Multidisciplinary project involving natural language processing and machine learning for clinical language processing

重点是建议自动发现不同病症之间的相似之处，这些相似之处可能导致需要联合治疗的候选疾病。为了超越临床文献中已知的疾病组合，本提案旨在大规模地详尽比较疾病之间的相似性，以确定可能的候选组合。这涉及从医学分类法和百科全书等来源提取有关疾病的信息，并为每种病症及其属性创建表示，以便进行比较。此外，将根据疾病的共同特征（包括症状、治疗和演变）对疾病进行大规模比较，使用识别表征之间相似性的技术。这将有助于发现具有相似特征并可以联合治疗的候选疾病组合。

The focus is on proposing to automatically discover similarities among different morbidities that could lead to candidate illnesses for joint treatment. To go beyond combinations of diseases that may be well known in the clinical literature, this proposal aims to exhaustively compare similarities between diseases at a large scale to identify possible candidate combinations. This involves extracting relevant information about diseases from sources including medical taxonomies and encyclopedias and creating a representation for each of the conditions and their properties that allows for their comparison. In addition, techniques for identifying similarities between representations will be used for large-scale comparison of diseases according to their common characteristics, including symptoms, treatments and evolution. These will enable the discovery of candidate combinations of diseases that have similar characteristics and could have joint treatments.