Sarah L Eddy
Current Insights
CBE life sciences education
encephalopathy severe comorbid condition critically ill patients includes different clinical constellation neurological symptoms however even recognised form delirium medical condition rarely recorded structured fields electronic health records precluding large unbiased retrospective studies aimed identify patients encephalopathy using machine learningbased approach clinical notes electronic health records used list icd9 codes clinical concepts related encephalopathy define cohort patients mimiciii dataset clinical notes annotated medcat vectorized bagofword approach word embedding using clinical concepts normalised standard nomenclatures features machine learning algorithms support vector machines random forest trained clinical notes patients diagnosis encephalopathy defined icd9 codes used classify patients clinical concepts related encephalopathy clinical notes without icd9 relevant code random selection 50 patients reviewed clinical expert model validation among 46520 different patients 75 encephalopathy related icd9 codes admissions group 1 definite encephalopathy 45 clinical concepts related encephalopathy clinical notes group 2 possible encephalopathy 38 encephalopathy related concepts neither structured clinical notes group 3 nonencephalopathy length stay mortality rate number comorbid conditions higher groups 1 2 compared group 3 best model classify patients group 2 patients encephalopathy svm using embeddings f1 85 predicted 31 patients group 2 encephalopathy probability 90 validation new cases found precision ranging 92 98 depending criteria considered natural language processing techniques leverage relevant clinical information might help identify patients underrecognised clinical disorders encephalopathy mimic dataset approach identifies high probability thousands patients formal diagnosis structured information ehr