  Early detection of preventable diseases is important for better disease
management, improved inter-ventions, and more efficient health-care resource
allocation. Various machine learning approacheshave been developed to utilize
information in Electronic Health Record (EHR) for this task. Majorityof
previous attempts, however, focus on structured fields and lose the vast amount
of information inthe unstructured notes. In this work we propose a general
multi-task framework for disease onsetprediction that combines both free-text
medical notes and structured information. We compareperformance of different
deep learning architectures including CNN, LSTM and hierarchical models.In
contrast to traditional text-based prediction models, our approach does not
require disease specificfeature engineering, and can handle negations and
numerical values that exist in the text. Ourresults on a cohort of about 1
million patients show that models using text outperform modelsusing just
structured data, and that models capable of using numerical values and
negations in thetext, in addition to the raw text, further improve performance.
Additionally, we compare differentvisualization methods for medical
professionals to interpret model predictions.
