HealthHistoryBERTimbau-pt

Health History BERTimbau pt

The HealthHistoryBERTimbau-pt was pre-trained from the pre-trained model BERTimbau Base and with patient data from health insurances organized in the form of historical sentences. The initial objective of the training was to predict hospitalizations, however, due to the possibility of applications in other tasks, we made these models available to the scientific community. This model was trained in Portuguese. There are also other training approaches that can be seen at:

Other pre-trained models

• HealthHistoryRoBERTa-en
• HealthHistoryRoBERTa-pt
• HealthHistoryBERT-en
• HealthHistoryBioBERT-en
• HealthHistoryBio_ClinicalBERT-en
• HealthHistoryBERTimbau-pt

Other Models trained to predict hospitalizations (fine-tune)

• HealthHistoryOpenLLaMA3Bv2-en-ft
• HealthHistoryOpenLLaMA7Bv2-en-ft
• HealthHistoryOpenLLaMA13B-en-ft
• HealthHistoryOpenCabrita3B-pt-ft
• HealthHistoryRoBERTa-en-ft
• HealthHistoryRoBERTa-pt-ft
• HealthHistoryBERTimbau-pt-ft
• HealthHistoryBERT-en-ft
• HealthHistoryBioBERT-en-ft
• HealthHistoryBio_ClinicalBERT-en-ft

Pretraining Data

The model was pre-trained from 837,159 historical sentences from health insurance patients generated using the approach described in this paper Predicting Hospitalization from Health Insurance Data.

Model Pretraining

Pretraining Procedures

The model was trained on a GeForce NVIDIA RTX A5000 24GB GPU from laboratories of IT departament at UFPR (Federal University of Paraná). The model parameters were initialized with BERTimbau Base.

Pretraining Hyperparameters

We use a batch size of 16, a maximum sequence length of 512, accumulation steps of 4, masked language model probability = 0.15, number of epochs = 1 and a learning rate of 10−4 to pre-train this model.

Pre-training time

The training time was 5 hours 26 minutes and 40 seconds per epoch.

How to use the model

Load the model via the transformers library:

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("efbaro/HealthHistoryBERTimbau-pt")
model = AutoModel.from_pretrained("efbaro/HealthHistoryBERTimbau-pt")

More Information

Refer to the original paper, Predicting Hospitalization with LLMs from Health Insurance Data

Refert to another article related to this research, Predicting Hospitalization from Health Insurance Data

Questions?

Email:

• Everton F. Baro: [email protected], [email protected]
• Luiz S. Oliveira: [email protected]
• Alceu de Souza Britto Junior: [email protected]