# Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models

arXiv: https://arxiv.org/abs/2309.16521 · HTML: https://mlsanity.com/paper/2309.16521/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 28 Sep 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Is Conditional Generative Modeling all you need for Decision-Making?](https://mlsanity.com/paper/2211.15657/) (2022)
- [Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes](https://mlsanity.com/paper/2204.03376/) (2022)
- [Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification](https://mlsanity.com/paper/2209.04526/) (2022)
- [Goal-directed Generation of Discrete Structures with Conditional Generative Models](https://mlsanity.com/paper/2010.02311/) (2020)

## Built on by (most cited first, 4 of 4)

- [Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes](https://mlsanity.com/paper/2311.08149/) (2023)
- [Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data](https://mlsanity.com/paper/2402.12190/) (2024)
- [Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification](https://mlsanity.com/paper/2410.00509/) (2024)
- [Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis](https://mlsanity.com/paper/2407.11427/) (2024)

## Abstract (arXiv)

We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose prediction