• Journal of Digestive Cancer Research
• /
• 제5권1호
• /
• pp.5-15
• /
• 2017

Hepatocellular carcinoma (HCC) is the sixth most common cancer and second leading cause of cancer-related death in the world. The aggressive but not always predictable pattern of HCC causes the limited treatment option and poorer outcome. Many researches had already proven the heterogeneity of HCC is one of the major challenges for treatment option and prognosis prediction. Molecular subtyping of HCC and selection of patient based on molecular profile can provide the optimization in the treatment and prognosis prediction. In this review, we have tried to summarize the molecular classification of HCC proposed by different valuable researches presented in the logistic way.

• Genomics & Informatics
• /
• 제16권4호
• /
• pp.32.1-32.7
• /
• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

• 정보과학회 논문지
• /
• 제45권1호
• /
• pp.61-68
• /
• 2018

암환자의 예후 예측에 기여하는 유전자를 찾는 것은 환자에게 보다 적합한 치료를 제공하기 위한 도전 과제 중 하나이다. 예후 유전자를 찾기 위해 유전자 발현 데이터를 이용한 분류 모델 개발 연구가 많이 이루어지고 있다. 하지만 암의 이질성으로 인해 예후 예측의 정확도 향상에 한계가 있다는 문제가 있다. 본 논문에서는 유방암을 비롯한 6개의 암에 대한 암환자의 마이크로어레이 데이터와 생물학적 네트워크 데이터를 이용하여 페이지랭크 알고리즘을 통해 예후 유전자들을 식별하고, K-Nearest Neighbor 알고리즘을 사용하여 암 환자의 예후를 예측하는 모델을 제안한다. 그리고 페이지랭크를 사용하기 전에 K-Means 클러스터링으로 유전자 발현 패턴이 비슷한 샘플들을 나누어 이질성을 극복하고자 한다. 본 논문에서 제안한 방법은 기존의 유전자 바이오마커를 찾는 알고리즘보다 높은 예측 정확도를 보여 주었으며, GO 검증을 통해 클러스터에 특이적인 생물학적 기능을 확인하였다.

• 한국컴퓨터정보학회논문지
• /
• 제20권2호
• /
• pp.137-147
• /
• 2015

본 논문에서는 유전자 네트워크를 기반으로 유방암 환자의 예후를 예측하는 알고리듬을 제안한다. 유방암 환자의 마이크로어레이 데이터와 PPI(Protein-protein interaction)데이터를 이용하여 알고리듬의 분류자로 사용될 예후 특이 네트워크(Prognosis specific gene network)를 추출한다. PPI에 속한 모든 유전자 네트워크에 대하여 각각의 네트워크가 예후 좋음과 나쁨을 잘 구분하는지에 대한 점수를 피어슨 상관계수(Pearson's correlation coefficient)와 마이크로어레이 데이터를 이용하여 계산한다. 이들 중 가장 예후에 유의한 네트워크를 식별하고, 이 네트워크를 분류자로 사용하여 에스트로겐 수용체 음성 유방암 환자의 예후를 분류 분석 한다. 본 연구와 기존 연구의 알고리듬 정확도를 비교 분석 하기 위하여 독립 실험을 진행하고, 본 연구에서 제안된 알고리듬의 성능이 더 우수함을 보인다. 또한, Gene Ontology 데이터베이스를 활용하여 식별된 예후 특이 네트워크를 기능적으로 검증 한다.

• 한국자동차공학회논문집
• /
• 제18권1호
• /
• pp.31-36
• /
• 2010

Collecting all failures during life cycle of vehicle is not easy way because its life cycle is normally over 10 years. Warranty period can help gathering failures data because most customers try to repair its failures during warranty period even though small failures. This warranty data, which means failures during warranty period, can be a good resource to predict initial reliability and permanence reliability. However uncertainty regarding reliability prediction remains because this data is censored. University of Wuppertal and major auto supplier developed the reliability prognosis model considering censored data and this model introduce to predict reliability estimate further "failure candidate". This paper predicts reliability of telecommunications system in vehicle using the model and describes data structure for reliability prediction.

