish characteristics between unilateral and bilateral patients. Random forest method is applied to obtain factors that can consistently distinguish characteristic between each laterality condition. Decision trees and the logistic regression method are added to obtain the visualization of the role of each important factors in the form of classification tree and the risk comparison of patients for experiencing a certain laterality condition by using odds ratios. The important factors based on random forest model are onset, fasting blood glucose levels, high density lipoprotein levels, age, two-hour postprandial glucose levels, and low density lipoprotein levels. Based on the odds ratio, advancing age and high density lipoprotein levels will decrease the risk of patients experiencing bilateral condition; on the other hand, the risk of bilateral condition will increase if other important factors are also increased. © 2021 Journal of Physics: Conference Series. |
Decision tree; Laterality; Logistic regression; Random forest |
Blood; Decision trees; Glucose; Lipoproteins; Logistic regression; Medical computing; Random forests; Blood glucose level; Cardio-vascular disease; Classification trees; High density lipoprotein levels; Logistic regression method; Low density lipoproteins; Random forest methods; Random forest modeling; Data mining |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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606 |
Hanifah N., Wiweko B., Bowolaksono A. |
57200075370;43061741400;57205093224; |
FSHR and LHR mRNA expression in granulosa cells of poor responder |
2021 |
Journal of Physics: Conference Series |
1725 |
1 |
012057 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100793552&doi=10.1088%2f1742-6596%2f1725%2f1%2f012057&partnerID=40&md5=498a20aef40d78ffcc6b9fa74da8dec6 |
Department of Biology, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Department of Obstetric and Gynecology, Faculty of Medicine, Universitas Indonesia, Depok, 16424, Indonesia |
Hanifah, N., Department of Biology, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Wiweko, B., Department of Obstetric and Gynecology, Faculty of Medicine, Universitas Indonesia, Depok, 16424, Indonesia; Bowolaksono, A., Department of Biology, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia |
In Vitro Fertilization (IVF) is one of the most commonly used procedures to help pregnancies in couples who have infertility problems. One of the problems of infertility is poor ovarian response and the woman who experiences it is known as poor responder. Poor responders do not have an adequate ovarian response to gonadotropin in ovarian stimulation. The success of fertilization in poor responders tends to be low due to low quantity and is generally followed by low oocyte quality. Gonadotropins consisting of FSH and LH, play a role in follicle development and ovulation. The follicle response in capturing gonadotropins depends on the exact bond between the hormone and its receptor (FSHR and LHR) in the granulosa cells surrounding the oocyte. The purpose of this research is to know the expression level of fshr and lhr mRNA in granulosa cells of poor responders through real-time PCR method which then tested statistically using t-test. Fourteen samples of each poor responders and normal women were used in this research. The results showed insignificant differences between expression level of fshr and lhr mRNA in granulosa cells of poor responders and normal women (p > 0.05). © 2021 Journal of Physics: Conference Series. |
FSHR; LHR; Poor ovarian response; Real-time PCR |
Cytology; Polymerase chain reaction; Expression levels; Follicle development; Granulosa cells; In-vitro; mRNA expression; Real-time PCR method; T-tests; Cells |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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607 |
Lestari D.A., Abdullah S., Rachman A. |
56119337300;57204563168;57217184320; |
Identification of factors associated with hypothyroidism due to radiotherapy in patients with nasopharyngeal cancer (Case study of nasopharyngeal cancer in one of the hospitals in Jakarta) |
2021 |
Journal of Physics: Conference Series |
1725 |
1 |
012027 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100793360&doi=10.1088%2f1742-6596%2f1725%2f1%2f012027&partnerID=40&md5=eb87ed0fd4bc75637854a08478652d08 |
Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Division of Hematology and Medical Oncology, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia |
Lestari, D.A., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Abdullah, S., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Rachman, A., Division of Hematology and Medical Oncology, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia |
