# BigBird for Mortality Prediction

Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes. This 
model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon 
admission. Data prepared for this task as described in [this project](https://github.com/bvanaken/clinical-outcome-prediction), 
using the simulated admission notes (taken from discharge summaries). This model will be used in an upcoming submission for 
IMLH at ICML 2021.

### References
* Van Aken, et al., 2021: [Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75/)
* Zaheer, et al., 2020: [Big Bird: Transformers for Longer Sequences](https://papers.nips.cc/paper/2020/hash/c8512d142a2d849725f31a9a7a361ab9-Abstract.html)
{"license": "bigscience-openrail-m"}
transformers pytorch safetensors big_bird text-classification license:bigscience-openrail-m autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #big_bird #text-classification #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #region-us 

# BigBird for Mortality Prediction

Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes. This 
model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon 
admission. Data prepared for this task as described in this project, 
using the simulated admission notes (taken from discharge summaries). This model will be used in an upcoming submission for 
IMLH at ICML 2021.

### References
* Van Aken, et al., 2021: Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
* Zaheer, et al., 2020: Big Bird: Transformers for Longer Sequences
# BigBird for Mortality Prediction

Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes. This 
model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon 
admission. Data prepared for this task as described in this project, 
using the simulated admission notes (taken from discharge summaries). This model will be used in an upcoming submission for 
IMLH at ICML 2021. ### References
* Van Aken, et al., 2021: Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
* Zaheer, et al., 2020: Big Bird: Transformers for Longer Sequences
TAGS
#transformers #pytorch #safetensors #big_bird #text-classification #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #region-us 
 # BigBird for Mortality Prediction

Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes. This 
model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon 
admission. Data prepared for this task as described in this project, 
using the simulated admission notes (taken from discharge summaries). This model will be used in an upcoming submission for 
IMLH at ICML 2021. ### References
* Van Aken, et al., 2021: Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
* Zaheer, et al., 2020: Big Bird: Transformers for Longer Sequences
passage: TAGS
#transformers #pytorch #safetensors #big_bird #text-classification #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #region-us 
# BigBird for Mortality Prediction

Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes. This 
model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon 
admission. Data prepared for this task as described in this project, 
using the simulated admission notes (taken from discharge summaries). This model will be used in an upcoming submission for 
IMLH at ICML 2021.### References
* Van Aken, et al., 2021: Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
* Zaheer, et al., 2020: Big Bird: Transformers for Longer Sequences