# Clinical Tagging with Joint Probabilistic Models

arXiv: https://arxiv.org/abs/1608.00686 · HTML: https://mlsanity.com/paper/1608.00686/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [Data Consistency for Weakly Supervised Learning](https://mlsanity.com/paper/2202.03987/) (2022)
- [From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models](https://mlsanity.com/paper/2509.09303/) (2025)

## Abstract (arXiv)

We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record. The method does not rely on the availability of gold-standard labels, but rather uses noisy labels, called anchors, for learning. We provide a likelihood-based objective and a moments-based initialization that are effective at learning the model parameters. The learned model is evaluated in a task of assigning a heldout clinical condition to patients based on retrospective analysis of the records, and outperforms baselines which do not account for the noisiness in the labels or do not model the conditions jointly.