Deleris Léa A, Sacaleanu Bogdan, Tounsi Lamia
IBM Research - Ireland, Damastown Industrial Estate, Dublin, Ireland.
Stud Health Technol Inform. 2013;192:1158.
Risk modeling in healthcare is both ubiquitous and in its infancy. On the one hand, a significant proportion of medical research focuses on determining the factors that influence the incidence, severity and treatment of diseases, which is a form of risk identification. Those studies typically investigate the micro-level of risk modeling, i.e., the existence of dependences between a reduced set of hypothesized (or demonstrated) risk factors and a focus disease or treatment. On the other hand, the macro-level of risk modeling, i.e., articulating how a large number of such risk factors interact to affect diseases and treatments is not widespread, though essential for medical decision support modeling. By exploiting advances in natural language processing, we believe that information contained in unstructured texts such as medical articles could be extracted to facilitate aggregation into macro-level risk models.
医疗保健中的风险建模既普遍存在又尚处起步阶段。一方面，相当一部分医学研究专注于确定影响疾病发病率、严重程度和治疗的因素，这是一种风险识别形式。这些研究通常调查风险建模的微观层面，即一组简化的假设（或已证实）风险因素与重点疾病或治疗之间的依存关系。另一方面，风险建模的宏观层面，即阐明大量此类风险因素如何相互作用以影响疾病和治疗，虽对医疗决策支持建模至关重要，但并不普遍。通过利用自然语言处理的进展，我们认为可以提取医学文章等非结构化文本中包含的信息，以促进汇总到宏观层面的风险模型中。