• 한국컴퓨터정보학회논문지
• /
• 제20권12호
• /
• pp.91-99
• /
• 2015

In this paper, we introduce the application of several currently-representative methods for mineral resources potential assessment on Geographic information system(hereinafter referred to as GIS), and combined with mineral resources potential assessment performed in China and with lead-zinc deposits taken as an example, summarized and divided minerals prediction and assessment models; on this basis, this paper presented the process of metallogenic prognosis based on MRAS platform, and made a simple analysis on existing problems.

• Journal of Hospice and Palliative Care
• /
• 제26권1호
• /
• pp.1-6
• /
• 2023

This paper aimed to summarize the current situation of prognostication for patients with an expected survival of weeks or months, and to clarify future research priorities. Prognostic information is essential for patients, their families, and medical professionals to make end-of-life decisions. The clinician's prediction of survival is often used, but this may be inaccurate and optimistic. Many prognostic tools, such as the Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and Prognosis in Palliative Care Study, have been developed and validated to reduce the inaccuracy of the clinician's prediction of survival. To date, there is no consensus on the most appropriate method of comparing tools that use different formats to predict survival. Therefore, the feasibility of using prognostic scales in clinical practice and the information wanted by the end users can determine the appropriate prognostic tool to use. We propose four major themes for further prognostication research: (1) functional prognosis, (2) outcomes of prognostic communication, (3) artificial intelligence, and (4) education for clinicians.

• 한국멀티미디어학회논문지
• /
• 제15권6호
• /
• pp.701-710
• /
• 2012

망막색소변성(RP: Retinitis Pigmentosa)이란 가장 흔한 유전성 망막질환이다. 정상적인 사회활동을 영위하던 사람들이 이 질병으로 시력이 손상되면서 좌절과 고통을 겪는다. 또한 국가적 차원에서 이들의 경제활동이 끊김에 따라 경제활동 인구 감소에 따른 손실 또한 크다고 하겠다. 이에 망막색소변성 질환에 대한 임상 예후 정보를 제공할 수 있는 연구기반이 절실히 요구되고 있다. 본 연구는 망막색소변성 데이터에 대한 패턴 분류를 통해 예후 예측이 가능함을 제안한다. 기존에는 주로 SPSS등을 활용한 통계 처리 결과가 데이터 분석에 적용되었다. 그러나 본 연구에서는 기계학습과 자동 패턴 분류를 실험하였다. SVM(Support Vector Machine)과 여러 다양한 패턴분류기들을 실험을 위해 사용하였다. 제안한 방법은 SVM 분류기에 의하여 RP 데이터가 자동적으로 분류된 결과를 바탕으로 예후 예측이 가능함을 확인하였다.

• Childhood Kidney Diseases
• /
• 제15권2호
• /
• pp.116-124
• /
• 2011

Acute kidney injury (AKI) can result in mortality or progress to chronic kidney disease in hospitalized patients. Although serum creatinine has long been used as the best biomarker for diagnosis of AKI, it has some clinical limitations, especially in children. New biomarkers are needed for early diagnosis, differential diagnosis, and reliable prediction of prognosis in AKI. Up to the present, candidate AKI biomarkers include neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), interleukin-18 (IL-18), livertype fatty acid-binding protein (L-FABP), matrix metalloproteinase-9 (MMP-9), and Nacetyl-$\ss$-D-glucosaminidase (NAG). However, whether these are superior to serum creatinine in the confirmation of diagnosis and prediction of prognosis in AKI is unclear. Further studies are needed for clinical application of these new biomarkers in AKI.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2007년도 추계학술대회논문집
• /
• pp.886-890
• /
• 2007

Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao's method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis.