Detect Drug Entities (DRUG)

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val ner_pipeline = PretrainedPipeline("ner_drug_benchmark_pipeline", "en", "clinical/models")

val text = """The patient was admitted to the Surgical Intensive Care Unit postoperatively with stable hemodynamics on Lidocaine at one , Dobutamine at 200 and Nipride .
The patient was extubated by postoperative day # 1 but was noted to be relatively hypoxemic with high oxygen requirement .
Chest x-ray demonstrating pulmonary edema .
Aggressive diuresis was undertaken and the patient responded , albeit sluggishly .
In addition , he remained agitated .
This was attributed to mild hypoxia and / or his underlying psychiatric diagnoses and he was treated with Haldol appropriately .
On day # 2 the patient continued to diurese and this was maintained with the Lasix/ Mannitol infusion .
His urine output remained at 150 to 200 cc. an hour .
Despite this , his chest x-ray continued to show severe pulmonary edema and the clinical picture correlated .
He required anti-hypertensive therapy initially with Nipride which was changed to Hydralazine to avoid shunting .
Consequently his Pronestyl was discontinued .
In addition , the patient was given magnesium to bring his level above 2 ."""

val result = ner_pipeline.fullAnnotate(text)