As electronic medical records become the norm for documenting medical history of patients, reuse of the data generated during the care process opens new ways of supporting clinical decisions. Advanced data analysis techniques, machine learning and data mining models that make secondary use of medical data are accepted more and more in clinical applications. Despite the advent of data-driven models, the practitioners find it important to have transparent models whose behavior can be understood well. In this respect, natural language is an effective means for communicating model behavior to the users. We argue that linguistic models based on fuzzy set theory form an excellent bridge between the data-driven modeling and the transparency required by the users in the clinical domain. We discuss several different modeling approaches that use fuzzy set theory to develop models for supporting clinical decisions and improving the care process.