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1.
BMC Palliat Care ; 19(1): 160, 2020 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-33059636

RESUMO

BACKGROUND: Most terminally ill cancer patients prefer to die at home, but a majority die in institutional settings. Research questions about this discrepancy have not been fully answered. This study applies artificial intelligence and machine learning techniques to explore the complex network of factors and the cause-effect relationships affecting the place of death, with the ultimate aim of developing policies favouring home-based end-of-life care. METHODS: A data mining algorithm and a causal probabilistic model for data analysis were developed with information derived from expert knowledge that was merged with data from 116 deceased cancer patients in southern Switzerland. This data set was obtained via a retrospective clinical chart review. RESULTS: Dependencies of disease and treatment-related decisions demonstrate an influence on the place of death of 13%. Anticancer treatment in advanced disease prevents or delays communication about the end of life between oncologists, patients and families. Unknown preferences for the place of death represent a great barrier to a home death. A further barrier is the limited availability of family caregivers for terminal home care. The family's preference for the last place of care has a high impact on the place of death of 51%, while the influence of the patient's preference is low, at 14%. Approximately one-third of family systems can be empowered by health care professionals to provide home care through open end-of-life communication and good symptom management. Such intervention has an influence on the place of death of 17%. If families express a convincing preference for home care, the involvement of a specialist palliative home care service can increase the probability of home deaths by 24%. CONCLUSION: Concerning death at home, open communication about death and dying is essential. Furthermore, for the patient preference for home care to be respected, the family's decision for the last place of care seems to be key. The early initiation of family-centred palliative care and the provision of specialist palliative home care for patients who wish to die at home are suggested.


Assuntos
Atitude Frente a Morte , Neoplasias/mortalidade , Neoplasias/psicologia , Assistência Terminal/métodos , Assistência Terminal/psicologia , Doente Terminal/psicologia , Adulto , Idoso , Idoso de 80 Anos ou mais , Inteligência Artificial , Interpretação Estatística de Dados , Mineração de Dados , Feminino , Serviços de Assistência Domiciliar , Hospitalização , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Satisfação do Paciente , Probabilidade , Suíça/epidemiologia , Adulto Jovem
2.
IEEE Trans Vis Comput Graph ; 17(12): 1757-64, 2011 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-22034292

RESUMO

As microscopes have a very shallow depth of field, Z-stacks (i.e. sets of images shot at different focal planes) are often acquired to fully capture a thick sample. Such stacks are viewed by users by navigating them through the mouse wheel. We propose a new technique of visualizing 3D point, line or area markers in such focus stacks, by displaying them with a depth-dependent defocus, simulating the microscope's optics; this leverages on the microscopists' ability to continuously twiddle focus, while implicitly performing a shape-from-focus reconstruction of the 3D structure of the sample. User studies confirm that the approach is effective, and can complement more traditional techniques such as color-based cues. We provide two implementations, one of which computes defocus in real time on the GPU, and examples of their application.


Assuntos
Gráficos por Computador , Microscopia/estatística & dados numéricos , Animais , Sistemas Computacionais , Percepção de Profundidade , Humanos , Imageamento Tridimensional/estatística & dados numéricos , Camundongos , Sifonápteros/anatomia & histologia , Interface Usuário-Computador
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