Nasopharyngeal cancer is an abnormal cell growth that develops around the nasopharynx. Treatment of nasopharyngeal cancer patients includes chemotherapy or radiotherapy. Both treatments have side effects in patients. In this study, we will focus on hypothyroidism as a side effect of radiotherapy in the treatment of patients with nasopharyngeal cancer. Hypothyroidism is a condition when the thyroid gland is unable to produce enough thyroid hormone. The main goal of this study is to identify the factors associated with hypothyroidism. To achieve this goal, classification tree and logistic regression methods will be used. Classification tree is used to obtain important variables in the classification of subject classes. Then, logistic regression is used to quantify the risk of variables that appear in the classification tree, hypothyroidism risk factors, and hypothyroidism marker factors. Based on the analysis, it was found that the factors associated in this study were variable symptom, physical sign, smoking habits, gender, age, BMI (Body Mass Index), TSH (Thyroid Stimulating Hormone) and fT4 (free thyroxine) hormones, and also all items on Zulewski score, except items delayed ankle reflex and slow movements. These factors associated tended to increase the risk of hypothyroidism, except for the fT4 hormone and BMI. © 2021 Journal of Physics: Conference Series. |
Classification tree; Hypothyroidism; Logistic regression; Marker factors; Risk factors |
Cell proliferation; Chemotherapy; Hormones; Logistic regression; Radiotherapy; Body mass index; Classification trees; Logistic regression method; Nasopharyngeal cancer; Risk factors; Thyroid glands; Thyroid hormones; Thyroid stimulating hormones; Diseases |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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608 |
Lukman P.A., Abdullah S., Rachman A. |
57221968648;57204563168;57217184320; |
Bayesian logistic regression and its application for hypothyroid prediction in post-radiation nasopharyngeal cancer patients |
2021 |
Journal of Physics: Conference Series |
1725 |
1 |
012010 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100754485&doi=10.1088%2f1742-6596%2f1725%2f1%2f012010&partnerID=40&md5=902f22d97f61ad13843e9bbe6a66f42e |
Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Department of Medical Education, Faculty of Medicine, Universitas Indonesia, Depok, 16424, Indonesia |
Lukman, P.A., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Abdullah, S., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Rachman, A., Department of Medical Education, Faculty of Medicine, Universitas Indonesia, Depok, 16424, Indonesia |
Logistic regression models are commonly used to model response variables in the form of categorical variables with several predictor variables. The contribution of the predictor variable to the response variable is expressed through a regression coefficient (β). Therefore, it is necessary to estimate β. This study discusses the estimation of β using the Bayesian method. Bayesian approach utilizes a combination of information from sample data and prior information about the characteristics of the parameters of interest, resulting in the updated information, namely the posterior. Bayesian method thus can overcome the problem if the quality of the sample data does not support observation. Bayesian logistic regression method will be used in analyzing post-radiation nasopharyngeal cancer (NPC) patient data, using measurement on Zulewski's score components. Markov Chain Monte Carlo with Gibbs Sampling were used to obtain the sample from posterior distribution. Convergent estimates were obtained, and the result showed that Zulewski's component scores only were not enough to explain the hypothyroidism in NPC. Additional information is required in order to explain the incidence of hypothyroidism in NPC. © 2021 Journal of Physics: Conference Series. |
Bayesian logistic regression; Gibbs sampling; Logistic regression; Markov chain monte carlo; Nasopharyngeal cancer |
Bayesian networks; Diseases; Hospital data processing; Markov chains; Radiotherapy; Categorical variables; Logistic regression method; Logistic regression models; Markov Chain Monte-Carlo; Nasopharyngeal cancer; Posterior distributions; Regression coefficient; Updated informations; Logistic regression |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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609 |
Amelia V., Siswantining T., Kamelia T. |
57221954910;57193446800;35603752000; |
Prediction model of exacerbations in patients with Chronic Obstructive Pulmonary Disease (COPD) at RSCM |
2021 |
Journal of Physics: Conference Series |
1725 |
1 |
012011 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100735594&doi=10.1088%2f1742-6596%2f1725%2f1%2f012011&partnerID=40&md5=3f0c08db1ff574eac108d1a10d8c68a9 |
Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Division of Pulmonology, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia |
Amelia, V., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Siswantining, T., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Kamelia, T., Division of Pulmonology, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia |
Chronic Obstructive Pulmonary Disease (COPD) is a worldwide health problem. COPD has a tendency for exacerbations. Exacerbations are worsening of acute respiratory symptoms resulting in additional therapy. Exacerbations in COPD increase the risk of death. The objective of this study is to determine the prediction model of exacerbations in patients with COPD based on factors affecting exacerbations in patients with COPD at RSCM (Rumah Sakit Cipto Mangunkusumo). The data used in this study is secondary data from the medical records of patients with COPD in RSCM. The sample was chosen using purposive sampling technique. The samples in this study are 107 patients with COPD. The method used is binary logistic regression analysis. The results of this study indicate that the factors that significantly influence the exacerbations of COPD are breathlessness, history of ICS use, and history of antibiotics use. Appropriate logistic regression model has been obtained. The result indicates that patients with COPD who have breathlessness, have history of ICS use, and have history of antibiotics use are more at risk of exacerbations than those who don't. Accuracy test has been conducted with classification table at cut point 0.5. The prediction model has an accuracy rate of 74.77 %. © 2021 Journal of Physics: Conference Series. |
COPD; Exacerbations; Logistic regression |
Antibiotics; Forecasting; Logistic regression; Predictive analytics; Binary logistic regression; Chronic obstructive pulmonary disease; Logistic Regression modeling; Medical record; Prediction model; Respiratory symptoms; Sampling technique; Secondary datum; Pulmonary diseases |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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621 |
George P.M.A., Abdullah S., Rachman A. |
57221954733;57204563168;57217184320; |
Analysis of Hypothyroidism Development in Post-Radiotherapy Nasopharyngeal Cancer Patients using Survival Trees |
2021 |
Journal of Physics: Conference Series |
1722 |
1 |
012095 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100712595&doi=10.1088%2f1742-6596%2f1722%2f1%2f012095&partnerID=40&md5=b152a0810c9274777e385793bf11e707 |
Department of Mathematics, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Department of Oncology, Faculty of Medicine, Universitas Indonesia, Depok, 16424, Indonesia |
George, P.M.A., Department of Mathematics, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Abdullah, S., Department of Mathematics, Universitas Indonesia, Kampus Baru UI, Depok, 16424, Indonesia; Rachman, A., Department of Oncology, Faculty of Medicine, Universitas Indonesia, Depok, 16424, Indonesia |
Radiotherapy is one of the treatments for nasopharyngeal cancer (NPC). However, this treatment might produce an unfavorable effect on the thyroid gland, which eventually results in less production of thyroid hormone. This is condition is known as hypothyroidism. The development of hypothyroidism in each patient with post-radiative NPC differs according to several factors. This study aims to analyze the rate of development of hypothyroidism in post-radiated NPC patients. This aim is achieved by identifying subgroups of patients with different hazard rates of developing hypothyroidism, and further identify factors explaining hypothyroidism in each subgroup. Data on ninety-seven NPC post-radiation patients taken from one of the hospitals in Jakarta were analyzed. Survival tree with the relative risk tree algorithm was proposed to analyze the data. We identified three subgroups of patients with relatively slow, medium, and fast developing of hypothyroidism. For the slow subgroup, 26% of the patients developed hypothyroidism at 150+ weeks post-radiation, while it only took less than 30 weeks for those in fast-growing subgroup; and 70 until 130 weeks for the medium subgroup. We also found that sweat production and Zulewski's total score were the important factors in explaining the development rate of hypothyroidism. © 2021 Institute of Physics Publishing. All rights reserved. |
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Clustering algorithms; Diseases; Forestry; Information analysis; Radiotherapy; Development rate; Hazard rates; Jakarta; Nasopharyngeal cancer; Relative risks; Thyroid glands; Thyroid hormones; Tree algorithms; Trees (mathematics) |
IOP Publishing Ltd |
17426588 |
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Conference Paper |
Q4 |
210 |
18731 |
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