• Acute and Critical Care
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• v.33 no.4
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• pp.216-221
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• 2018

Background: In this study, we analyze the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS) 3, and Mortality Probability Model $(MPM)_0$ III in order to determine which system best implements data related to the severity of medical intensive care unit (ICU) patients. Methods: The present study was a retrospective investigation analyzing the discrimination and calibration of APACHE II, APACHE IV, SAPS 3, and $MPM_0$ III when used to evaluate medical ICU patients. Data were collected for 788 patients admitted to the ICU from January 1, 2015 to December 31, 2015. All patients were aged 18 years or older with ICU stays of at least 24 hours. The discrimination abilities of the three systems were evaluated using c-statistics, while calibration was evaluated by the Hosmer-Lemeshow test. A severity correction model was created using logistics regression analysis. Results: For the APACHE IV, SAPS 3, $MPM_0$ III, and APACHE II systems, the area under the receiver operating characteristic curves was 0.745 for APACHE IV, resulting in the highest discrimination among all four scoring systems. The value was 0.729 for APACHE II, 0.700 for SAP 3, and 0.670 for $MPM_0$ III. All severity scoring systems showed good calibrations: APACHE II (chi-square, 12.540; P=0.129), APACHE IV (chi-square, 6.959; P=0.541), SAPS 3 (chi-square, 9.290; P=0.318), and $MPM_0$ III (chi-square, 11.128; P=0.133). Conclusions: APACHE IV provided the best discrimination and calibration abilities and was useful for quality assessment and predicting mortality in medical ICU patients.

• Tuberculosis and Respiratory Diseases
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• v.50 no.2
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• pp.236-244
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• 2001

Background : Most current research using prognostic scoring systems in critically ill patients have focused on prediction using the first intensive care unit (ICU) day data or daily updated data. Usually the mean ICU length of stay in Korea is longer than in the western world. Consequently, a more cost-effective and practical prognostic parameter is required. The principal aim of this study was to assess the prognostic value of the seventh day(7th day : the average mean ICU length of stay) APACHE III score in a medical intensive care unit. Methods : 241 medical ICU patients from July 1997 to April 1998 were enrolled. The 1st and 7th scores were measured by using the APACHE III scoring system and compared between survivors and non-survivors. Logistic regression analysis was performed to determine the relationship between the $1^{st}$ and $7^{th}$ APACHE III scores and the mortality risk. Results : 1 )The mean length of stay in the ICU was $10.3{\pm}13.8$ days. 2)The mean $1^{st}$ and $7^{th}$ day APACHE III scores were $59.7{\pm}30.9$ and $37.9{\pm}27.7$. 3) The mean $1^{st}$ day APACHE III score was significantly lower in survivors than in non- survivors($49.9{\pm}23.8$ vs $86.3{\pm}32.3$, P<0.0001). 4)The mean $7^{th}$ day APACHE III score was significantly lower in survivors than in non- survivors($30.1{\pm}18.5$ vs $80.1{\pm}30.4$, P<0.0001). 5)The odds ratios among the $1^{st}$ and $7^{th}$ day APACHE III scores and the mortality rate were 1.0507 and 1.0779 respectively. Conclusion : These results suggest that the seventh day APACHE III score is as useful in predicting the outcome as is such like the first day APACHE III score. Therefore, in comparison to the daily APACHE III score, measuring the $1^{st}$ and $7^{th}$ day APACHE III scores are also useful for predicting the prognosis of critically ill patients in terms of cost-effectiveness. It is suggested that the $7^{th}$ day APACHE III score is useful for predicting the clinical outcome.

