Early Prediction of Vital Signs Using Generative Boosting via LSTM Networks Vital signs including heart rate, respiratory rate, body temperature and blood pressure, are critical in the clinical decision making process. Abnormal vital signs help to alert medical practitioners to potential health problems. Effective and longrange early prediction of vital signs may prevent adverse health outcomes and reduce cost. In this paper, we suggest a new approach called generative boosting, in order to effectively perform longrange early prediction of vital signs. Generative boosting consists of a generative model, to generate synthetic data for the next few time steps, and a predictive model, to directly make longrange predictions based on observed and generated data. We explore generative boosting via long shortterm memory (LSTM) for both the predictive and generative models, leading to a scheme called generative LSTM (GLSTM). Our experiments indicate that GLSTM outperforms a diverse range of strong benchmark models, with and without generative boosting. As expected, the results also indicate that more accurately generated data leads to more accurate longrange predictions. In light of this, we use a mutual information based clustering algorithm to select a more representative dataset to train the generative model. This leads to significantly improved accuracy of the longrange prediction of high variation vital signs such as heart rate and systolic blood pressure. Overall, our best results indicate that the proposed method is able to predict heart rate and systolic blood pressure 20 minutes in advance, with a mean absolute percentage error of 7.41% and 6.17%, respectively.

BoostER: A Performance Boosting Module for Biomedical Entity Recognition Biomedical Entity Recognition tasks have gained significant importance in the clinical research domain. There has been a lot of prior work on improving entity recognition of biomedical concepts from using rulebased to more contextdependent deep learningbased approaches. However, due to its high domain dependency and distinctive vocabularies, appropriate utilization of contextual knowledge becomes a challenge even for context dependent deep models. To this end, we propose a novel performanceboosting improvement module xe2x80x9cBoostERxe2x80x9d that can be applied to any existing entity recognition system to boost its performance in terms of precision and F1 scores. The proposed module has been developed on top of a pretrained BERT model and finetuned to give more weights to contextual learning compared to wordspecific information. We tested our system with Chemical and Disease Entity Recognition tasks using the BioCreative CDR dataset to demonstrate its effectiveness compared to existing stateoftheart models.