Nikita Makarov
@nikitamakarov.bsky.social
LLMs & Digital Twins for Cancer | PhD student at Roche pRED & Helmholtz Munich | Opinions are my own
Paper here: www.nature.com/articles/s41...
“Large Language Models forecast Patient Health Trajectories enabling Digital Twins”
“Large Language Models forecast Patient Health Trajectories enabling Digital Twins”
Large language models forecast patient health trajectories enabling digital twins - npj Digital Medicine
npj Digital Medicine - Large language models forecast patient health trajectories enabling digital twins
www.nature.com
October 7, 2025 at 7:39 AM
Paper here: www.nature.com/articles/s41...
“Large Language Models forecast Patient Health Trajectories enabling Digital Twins”
“Large Language Models forecast Patient Health Trajectories enabling Digital Twins”
Overall, DT-GPT shows that LLMs have the potential to become human digital twins. We hope that, in the future, LLM based digital twins will revolutionize the way we run clinical trials & patient care (10/10).
October 7, 2025 at 7:39 AM
Overall, DT-GPT shows that LLMs have the potential to become human digital twins. We hope that, in the future, LLM based digital twins will revolutionize the way we run clinical trials & patient care (10/10).
Check out the paper and appendix for many more results, including exploration of zero shot, various input parameters, latent clinical knowledge and tech details (9/10)
October 7, 2025 at 7:39 AM
Check out the paper and appendix for many more results, including exploration of zero shot, various input parameters, latent clinical knowledge and tech details (9/10)
In zero shot forecasting, DT-GPT outperformed a fully trained model on 13 variables. These variables were typically biologically linked to the target variables used during training (8/10)
October 7, 2025 at 7:38 AM
In zero shot forecasting, DT-GPT outperformed a fully trained model on 13 variables. These variables were typically biologically linked to the target variables used during training (8/10)
We show that key variables (e.g. therapy, ECOG) can drive differences in both predictions and real data. DT-GPT can even offer preliminary explainability & perform zero-shot on variables that it did not see during training. (7/10)
October 7, 2025 at 7:38 AM
We show that key variables (e.g. therapy, ECOG) can drive differences in both predictions and real data. DT-GPT can even offer preliminary explainability & perform zero-shot on variables that it did not see during training. (7/10)
DT-GPT is robust: achieves competitive performance after ~5,000 patients, can even handle a 20% increase in missingness and up to 25 misspellings per sample without significant performance degradation (6/10)
October 7, 2025 at 7:38 AM
DT-GPT is robust: achieves competitive performance after ~5,000 patients, can even handle a 20% increase in missingness and up to 25 misspellings per sample without significant performance degradation (6/10)
Taking a step back, we see that DT-GPT also preserves the overall distribution of the outputs better than other baselines, quantified with the KS-distance (5/10)
October 7, 2025 at 7:37 AM
Taking a step back, we see that DT-GPT also preserves the overall distribution of the outputs better than other baselines, quantified with the KS-distance (5/10)
Digging deeper, DT-GPT generally outperforms the second best model longitudinally. In many cases, high error predictions occur since our forecasts are based on aggregations of multiple trajectories, even if some individual trajectories are closer to the ground truth (4/10)
October 7, 2025 at 7:37 AM
Digging deeper, DT-GPT generally outperforms the second best model longitudinally. In many cases, high error predictions occur since our forecasts are based on aggregations of multiple trajectories, even if some individual trajectories are closer to the ground truth (4/10)
Our method, DT-GPT, outperforms the SOTA baselines in most cases, or achieves very competitive performance. Here you see the mean absolute error (MAE) across 12 variables in 3 different indications (3/10)
October 7, 2025 at 7:37 AM
Our method, DT-GPT, outperforms the SOTA baselines in most cases, or achieves very competitive performance. Here you see the mean absolute error (MAE) across 12 variables in 3 different indications (3/10)
We fine-tune biomedical LLMs on patient clinical data, exploring the method on both a long term lung cancer dataset, and a short term ICU dataset. A few adjustments are required to get full performance, esp. trajectory aggregation & instruction masking (2/10)
October 7, 2025 at 7:37 AM
We fine-tune biomedical LLMs on patient clinical data, exploring the method on both a long term lung cancer dataset, and a short term ICU dataset. A few adjustments are required to get full performance, esp. trajectory aggregation & instruction masking (2/10)
This is great! Would it be possible to add me to this? 🙏
November 27, 2024 at 9:18 AM
This is great! Would it be possible to add me to this? 🙏
This is fantastic! Would it be possible to add me in as well?
November 22, 2024 at 7:05 AM
This is fantastic! Would it be possible to add me in as well?
Pre-print here: medrxiv.org/content/10.1...
“Large Language Models forecast Patient Health Trajectories enabling Digital Twins”
Reposted from X to have some content here :)
“Large Language Models forecast Patient Health Trajectories enabling Digital Twins”
Reposted from X to have some content here :)
Large Language Models forecast Patient Health Trajectories enabling Digital Twins
Background Generative artificial intelligence (AI) accelerates the development of digital twins, which enable virtual representations of real patients to explore, predict and simulate patient health t...
medrxiv.org
November 20, 2024 at 10:07 AM
Pre-print here: medrxiv.org/content/10.1...
“Large Language Models forecast Patient Health Trajectories enabling Digital Twins”
Reposted from X to have some content here :)
“Large Language Models forecast Patient Health Trajectories enabling Digital Twins”
Reposted from X to have some content here :)
Overall, DT-GPT shows that LLMs have the potential to become human digital twins. We hope that, in the future, LLM based digital twins will revolutionize the way we run clinical trials & patient care (8/8).
November 20, 2024 at 10:07 AM
Overall, DT-GPT shows that LLMs have the potential to become human digital twins. We hope that, in the future, LLM based digital twins will revolutionize the way we run clinical trials & patient care (8/8).
In zero shot forecasting, DT-GPT outperformed a fully trained model on 13 variables. These variables were typically biologically linked to the target variables used during training (7/8)
November 20, 2024 at 10:07 AM
In zero shot forecasting, DT-GPT outperformed a fully trained model on 13 variables. These variables were typically biologically linked to the target variables used during training (7/8)
DT-GPT can offer preliminary explainability & perform zero-shot on variables that it did not see during training. We show that key variables (e.g. therapy, ECOG) can drive differences in both predictions and real data (6/8)
November 20, 2024 at 10:06 AM
DT-GPT can offer preliminary explainability & perform zero-shot on variables that it did not see during training. We show that key variables (e.g. therapy, ECOG) can drive differences in both predictions and real data (6/8)