TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.
Frank Po-Yen LinAdrian PokornyChristina TengRichard J EpsteinPublished in: Scientific reports (2017)
Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pipeline termed Text-based Exploratory Pattern Analyser for Prognosticator and Associator discovery (TEPAPA). This pipeline combines semantic-free natural language processing (NLP), regular expression induction, and statistical association testing to identify conserved text patterns associated with outcome variables of clinical interest. When we applied TEPAPA to a cohort of head and neck squamous cell carcinoma patients, plausible concepts known to be correlated with human papilloma virus (HPV) status were identified from the EMR text, including site of primary disease, tumour stage, pathologic characteristics, and treatment modalities. Similarly, correlates of other variables (including gender, nodal status, recurrent disease, smoking and alcohol status) were also reliably recovered. Using highly-associated patterns as covariates, a patient's HPV status was classifiable using a bootstrap analysis with a mean area under the ROC curve of 0.861, suggesting its predictive utility in supporting EMR-based phenotyping tasks. These data support using this integrative approach to efficiently identify disease-associated factors from unstructured EMR narratives, and thus to efficiently generate testable hypotheses.
Keyphrases
- smoking cessation
- high throughput
- end stage renal disease
- small molecule
- ejection fraction
- high grade
- poor prognosis
- transcription factor
- chronic kidney disease
- electronic health record
- mental health
- autism spectrum disorder
- machine learning
- peritoneal dialysis
- squamous cell carcinoma
- deep learning
- prognostic factors
- lymph node
- artificial intelligence
- single cell
- data analysis
- patient reported outcomes
- cervical cancer screening