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US 10,949,622 B2Granted
Systems and methods for segmenting documents
AssigneeThe Florida International University Board of Trustees
InventorsDeya Banisakher, Naphtali Rishe, Mark Finlayson
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Abstract
Systems and methods for automatically modeling the discourse structure of psychiatric reports and segmenting these reports into various sections are provided. The systems and methods can be based around a model that learns the section types, positions, and sequence and can automatically segment unlabeled text in a psychiatric report into the corresponding sections. Knowledge of the ordering of the sections can improve the performance of a section classifier and a text segmenter. A Hierarchical Hidden Markov Model (HHMM) can be trained and can categorize sections in psychiatric reports into a predefined section label.
Claims20
A computer-based system of segmenting a psychiatric evaluation report into sections, the system comprising:
- a processor;
- and
- a non-transitory computer-readable medium in operable communication with the processor and comprising program instructions stored thereon that, when executed, cause the processor to:
- receive text data of the psychiatric evaluation report;
- analyze the text data;
- learn and build a first model for an order and presence of the sections in the psychiatric evaluation report;
- learn and build a second model to describe distinctive features of the respective sections in the psychiatric evaluation report;
- and
- apply a combination of the first model and the second model to simultaneously identify boundaries of the respective sections and to label section types of the respective sections, thereby segmenting the psychiatric evaluation report,
- the first model being a Hierarchical Hidden Markov Model (HHMM),
- the sections comprising a medical history section, a family history section, a mental status section, a psychiatric history section, a family psychiatric history section, and a treatment plan section,
- the first model and the second model using sentences as processing units to identify the boundaries of the respective sections, and
- the first model and the second model requiring no prior information regarding a quantity of sections in the psychiatric evaluation report.
10 more elements
A non-transitory computer-readable medium comprising program instructions stored thereon that, when executed, cause a processor to:
- receive text data of a psychiatric evaluation report;
- analyze the text data;
- learn and build a first model for an order and presence of sections in the psychiatric evaluation report;
- learn and build a second model to describe distinctive features of the respective sections in the psychiatric evaluation report;
- and
- apply a combination of the first model and the second model to simultaneously identify boundaries of the respective sections and to label section types of the respective sections, thereby segmenting the psychiatric evaluation report into the sections,
- the first model being a Hierarchical Hidden Markov Model (HHMM),
- the sections comprising a medical history section, a family history section, a mental status section, a psychiatric history section, a family psychiatric history section, and a treatment plan section,
- the first model and the second model using sentences as processing units to identify the boundaries of the respective sections, and
- the first model and the second model requiring no prior information regarding a quantity of sections in the psychiatric evaluation report.
7 more elements
A computer-based system of segmenting a psychiatric evaluation report into sections, the system comprising:
- a processor;
- and
- a non-transitory computer-readable medium in operable communication with the processor and comprising program instructions stored thereon that, when executed, cause the processor to:
- train the system using preexisting psychiatric evaluation reports, the training comprising using first n-gram language models to capture distinctive lexical information contained within individual sections of the preexisting psychiatric evaluation reports;
- receive text data of the psychiatric evaluation report, the training of the system being performed before the receiving of the text data of the psychiatric evaluation report;
- analyze the text data;
- learn and build a first model for an order and presence of the sections in the psychiatric evaluation report;
- learn and build a second model to describe distinctive features of the respective sections in the psychiatric evaluation report;
- apply a combination of the first model and the second model to simultaneously identify boundaries of the respective sections and to label section types of the respective sections, thereby segmenting the psychiatric evaluation report;
- and
- classify unknown blocks of text, in the text data of the psychiatric evaluation report, into the respective sections,
- the first model being a Hierarchical Hidden Markov Model (HHMM),
- the second model comprising a respective second n-gram language model for each section type in the psychiatric evaluation report,
- the learning and building of the second model comprising using the second n-gram language models as emission probabilities for the HHMM,
- the applying of the combination of the first model and the second model comprising following a decoding scheme using a Viterbi algorithm,
- the decoding scheme comprising applying the following equation to obtain the most likely labeling of each respective section, where O* is a set of the sections, n is an index of the sections, w0kn is a first long sequence of words, w1kn is a second long sequence of words, s is a state of the HHMM, kn is a word index for section n, and i is an index of the states of the HHMM:
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- the learning and building of the first model comprising using the states of the HHMM to respectively represent the sections in the psychiatric evaluation report, and
- the first n-gram language models being bigram language models.
121 more elements
Dependencies are inferred from the claim text.