# Ontology-driven weak supervision for clinical entity classification in electronic health records

arXiv: https://arxiv.org/abs/2008.01972 · HTML: https://mlsanity.com/paper/2008.01972/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 18 of 18)

- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [Language Models in the Loop: Incorporating Prompting into Weak Supervision](https://mlsanity.com/paper/2205.02318/) (2022)
- [BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing](https://mlsanity.com/paper/2206.15076/) (2022)
- [Learning from Multiple Noisy Partial Labelers](https://mlsanity.com/paper/2106.04530/) (2021)
- [Creating Training Sets via Weak Indirect Supervision](https://mlsanity.com/paper/2110.03484/) (2021)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning](https://mlsanity.com/paper/2406.06620/) (2024)
- [Leveraging Large Language Models for Knowledge-free Weak Supervision in Clinical Natural Language Processing](https://mlsanity.com/paper/2406.06723/) (2024)
- [Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes](https://mlsanity.com/paper/2205.05656/) (2022)
- [Weak Supervision Performance Evaluation via Partial Identification](https://mlsanity.com/paper/2312.04601/) (2023)
- [Binary Classification with Positive Labeling Sources](https://mlsanity.com/paper/2208.01704/) (2022)
- [ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision](https://mlsanity.com/paper/2204.06863/) (2022)
- [An Adaptive Method for Weak Supervision with Drifting Data](https://mlsanity.com/paper/2306.01658/) (2023)
- [Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures](https://mlsanity.com/paper/2308.06199/) (2023)
- [What's in the laundromat? Mapping and characterising offshore owned domestic property in London](https://mlsanity.com/paper/2207.10931/) (2022)
- [A Benchmark Generative Probabilistic Model for Weak Supervised Learning](https://mlsanity.com/paper/2303.17841/) (2023)
- [Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes](https://mlsanity.com/paper/2305.11948/) (2023)
- [Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data](https://mlsanity.com/paper/2401.12295/) (2024)

## Abstract (arXiv)

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.