Identifying Health Risks from Family History: A Survey of Natural
  Language Processing Techniques
Xiang Dai Sarvnaz Karimi Nathan O'Callaghan
Electronic health records include information on patients' status and medical
history, which could cover the history of diseases and disorders that could be
hereditary. One important use of family history information is in precision
health, where the goal is to keep the population healthy with preventative
measures. Natural Language Processing (NLP) and machine learning techniques can
assist with identifying information that could assist health professionals in
identifying health risks before a condition is developed in their later years,
saving lives and reducing healthcare costs.
  We survey the literature on the techniques from the NLP field that have been
developed to utilise digital health records to identify risks of familial
diseases. We highlight that rule-based methods are heavily investigated and are
still actively used for family history extraction. Still, more recent efforts
have been put into building neural models based on large-scale pre-trained
language models. In addition to the areas where NLP has successfully been
utilised, we also identify the areas where more research is needed to unlock
the value of patients' records regarding data collection, task formulation and
downstream applications.