# Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework

arXiv: https://arxiv.org/abs/2210.04232 · HTML: https://mlsanity.com/paper/2210.04232/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 68 references that are in the map.
- First posted on arXiv 9 Oct 2022.

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

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [Hierarchical Block Structures and High-resolution Model Selection in Large Networks](https://mlsanity.com/paper/1310.4377/) (2013)
- [Improving Topic Models with Latent Feature Word Representations](https://mlsanity.com/paper/1810.06306/) (2018)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [Nonparametric Bayesian inference of the microcanonical stochastic block model](https://mlsanity.com/paper/1610.02703/) (2016)
- [The anatomy of Reddit: An overview of academic research](https://mlsanity.com/paper/1810.10881/) (2018)
- [Twitter for Sparking a Movement, Reddit for Sharing the Moment: #metoo through the Lens of Social Media](https://mlsanity.com/paper/1803.08022/) (2018)
- [Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit](https://mlsanity.com/paper/2005.10454/) (2020)
- [A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter](https://mlsanity.com/paper/1805.09959/) (2018)

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

- [Probabilistic emotion and sentiment modelling of patient-reported experiences](https://mlsanity.com/paper/2401.04367/) (2024)

## Abstract (arXiv)

Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centered care approach. Healthcare discourse on social media presents an opportunity to gain a unique perspective on patient-reported experiences, complementing traditional survey data. These social media reports often appear as first-hand accounts of patients' journeys through the healthcare system, whose details extend beyond the confines of structured surveys and at a far larger scale than focus groups. However, in contrast with the vast presence of patient-experience data on social media and the potential benefits the data offers, it attracts comparatively little research attention due to the technical proficiency required for text analysis. In this paper, we introduce the Design-Acquire-Process-Model-Analyse-Visualise (DAPMAV) framework to provide an overview of techniques and an approach to capture patient-reported experiences from social media data. We apply this framework in a case study on prostate cancer data from /r/ProstateCancer, demonstrate the framework's value in capturing specific aspects of patient concern (such as sexual dysfunction), provide an overview of the discourse, and show narrative and emotional progression through these stories. We anticipate this framework to apply to a wide variety of areas in healthcare, including capturing and differentiating experiences across minority groups, geographic boundaries, and types of illnesses.