• Title/Summary/Keyword: Discharge injury patient

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Development of Predictive Model for Length of Stay(LOS) in Acute Stroke Patients using Artificial Intelligence (인공지능을 이용한 급성 뇌졸중 환자의 재원일수 예측모형 개발)

  • Choi, Byung Kwan;Ham, Seung Woo;Kim, Chok Hwan;Seo, Jung Sook;Park, Myung Hwa;Kang, Sung-Hong
    • Journal of Digital Convergence
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    • v.16 no.1
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    • pp.231-242
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    • 2018
  • The efficient management of the Length of Stay(LOS) is important in hospital. It is import to reduce medical cost for patients and increase profitability for hospitals. In order to efficiently manage LOS, it is necessary to develop an artificial intelligence-based prediction model that supports hospitals in benchmarking and reduction ways of LOS. In order to develop a predictive model of LOS for acute stroke patients, acute stroke patients were extracted from 2013 and 2014 discharge injury patient data. The data for analysis was classified as 60% for training and 40% for evaluation. In the model development, we used traditional regression technique such as multiple regression analysis method, artificial intelligence technique such as interactive decision tree, neural network technique, and ensemble technique which integrate all. Model evaluation used Root ASE (Absolute error) index. They were 23.7 by multiple regression, 23.7 by interactive decision tree, 22.7 by neural network and 22.7 by esemble technique. As a result of model evaluation, neural network technique which is artificial intelligence technique was found to be superior. Through this, the utility of artificial intelligence has been proved in the development of the prediction LOS model. In the future, it is necessary to continue research on how to utilize artificial intelligence techniques more effectively in the development of LOS prediction model.

A study on the development of severity-adjusted mortality prediction model for discharged patient with acute stroke using machine learning (머신러닝을 이용한 급성 뇌졸중 퇴원 환자의 중증도 보정 사망 예측 모형 개발에 관한 연구)

  • Baek, Seol-Kyung;Park, Jong-Ho;Kang, Sung-Hong;Park, Hye-Jin
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.11
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    • pp.126-136
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    • 2018
  • The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.

The Hospital Life of the Patient with Femoral Neck Fracture (대퇴경부 골절 환자의 입원 생활)

  • Kim, Kyung-Ja;Chi, Sung-Ai
    • Journal of Korean Academy of Nursing Administration
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    • v.2 no.1
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    • pp.35-56
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    • 1996
  • Nowerdays, the increase of traffic accidents and old age population make the Femoral Neck Fracture(FNF) patients increase. By the improvement of education and standard of living the patients demand better medical service than before. This study is designed to give practical help for the FNF patients by observing their hospital life and establish practical nursing strategies for the FNF patients. For these purposes the Ethnographic Participant Observation was adopted. By this study is focused on the hospital life patient's view. For this end, the field study adopted orthopedic ward in the C University Hospital with 400 beds in Seoul. The object patients of the study were twelve patients. The patients experienced five stages : Embarrassment, Conflict, Stability, Independent, and Extension Stage. The findings and prepared nursing strategies are stated as follows. First, in the Embarrassment Stage they suffered embarrassment, anxiety, pain, they could not do ordinary things. The patients who accidental fractures had anxiety from unfamiliar tests and from hospitalization itself. They lamented that they could not ordinary things, and do nothing but obeying the hospital, and endure the pain. They recognized the changed environment and resigned themselves to life in the ward. In this stage, full openness by the nurses is needed. Second, the attribute of the Conflict Stage were conflict, fear, curiosity, belief, reflection. When they sign the consentment form, they experience conflicts about the possibility of complication, fear of recovery from anesthesia, curiosity about the operation procedure, post - operation state, reflection on their past life, and promise to care for their family members after discharge and keep their religious life faithfully. And they accepted the operation depending on God, believing in modern medicine, and the surgeon. Asking for their changed informations, they expected positive results from the operation. In this stage, an empathic attitude by the nurses is needed. Third, the attribute of the Stability Stage were relief, gratitude, difficulty with excretion, and pain. When they awoke from anesthesia, they felt relief because of a the end of the operation, but they experienced extreme pain, difficulty of excretion in bed. They accepted the changed environment and expected recovery. In this stage, support by the nurses is needed. Fourth, the attributes of the Independence Stage were freedom, exercise, nurturing, anxiety, and discomfort. When they ambulated and exercised, they experienced freedom. They showed exhibited weakness of the digestive organs and discomfort hospital's space, structure, and facilities, the delay of medical certificate issue the lack of prompt response by the medical agents. They ate nurturious food and felt anxiety on the end of hospital life and returning to their ordinary life. They showed the independence of overcoming their environment by increasing exercise and expected their discharges. In this stage, respect by the nurses is needed for the patients to, overcome their environment and prepare for their independence. Fifth, the attributes of the Extension Stage were pessimism, isolation, dissatisfaction, and pain. Accompanied injury and old age made their ward life extend to over seven weeks. They exhibited weariness, melancholy, skeptisis, general pessimistic feeling, and desperation caused by their isolated life. They experienced the digestive discomfort caused by the prolonged medication and psycological pain caused by long-time hospitalization. As a, result, their dissatisfaction on the human, physical, and systematic environments had been increased. They acquired critical power and sought for something to do spending their time. They expected vaguely about the returning of their ordinary life. In this stage, counseling is needed by the nurse to overcome positively their psychological, social, and physical problems. The process of the FNF patient's ward life starts from the dependent state, when they are hospitalized, and gradually progresses to self-fulfillment in order to keep independent life. As a result, the FNF patients showed "Response in Challenge" or "Adaptation in Conflict" through their experiences of social, physical, and psychological difficulties.

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The Analysis of Disease Distribution of patients discharged from a general hospital in a farming and fishing village region (일개 종합병원을 이용한 농.어촌지역 퇴원환자의 질병분포에 관한 연구)

  • Yu, Eun-Yeong;Kim, Youl
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.11 no.12
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    • pp.4863-4872
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    • 2010
  • This study examined the disease conditions of patients from a farming and fishing village area. In order to analyze the medical service utilization, the necessary data were obtained from established health and medical care service plans from medical treatment related organizations. The following results were based on the analysis of data from the medical records of 2,365 discharged patients during a six months period from July to December 2009 at a general hospital in an aging farming and fishing village area. Results: The sex of the patients investigated was male 55.3%, female 44.7%, and the most frequent age category at 42.0% was 70 years of age or older. Based on type of hospital admission, 65.5% of patients who were admitted were originally outpatients. Patients were admitted according to the following departments: 49.7% for the department of internal medicine, 16.7% for the department of orthopedics, and 13.8% for the department of neurosurgery. The average number of days hospitalized was 14.8 days. The following ranks the principal diagnosis among patients in this study: S00-T98 18.4%, J00-J99 15.5%, and I00-I99 11.5%. The average number of diagnosis listed per patient was 5.6. There was a statistically significant difference for the following general characteristics according to principal diagnosis list: gender, type of insurance, admission process, and age category distribution had statistically significant differences. Monthly distribution of principal diagnoses were statistically significant difference. There was a statistically significant difference for principal diagnosis lists according to the average number of days admitted and the number of diagnosis. The results of this study showed the types of disease from typical farming and fishing village regions as disease from external injury due to the work environment of farming and fishing village regions and excessive labor throughout the year, respiratory disease, and various chronic disease from aging.