• Korean Journal of Adult Nursing
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• v.17 no.5
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• pp.762-771
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• 2005

Purpose: The purpose of this study was to identify characteristics of patients who were recipients of decision-making DNR, to describe the situations of DNR, and to analyze the APACHE III and MOF scores. Method: Data collection was conducted through reviews of medical records of 51 patients and through interviews with families of patients who were decision-makers for DNR at C university K Hospital located in Seoul from April to September 2002. Results: The men's APACHE III and MOF scores were higher than the women's and the non cancer patients were higher than cancer patients. Some 80.4% of DNR orders was by communication, while 11.8% of consents were written. Each of APACHE III and MOF scores of patients in the intensive care unit was higher than the patients in general ward at both points of admission and decision-making of DNR. APACHE III and MOF scores positively correlated statistically with each other. Conclusions: The findings of this study suggest that APACHE III and MOF scores be useful for decision-making of DNR as a tool measuring severity.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.9
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• pp.207-212
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• 2020

The amount of data has increased significantly due to advances in technology, and various big data processing platforms are emerging, to handle it. Among them, the most widely used platform is Hadoop developed by the Apache Software Foundation, and Hadoop is also used in the IoT field. However, the existing Hadoop-based IoT sensor data collection and analysis environment has a problem of overloading the name node due to HDFS' Small File, which is Hadoop's core project, and it is impossible to update or delete the imported data. This paper uses Apache Kudu and Impala to design Lambda Architecture. The proposed Architecture classifies IoT sensor data into Cold-Data and Hot-Data, stores it in storage according to each personality, and uses Batch-View created through Batch and Real-time View generated through Apache Kudu and Impala to solve problems in the existing Hadoop-based IoT sensor data collection analysis environment and shorten the time users access to the analyzed data.

• International Journal of Contents
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• v.14 no.1
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• pp.28-33
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• 2018

In this paper, we design and implement a distributed, moving objects management system for processing locations and sensor data from smart black boxes. The proposed system is designed and implemented based on Apache Kafka, Apache Spark & Spark Streaming, Hbase, HDFS. Apache Kafka is used to collect the data from smart black boxes and queries from users. Received location data from smart black boxes and queries from users becomes input of Apache Spark Streaming. Apache Spark Streaming preprocesses the input data for indexing. Recent location data and indexes are stored in-memory managed by Apache Spark. Old data and indexes are flushed into HBase later. We perform experiments to show the throughput of the index manager. Finally, we describe the implementation detail in Scala function level.

• Journal of Preventive Medicine and Public Health
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• v.38 no.3
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• pp.276-282
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• 2005

Objectives : To evaluate the predictive validity of three scoring systems; the acute physiology and chronic health evaluation(APACHE) III, simplified acute physiology score(SAPS) II, and mortality probability model(MPM) II systems in critically ill patients. Methods : A concurrent and retrospective study conducted by collecting data on consecutive patients admitted to the intensive care unit(ICU) including surgical, medical and coronary care unit between January 1, 2004, and March 31, 2004. Data were collected on 348 patients consecutively admitted to the ICU(aged 16 years or older, no transfer, ICU stay at least 8 hours). Three models were analyzed using logistic regression. Discrimination was assessed using receiver operating characteristic(ROC) curves, sensitivity, specificity, and correct classification rate. Calibration was assessed using the Lemeshow-Hosmer goodness of fit H-statistic. Results : For the APACHE III, SAPS II and MPM II systems, the area under the receiver operating characterist ic(ROC) curves were 0.981, 0.978, and 0.941 respectively. With a predicted risk of 0.5, the sensitivities for the APACHE III, SAPS II, and MPM II systems were 81.1, 79.2 and 71.7%, the specificities 98.3, 98.6, and 98.3%, and the correct classification rates 95.7, 95.7, and 94.3%, respectively. The SAPS II and APACHE III systems showed good calibrations(chi-squared H=2.5838 p=0.9577 for SAPS II, and chi-squared H=4.3761 p=0.8217 for APACHE III). Conclusions : The APACHE III and SAPS II systems have excellent powers of mortality prediction, and calibration, and can be useful tools for the quality assessment of intensive care units(ICUs).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.208-209
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• 2011

In this paper, we introduce a Naive Bayes learning algorithm for learning and reasoning in Map-Reduce model based environment. For this purpose, we use Apache Mahout to execute Distributed Naive Bayes on University of California, Irvine (UCI) benchmark data sets. From the experimental results, we see that Apache Mahout' s Distributed Naive Bayes algorithm is comparable to WEKA' s Naive Bayes algorithm in terms of performance. These results indicates that in the future Big Data environment, Map-Reduce model based systems such as Apache Mahout can be promising for machine learning usage.

