Advancing Personalized Medicine: A Scalable {LLM}-based Recommender System for Patient Matching

This study explores efficient algorithms to enhance user matching in Unrare.me, a novel social networking platform designed to connect individuals affected by rare diseases. Our primary objective is to develop a recommender system that identifies and suggests users with similar medical conditions, facilitating meaningful connections within these unique communities. Utilizing textual user profile data, we train sentence embedder models to generate similar embeddings for users that have rated each other high. We investigate various fine-tuning strategies, as well as a hybrid approach between a dense embedder and sparse {SPLADE} embeddings. Furthermore, we investigate the efficacy of various clustering algorithms, such as {TopicBERT} for thematic analysis, K-Means for centroid-based grouping, and Latent Dirichlet Allocation ({LDA}) for probabilistic topic modeling, to reduce the matching complexity and enable better scalability of the platform.

  • Published in:
    2024 {IEEE} International Conference on Big Data ({BigData})
  • Type:
    Inproceedings
  • Authors:
    Berger, Armin; Berghaus, David; Bashir, Ali Hamza; Grigull, Lorenz; Fendrich, Lara; Lagones, Tom Anglim; Högl, Henriette; Ernst, Gundula; Schmidt, Ralf; Bascom, David; Deußer, Tobias; Bell, Thiago; Lübbering, Max; Sifa, Rafet
  • Year:
    2024
  • Source:
    https://ieeexplore.ieee.org/abstract/document/10825567/authors#authors

Citation information

Berger, Armin; Berghaus, David; Bashir, Ali Hamza; Grigull, Lorenz; Fendrich, Lara; Lagones, Tom Anglim; Högl, Henriette; Ernst, Gundula; Schmidt, Ralf; Bascom, David; Deußer, Tobias; Bell, Thiago; Lübbering, Max; Sifa, Rafet: Advancing Personalized Medicine: A Scalable {LLM}-based Recommender System for Patient Matching, 2024 {IEEE} International Conference on Big Data ({BigData}), 2024, 5876--5883, December, https://ieeexplore.ieee.org/abstract/document/10825567/authors#authors, Berger.etal.2024a,

Associated Lamarr Researchers

Prof. Dr. Rafet Sifa

Prof. Dr. Rafet Sifa

Principal Investigator Hybrid ML to the profile