# Linking Symptom Inventories using Semantic Textual Similarity

arXiv: https://arxiv.org/abs/2309.04607 · HTML: https://mlsanity.com/paper/2309.04607/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 113 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [BERTMap: A BERT-based Ontology Alignment System](https://mlsanity.com/paper/2112.02682/) (2021)
- [Aligning Biomedical Metadata with Ontologies Using Clustering and Embeddings](https://mlsanity.com/paper/1903.08206/) (2019)
- [Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus](https://mlsanity.com/paper/2109.13348/) (2021)

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

- [Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey](https://mlsanity.com/paper/2601.03270/) (2025)

## Abstract (arXiv)

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment.