# Identifying Health Risks from Family History: A Survey of Natural Language Processing Techniques

arXiv: https://arxiv.org/abs/2403.09997 · HTML: https://mlsanity.com/paper/2403.09997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction](https://mlsanity.com/paper/2004.05438/) (2020)
- [A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing](https://mlsanity.com/paper/2112.05780/) (2021)
- [Question Answering with Deep Neural Networks for Semi-Structured Heterogeneous Genealogical Knowledge Graphs](https://mlsanity.com/paper/2307.16214/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Systematic Review on the Generative AI Applications in Human Medical Genomics](https://mlsanity.com/paper/2508.20275/) (2025)

## Abstract (arXiv)

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.