Detect adverse drug events in clinical text, tweets and reviews
It is a pretrained NER deep learning model used for detecting adverse reactions of drugs in reviews, tweets, and medical text, and is compatible with Spark NLP for Healthcare versions 3.0.0+. It uses SOTA techniqus like BERT, BioBERT, BERT-clinical, etc.
Input variables : Clinical text
Output Variables : DRUG, ADE
Metrics to Monitor
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Statistical
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Somers D |
Accuracy |
Precision and Recall |
Confusion Matrix |
F1 Score |
Roc and Auc |
Prevalence |
Detection Rate |
Balanced Accuracy |
Cohen's Kappa |
Concordance |
Gini Coefficent |
KS Statistic |
Youden's J Index
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Business
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# of Adverse Events
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Infrastructure
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Log Bytes |
Logging/User/IAMPolicy |
Logging/User/VPN |
CPU Utilization |
Memory Usage |
Error Count |
Prediction Count |
Prediction Latencies |
Private Endpoint Prediction Latencies |
Private Endpoint Response Count
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Visit Model :
nlp.johnsnowlabs.com