# THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

arXiv: https://arxiv.org/abs/2108.02817 · HTML: https://mlsanity.com/paper/2108.02817/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 109 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records](https://mlsanity.com/paper/1805.10724/) (2018)
- [Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features](https://mlsanity.com/paper/1802.01059/) (2018)
- [Clustrophile 2: Guided Visual Clustering Analysis](https://mlsanity.com/paper/1804.03048/) (2018)
- [Dynamic Influence Networks for Rule-based Models](https://mlsanity.com/paper/1711.00967/) (2017)
- [Clustrophile: A Tool for Visual Clustering Analysis](https://mlsanity.com/paper/1710.02173/) (2017)
- [Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration](https://mlsanity.com/paper/1907.05919/) (2019)
- [In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals](https://mlsanity.com/paper/2008.09552/) (2020)
- [Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology](https://mlsanity.com/paper/2008.11282/) (2020)

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

- [Visual Analysis and Detection of Contrails in Aircraft Engine Simulations](https://mlsanity.com/paper/2208.02321/) (2022)
- [DASS Good: Explainable Data Mining of Spatial Cohort Data](https://mlsanity.com/paper/2304.04870/) (2023)

## Abstract (arXiv)

Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.