Xiaowen-JI
Experienced researcher and passionate practitioner in the science of mental healthcare; Familiar with Supervised machine learning and NLP techniques.
Pinned Repositories
BRFSS_California_depession_hypertension
google-mobility-and-covid-19-new-cases-2020
NLP
Compare Bag of word, GloVe imbedding, BERTS to MINIC-III notes
Rbasic
Semi-automation-of-systematic-review-of-clinical-trials-in-medical-psychology-with-BERT-models
We employed pre-trained BERT models (distillBERT, BioBert, and SciBert) for text-classifications of the titles and abstracts of clinical trials in medical psychology. The average score of AUC is 0.92. A stacked model was then built by featuring the probability predicted by distillBERT and keywords of search domains. The AUC improved to 0.96 with F1, precision, and recall increasing to 0.95, 0.94, and 0.96 respectively. Training sample size of 100 results in the most cost-effective performance.
Xiaowen-JI's Repositories
Xiaowen-JI/Semi-automation-of-systematic-review-of-clinical-trials-in-medical-psychology-with-BERT-models
We employed pre-trained BERT models (distillBERT, BioBert, and SciBert) for text-classifications of the titles and abstracts of clinical trials in medical psychology. The average score of AUC is 0.92. A stacked model was then built by featuring the probability predicted by distillBERT and keywords of search domains. The AUC improved to 0.96 with F1, precision, and recall increasing to 0.95, 0.94, and 0.96 respectively. Training sample size of 100 results in the most cost-effective performance.
Xiaowen-JI/BRFSS_California_depession_hypertension
Xiaowen-JI/google-mobility-and-covid-19-new-cases-2020
Xiaowen-JI/NLP
Compare Bag of word, GloVe imbedding, BERTS to MINIC-III notes
Xiaowen-JI/Rbasic