緩和ケアと医学

抽象的な

Improvement of Patient Stratification in Palliative Care

Sarah Ziane

An essential component of computer science research, artificial intelligence model building for synthetic data creation to enhance Machine Learning (ML) methodology is presently being used to allied medical domains, such as Systems Medicine and Medical Informatics. For more than ten years, medical researchers have been motivated by the notion of individualized decision-making assistance based on patient data, but there are still significant constraints due to the overall unavailability and sparsity of data. Contrast this with the technology that is now being used, which enables us to create and analyse patient data in a variety of formats, including tabular data on health records, medical photographs, genetic data, and even audio and video. The creation of synthetic tabular data based on real-world data is one way to get around these data constraints in respect to medical records. Consequently, with more pertinent patient data available, ML-assisted decision-support may be understood more easily. A number of cutting-edge ML algorithms create and draw choices from such data, from a methodological perspective. However, there are still significant problems that prevent a widespread practical application in actual clinical situations. As a challenging primary example of highly customised, hardly available patient information, we will provide for the first time insights into current viewpoints and prospective consequences of adopting synthetic data creation in palliative care screening in this study. Together, the reader will discover some basic ideas and workable solutions that are pertinent to creating and utilising synthetic data for ML-based screens in palliative care and other fields.