• Korean Journal of Adult Nursing
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• v.14 no.1
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• pp.93-101
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• 2002

Using the APCHE III tool, this study was about the factors related to the death of ICU-patients. From 1999. 12. 1 to 2000. 9. 30, the 284 patients admitted to ICU at P university who were over 15 years of age were selected for the subjets. The data was analyzed through SPSS WIN program for frequency, percentile, $x^2$-test, t-test and logistic regression. The results are summarized as follows: 1) Of the 284 patients, 88died. The mortality is 31.0 percent. The average APACHE III point was $48.62{\pm}32.32$. The average point of non-survivors was higher than that of survivors. 2) There are the significant difference between APACHE III marks and mortality. The mortality rate were over 50 percent 60 points of the mark. When the marks were over 100 points, the mortality were over 90 percent. Below 40 points, the mortality was below 10 percent. Among the variables in the APACHE III, the most significant variables in explaining death were neurologic abnormalities, pulse, $PaO_2$/$AaDO_2$, creatinine, sodium, glucose, chronic health state and age. According to the variables, the models explained the 42.43 percent of the variance in patient's death. In conclusion, the APACHE III tool can be used to predict the progress of ICU patients, and can also be used for the selection of patients for ICU admission/discharge criteria.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.52-54
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• 2017

실시간으로 빠르게 발생하는 대용량 데이터를 다루기 위해 Apache Storm, Apache Spark 등 실시간 데이터 스트림 처리 기술에 대한 연구가 활발하다. 대부분의 실시간 처리 기술들은 단독으로 사용하기에 어려움이 있으며, 데이터 스트림의 입출력을 위해 메시징 시스템과 함께 사용하는 것이 일반적이다. Apache Kafka는 대표적인 분산 메시징 시스템으로써, 실시간으로 발생하는 대용량의 로그 데이터를 전달하는데 특화된 시스템이다. 현재 Kafka를 위한 다양한 성능 모니터링 도구들이 존재한다. 이러한 모니터링 도구들은 Kafka에서 처리되는 데이터의 양 이외에도 유입 데이터의 크기, 수집 속도, 처리 속도 등 다양한 데이터들을 관찰할 수 있다. 본 논문은 Kafka에서 제공하는 도구와 오픈 소스로 제공되는 여러 개의 도구들을 비교하여, 향후 Kafka의 로드 쉐딩에 대한 연구에 적용할 수 있는 최적의 모니터링 도구를 선별하고자 한다.

• 한국IT서비스학회:학술대회논문집
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• 2010.05a
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• pp.525-528
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• 2010

웹 서비스에 대한 증가하는 데이터 수요를 감당하기 위해서 많은 웹 서버들이 이용되고 있다. 현재의 웹 서비스 분야에서 클러스터와 대용량 데이터 처리의 중요성이 높아지고 있다. 또한 웹 서버의 설정이 서비스 제공자의 필요에 따라 유기적으로 변경이 가능해야한다. 하지만 현재 서비스 개발자들은 UML 등의 언어를 이용해서 비즈니스 프로세스를 디자인한다. 하지만 빠르게 변화하는 비즈니스 프로세스를 웹 서버에 적용하는 보편화된 방법은 존재하지 않는다. 본 논문에서는 웹 서버 시장에서 쓰이고 있는 Apache 서버와 웹서버의 실질적인 동작을 묘사하는 FMC (Fundamental Modeling Concepts)의 Apache Modeling Language를 이용해서 Apache 웹 서버에 대한 설정 작업을 수행하는 방법에 대해 제안한다.