Text descriptions in natural language are an essential part of electronic health records (EHRs). Such descriptions usually contain facts about patient’s life, events, diseases and other relevant information. Sometimes it may also include facts about their family members. In order to find the facts about the right person (experiencer) and convert the unstructured medical text into structured information, we developed a module of experiencer detection. We compared different vector representations and machine learning models to get the highest quality of 0.96 f-score for binary classification and 0.93 f-score for multi-classification. Additionally, we present the results plotting the family disease tree.