• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.137-147
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• 2015

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.7 no.2
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• pp.161-166
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• 1996

The major goals of this study are to investigate the correlation between the cormorbid symptom and the prognosis of conduct disorder in the adolescents. for this purpose, according to the result of 6-month follow-up of discharged patient who met the criteria of conduct disorder in admission, good-prognosis group(n=37) and poor-prognosis group(n=36) were selected. Authors applied Children's Depression Inventory and Trait Anxiety Inventory, Conners Parenting Rating Scale. Yale Children's Inventory to two groups. The results are summarized as follows : 1) Using CDI, the mean scores of poor-prognosis group were significantly higher compared with those of good prognosis group. 2) Using TAI, CPRS, YCI, the mean scores of poor-prognosis group were insignificantly higher compared with those of good prognosis. 3) The limitation of our study is that number of subjects is small, definition of prognosis is ambiguous, and period of 6 month follow-up is short.

• Journal of KIISE
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• v.45 no.1
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• pp.61-68
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• 2018

The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.

• Journal of the Korean Child Neurology Society
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• v.26 no.4
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• pp.240-245
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• 2018

Purpose: Early prediction of prognosis of children resuscitated from cardiac arrest is a major challenge. We investigated the utility of electroencephalography (EEG) and laboratory studies for predicting of neurologic outcome in children resuscitated from cardiac arrest. Methods: We retrospectively analyzed medical records of patients who were resuscitated from cardiac arrest from 2006 to 2015 at the Gil Medical Center. Patients aged one month to 18 years were included. EEG analysis included background scoring, reactivity and seizure burden. EEG background was classified score 0 (normal/organized), score 1 (slow and disorganized), score 2 (discontinuous or burst suppression), and score 3 (suppressed and featureless). Neurologic outcome was evaluated by Pediatric Cerebral Performance Category (PCPC) at least 6 months after cardiac arrest. Results: Total 26 patients were evaluated. Nine patients showed good neurologic outcome (PCPC 1, 2, 3) and 17 patients showed poor neurologic outcome (PCPC 4, 5, 6). Patients of poor neurologic outcome group showed EEG background score 3 in 88.2%, whereas 44.4% in patients of good neurologic outcome group (P=0.028). Electrographic ictal discharges except non-convulsive status epilepticus were presented in 44.4% of good neurologic outcome group and 5.9% of poor neurologic outcome group (P=0.034). Ammonia and lactate levels were higher and pH levels were lower in poor outcome group than good neurologic outcome group. Conclusion: Suppressed and featureless EEG background is associated with poor neurologic outcome and electrographic seizures are associated with good neurologic outcome.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.7 no.2
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• pp.153-160
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• 1996

The purpose of this study is to find out the factors that affect the prognosis of conduct disorder in the adolescents. According to the nature or behavioral problems during 6 months after discharge, the good prognosis group(N=37) and the poor prognosis group(N= 36) were selected and scores of Youth Self Report(YSR), MMPI, KWIS were compared between both groups. The following results were obtained. 1) In family environmental factors, no significant difference was found between both groups. 2) In YSL total problem score, score of externalizing syndrome and score of delinquent behavior were significantly higher in the poor prognosis group. 3) In MMPI, no significant difference was found between both groups. 4) In KWIS, total 1.0. did not show significant differences between both groups. Our hypothesis that the prognosis of conduct disorder in adolescent is poorer in cases with higher quantities of problematic behaviors is certified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.297-299
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• 2022

Malignant glioma has a poor prognosis with the reported median survival of between 6 months to 14 months. Thus, it is crucial to predict the accurate survival of patients with malignant glioma. In this paper, we propose a convolutional neural network to predict the overall survival and age of the patients. A total of four MRI modalities, T1, T1-contrast enhanced, T2, and fluid-attenuated inversion recovery, which effectively capture spatial characteristics of malignant glioma, were used as input images. Age is an important factor impacting the overall survival, thus incorporating it into the model will thereby improve the performance of the proposed model. Our model successfully predicted overall survival and age of the patients with pearson correlation coefficients of 0.1748 and 0.3056, respectively.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.2
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• pp.126-135
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• 2018

This study examined whether the difference in the middle cerebral artery (MCA) velocities can predict the prognosis of stroke and whether the prognostic impact differs among stroke subtypes. Transient ischemic attack (TIA) or acute ischemic stroke patients, who underwent a routine evaluation and transcranial Doppler (TCD), were included in this study. The MCA asymmetry index was calculated using the relative percentage difference in the mean flow velocity (MFV) between the left and right MCA: (|RMCA MFV-LMCA MFV|/mean MCA MFV)${\times}100$. The stroke subtypes were determined using the TOAST classification. Poor functional outcomes were defined as a mRS score ${\geq}3$ at 3 months after the onset of stroke. A total of 988 patients were included, of whom 157 (15.9%) had a poor functional outcome. Multivariable analysis showed that only the MCA asymmetry index was independently associated with a poor functional outcome. ROC curve analysis showed that adding the MCA asymmetry index to the prediction model improved the discrimination of a poor functional outcome from acute ischemic stroke (from 88.6% [95% CI, 85.2~91.9] to 89.2% [95% CI, 85.9~92.5]). The MCA asymmetry index has an independent prognostic value for predicting a poor short-term functional outcome after an acute cerebral infarction. Therefore, TCD may be useful for predicting a poor functional outcome in patients with acute ischemic stroke.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.383-386
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• 2019

