# WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data

arXiv: https://arxiv.org/abs/2312.14129 · HTML: https://mlsanity.com/paper/2312.14129/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [BioSentVec: creating sentence embeddings for biomedical texts](https://mlsanity.com/paper/1810.09302/) (2018)
- [DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](https://mlsanity.com/paper/1909.03602/) (2019)
- [Learning to Hash with Graph Neural Networks for Recommender Systems](https://mlsanity.com/paper/2003.01917/) (2020)
- [Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation](https://mlsanity.com/paper/2204.00377/) (2022)
- [Hybrid Clustering based on Content and Connection Structure using Joint Nonnegative Matrix Factorization](https://mlsanity.com/paper/1703.09646/) (2017)
- [TopicSifter: Interactive Search Space Reduction Through Targeted Topic Modeling](https://mlsanity.com/paper/1907.12079/) (2019)
- [Time Lag Aware Sequential Recommendation](https://mlsanity.com/paper/2208.04760/) (2022)

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

- [Randomized Algorithms for Symmetric Nonnegative Matrix Factorization](https://mlsanity.com/paper/2402.08134/) (2024)

## Abstract (arXiv)

In the rapidly evolving healthcare industry, platforms now have access to not only traditional medical records, but also diverse data sets encompassing various patient interactions, such as those from healthcare web portals. To address this rich diversity of data, we introduce WellFactor: a method that derives patient profiles by integrating information from these sources. Central to our approach is the utilization of constrained low-rank approximation. WellFactor is optimized to handle the sparsity that is often inherent in healthcare data. Moreover, by incorporating task-specific label information, our method refines the embedding results, offering a more informed perspective on patients. One important feature of WellFactor is its ability to compute embeddings for new, previously unobserved patient data instantaneously, eliminating the need to revisit the entire data set or recomputing the embedding. Comprehensive evaluations on real-world healthcare data demonstrate WellFactor's effectiveness. It produces better results compared to other existing methods in classification performance, yields meaningful clustering of patients, and delivers consistent results in patient similarity searches and predictions.