Automated workflows for identification of reading order from text segments using probabilistic language models
US-2018373952-A1 · Dec 27, 2018 · US
Systems and methods for segmenting documents
US10949622B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-10949622-B2 |
| Application number | US-201916667991-A |
| Country | US |
| Kind code | B2 |
| Filing date | Oct 30, 2019 |
| Priority date | Oct 30, 2018 |
| Publication date | Mar 16, 2021 |
| Grant date | Mar 16, 2021 |
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Abstract
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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.
First claim
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What is claimed is: 1. 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. 2. The system according to claim 1 , the second model comprising a respective language model for each section type in the psychiatric evaluation report. 3. The system according to claim 2 , each respective language model being an n-gram language model. 4. The system according to claim 3 , the learning and building of the second model comprising using the n-gram language models as emission probabilities for the HHMM. 5. The system according to claim 1 , the applying of the combination of the first model and the second model comprising following a decoding scheme using a Viterbi algorithm. 6. The system according to claim 5 , 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, w 0 k n is a first long sequence of words, w 1 k n is a second long sequence of words, s is a state of the HHMM, k n is a word index for section n, and i is an index of the states of the HHMM: O * = arg max s P ( s ) P ( w 0 k n | s ) = arg max s 1 s 2 … s n P ( s 1 ) P ( w 0 k
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Probabilistic graphical models, e.g. probabilistic networks · CPC title
Machine learning · CPC title
ICT specially adapted for medical reports, e.g. generation or transmission thereof · CPC title
Parsing · CPC title
for mining of medical data, e.g. analysing previous cases of other patients · CPC title
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- What does patent US10949622B2 cover?
- 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 …
- Who is the assignee on this patent?
- Banisakher Deya, Rishe Naphtali, Finlayson Mark, and 1 more
- What technology area does this patent fall under?
- Primary CPC classification G06F40/30. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Tue Mar 16 2021 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
- What related patents are in patentsdb?
- We list 5 related publications on this page (citations in our corpus or others sharing the same primary CPC).