Every year, a vast amount of unstructured medical knowledge is described in thousands

of pre-clinical studies published on publicly available websites such as PubMed. The

aggregation of such knowledge plays an important role in various medical applications

such as therapy development in evidence-based medicine where decisions are made on

the basis of the best available evidence published in the literature so far. However, due

to their natural language format, the manual aggregation of available information is

tedious and time-consuming and can hardly be performed by researchers. Towards this

issue, we are concerned with the automatic information extraction of structured knowledge at a level of detail that supports evidence-based decision making. Specifically, we

focus on automatically populating a deep domain knowledge graph with information

from pre-clinical studies that describe experimental results in the area of spinal cord

injury. An important challenge is that a single study contains multiple outcomes described by a total of up to 7,816 (dependent) study parameters. Since the problem of

extracting all these parameters jointly is so far intractable, we propose a hierarchical

architecture that predicts incrementally feasible substructures in a bottom-up fashion

relying on statistical inference and conditional random fields at the heart of our system.

The main contribution of this work is the development of a machine learning methods

integrated into a holistic domain-adapted information extraction system that is capable of predicting the full details of experimental outcomes as described in pre-clinical

studies written in natural language. We present a general methodology for the extraction of deeply nested structures rooted in the paradigm of structure prediction and

model-complete text comprehension. We further identify domain specific challenges, and

provide adapted solutions. We show how to efficiently evaluate complex nested structures predicted by our system and present a comprehensive evaluation to understand

the extent to which it can be used with the depth required to support aggregation of

evidence. We show that the information extraction results are satisfactory for many

classes of our domain ontology and identify those which require further research.