4 차산업혁명의 발달은 전 세계가 건강한 삶에 관련된 스마트시티 및 맞춤형 치료에 큰 관심을 갖게 하였고, 특히 기계학습 기술은 암을 극복하기 위한 유전체 기반의 정밀 의학 연구에 널리 활용되고 있어 암환자의 예후 예측 및 예후에 따른 맞춤형 치료 전략 수립 등을 가능케하였다. 하지만 암 예후 예측 연구에 주로 사용되는 유전자 발현량 데이터는 약 17,000 개의 유전자를 갖는 반면에 샘플의 수가 200 여개 밖에 없는 문제를 안고 있어, 예후 예측을 위한 신경망 모델의 일반화를 어렵게 한다. 이러한 문제를 해결하기 위해 본 연구에서는 고차원의 유전자 발현량 데이터를 신경망 모델이 효과적으로 학습할 수 있도록 2D 이미지로 표현하는 기법을 제안한다. 길이 17,000 인 1 차원 유전자 벡터를 64×64 크기의 2 차원 이미지로 사상하여 입력크기를 압축하였다. 2 차원 평면 상의 유전자 좌표를 구하기 위해 유전자 네트워크 데이터와 Node2Vec 이 활용되었고, 이미지 기반의 암 예후 예측을 수행하기 위해 합성곱 신경망 모델을 사용하였다. 제안하는 기법을 정확하게 평가하기 위해 이중 교차 검증 및 무작위 탐색 기법으로 모델 선택 및 평가 작업을 수행하였고, 그 결과로 베이스라인 모델인 고차원의 유전자 벡터를 입력 받는 다층 퍼셉트론 모델보다 더 높은 예측 정확도를 보여주는 것을 확인하였다.

• Journal of Chest Surgery
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• v.36 no.10
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• pp.754-758
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• 2003

Backgroun: Thymectomy was known as an effective treatment modality of myasthenia gravis. In this paper, we analyzed the result of thymectomy and the factors affecting the postoperative system improvement. Material and Method: We analyzed the medical records of 50 patients who received the thymectomy for myasthenia gravis from January 1997 to December 2001. Result: 39 patients showed sympton improvement. The effect of thymectomy as a treatment is 78%. There was no statistically significant correlation between postoperative improvement and Sex, Age, the Weight of thymic tissue, preoperative symptom duration, and preoperative mestinon dosage. However, the thymic pathology and low grade preoperative symptoms were affecting the postoperative prognosis. Conclusion: Thymic hyperplasia showed good prognosis compared to thymoma. Low grade preoperative symptoms (Group I or IIA) also showed good prognosis. So, early thymectomy is recommendable for the good treatment results of myasthenia gravis.

• Journal of Gastric Cancer
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• v.7 no.3
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• pp.124-131
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• 2007

Purpose: This study was conducted to identify prognostic factors in gastric cancer without lymph node metastasis and to specifiy which prognostic factors can be available in detail according to the depth of invasion. Materials and Methods: This retrospective study was based on the medial records of 268 gastric cancer patients who received resectional therapy from 1990 to 1999. The patients who revealed pT2NOMO, pT3NOMO, pT4NOMO on postoperative pathologic reports were enrolled. The survival rate was analyzed according to clinicopathologic and therapeutic factors. Results: According to the depth of invasion, the number of patients with pT2a, pT2b, pT3 and pT4 were 86 (32.1%), 56 (20.9%), 108 (40.3%), and 18 (6.7%) respectively. Age, depth of invasion, histological type, Borrmann type, and Lauren classification were statistically significant in the univariate analysis, and the age, the depth of invasion, and Lauren classification were independent prognostic factors identified by multivariate analysis. On multivariate analysis of subgroups according to the depth of invasion, the independent prognostic factors were age, Borrmann type, and Lauren classification in pT2, and age, Lauren classification, and vascular invasion in pT3. The prognostic factors of pT4 patients could not be analyzed due to limited sample size. Conclusion: In advanced gastric cancer patients without lymph node metastasis, age, the depth of invasion, and Lauren classification should be checked to predict prognosis. In patients with pT2 lesion among the above patients, the Borrmann type should be added in check-list.