# A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care

arXiv: https://arxiv.org/abs/1808.03265 · HTML: https://mlsanity.com/paper/1808.03265/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS](https://mlsanity.com/paper/2104.04140/) (2021)
- [Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings](https://mlsanity.com/paper/2106.07720/) (2021)
- [Personalized next-best action recommendation with multi-party interaction learning for automated decision-making](https://mlsanity.com/paper/2108.08846/) (2021)
- [Extreme Multilabel Classification for Specialist Doctor Recommendation with Implicit Feedback and Limited Patient Metadata](https://mlsanity.com/paper/2308.11022/) (2023)
- [Dissecting Medical Referral Mechanisms in Health Services: Role of Physician Professional Networks](https://mlsanity.com/paper/2312.02387/) (2023)
- [C-PATH: Conversational Patient Assistance and Triage in Healthcare System](https://mlsanity.com/paper/2506.06737/) (2025)
- [SiMCa: Sinkhorn Matrix Factorization with Capacity Constraints](https://mlsanity.com/paper/2203.10107/) (2022)

## Abstract (arXiv)

We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care. We define the matchmaking process for several distinct use cases given different levels of available information about patients. Then, we adopt a hybrid recommender system to present each patient a list of family doctor recommendations. In particular, we model patient trust of family doctors using a large-scale dataset of consultation histories, while accounting for the temporal dynamics of their relationships. Our proposed approach shows higher predictive accuracy than both a heuristic baseline and a collaborative filtering approach, and the proposed trust measure further improves model performance.