• The Korean Nurse
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• v.36 no.5
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• pp.45-62
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• 1998

The study was conducted from November 1995 to May 1996 at the one general hospital in Seoul. The total subjects of this study were 412 patients who have the experience of fall accident, among them 31 was who have fallen during hospitalization and 381 was who visited emergency room and out patient clinic. The purposes of this study were to determine the characteristics, risk factors and results of fall accident and to suggest the nursing strategies for prevention of fall. Data were collected by reviewing the medical records and interviewing with the fallers and their family members. For data analysis, spss/pc+ program was utilized for descriptive statistics, adjusted standardized $X^2$-test. The results of this study were as follows: 1) Total subjects were 412 fallers, of which 245(59.5%) were men and 167(40.5%) were women. Age were 0-14 years 79(19.2%), 15-44 years 125(30.4%), 45-64 years 104(25.2%), over 65 years 104(25.2%). 2) There was significant association between age and the sexes ($X^2$=39.17, P=0.00). 3) There was significant association between age and history of falls ($X^2$=44.41. P= .00). And history of falls in the elderly was significantly associated with falls. 4) There was significant association with age and medical diagnosis ($X^2$=140.66, P= .00), chief medical diagnosis were hypertension(34), diabetis mellitus(22), arthritis(11), stroke(8), fracture(7), pulmonary tuberculosis(6), dementia(5) and cataract(5), 5) There was significant association between age and intrinsic factors: cognitive impairment, mobility impairment, insomnia, emotional problems, urinary difficulty, visual impairments, hearing impairments, use of drugs (sedatives , antihypertensive drugs, diuretics, antidepressants) (P < 0.05). But there was no significant association between age and dizziness ($X^2$=2.87, P=.41). 6) 15.3% of total fallers were drunken state when they were fallen. 7) Environmental factors of fall accident were unusual posture (50.9%), slips(35.2%), trips (9.5%) and collision(4.4%). 8) Most of falls occurred during the day time, peak frequencies of falls occurred from 1pm to 6pm and 7am to 12am. 9) The places of fall accident were roads(22.6%), house-stairs 06.7%), rooms, floors, kitchen (11.2%), the roof-top, veranda, windows(10.9%), hospital(7.5%), ice or snowy ways(5.8%), bathroom(4.9%), playground, park(4.9%), subway-stairs(4.4%) and public-bathrooms (2.2%). 10) Activities at the time of fall accident were walking(37.6%), turning around or reaching for something(20.9%), going up or down stairs09.2%), exereise, working07.4%), up or down from a bed(2.7%), using wheelchair or walking aids, standing up or down from a chair(2.2%) and standing still(2.2%). 11) Anatomical locations of injuries by falls were head, face, neck(31.3%), lower extremities (29.9%). upper extremities(20.6%), spine, thorax, abdomen or pelvic contents(l1.4%) and unspecified(2.9%). 12) Types of injures were fracture(47.6%), bruises03.8%), laceration (13.3%), sprains(9.0%), headache(6.6%), abrasions(2.9%), intracranial hemorrage(2.4%) and burns(0.5%). 13) 41.5% of the fallers were hospitalized and average of hospitalization was 22.3 days. 14) The six fallers(1.46%) died from fall injuries. The two fallers died from intracranial hemorrage and the four fallers died of secondary infection; pneumonia(2), sepsis(1) and cell lulitis(1). It is suggested that 1) Further study is needed with larger sample size to identify the fall risk factors. 2) After the fall accident, comprehensive nursing care and regular physical exercise should be emphasized for the elderly person. 3) Safety education and safety facilities of the public place and home is necessary for fall prevention.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.