Abstract: Techniques for analyzing patient health records are provided. Clinical documents may be received in response to a patient-initiated request, for example. In one embodiment, machine learning algorithms are used to sectionalize and extract data from clinical documents. The machine learning algorithms used may be more highly focused for analyzing text residing deeper in a clinical document hierarchy, for example. In one embodiment, extracted data is stored in a patient graph. Searches may be made against the graph to yield results to help save lives and/or improve patient outcomes.

Abstract: Techniques for sectionalizing clinical documents are provided. In one set of embodiments, a computer system can, for each page of a clinical document: identify one or more section header candidates in the page and, for each section header candidate, attempt to classify the section header candidate as corresponding to one of a plurality of section types using a first classifier or a second classifier. The computer system can further partition the page into one or more sections based on corresponding section header candidates that have been successfully classified using either the first classifier or the second classifier, where the partitioning includes associating each section with a section type in the plurality of section types in accordance with the classification of the section's corresponding section header candidate. The computer system can then validate, for each section, the section's section type via an analysis of the body of the section.

Abstract: Techniques for extracting clinical entities from clinical documents are provided. In some embodiments, a non-transitory machine-readable medium stores a program. The program receives a clinical document partitioned into a set of sections. Each section in the set of sections includes a plurality of terms. The program further identifies a section in the set of sections. The program also determines a subset of a plurality of entity recognizers based on a classification of the section. Each entity recognizer in the plurality of entity recognizers is configured to identify terms in the section as being associated with a particular type of data. The program further sends the section to the subset of the entity recognizers for processing. The program also generates a clinical statement based on terms identified in the section by the subset of the plurality of entity recognizers.