Google llc (20240112804). MATCHING UNSTRUCTURED TEXT TO CLINICAL ONTOLOGIES simplified abstract

MATCHING UNSTRUCTURED TEXT TO CLINICAL ONTOLOGIES

Organization Name

google llc

Inventor(s)

Itay Laish of Timrat (IL)

Uri N. Lerner of Los Altos CA (US)

Aviel Atias of Tel Aviv (IL)

Natan Potikha of Tel Aviv (IL)

Ayelet Benjamini of Kfar Saba (IL)

MATCHING UNSTRUCTURED TEXT TO CLINICAL ONTOLOGIES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240112804 titled 'MATCHING UNSTRUCTURED TEXT TO CLINICAL ONTOLOGIES

Simplified Explanation

The computer-implemented method described in the abstract involves matching unstructured text from clinical notes to ontology entities in a clinical ontology. Here is a simplified explanation of the abstract:

• Receiving clinical notes associated with a patient
• Extracting text spans from the unstructured text in each clinical note using a neural network
• Matching each text span with a respective output ontology entity from an ontology using a text matcher
• Outputting data defining the text spans and the respective output ontology entity for each text span

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1. 1. 1. Potential Applications of this Technology

- Automated coding of clinical notes - Enhancing clinical decision support systems

1. 1. 1. Problems Solved by this Technology

- Improving accuracy and efficiency in identifying clinical conditions in unstructured text - Streamlining the process of mapping text to ontology entities

1. 1. 1. Benefits of this Technology

- Facilitates better patient care through accurate identification of clinical conditions - Saves time for healthcare professionals by automating the matching process

1. 1. 1. Potential Commercial Applications of this Technology
         1. Improving Clinical Documentation Efficiency with Text Matching to Ontology Entities

1. 1. 1. Possible Prior Art

There are existing methods for text matching and ontology mapping in various fields, such as natural language processing and information retrieval.

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1. 1. Unanswered Questions

1. 1. 1. How does this method handle ambiguity in the text spans when matching them to ontology entities?

The abstract does not specify how the method deals with potential ambiguity in the text spans during the matching process.

1. 1. 1. What is the computational complexity of this method when processing a large volume of clinical notes?

The abstract does not provide information on the scalability and computational efficiency of the method when handling a significant amount of unstructured text data.

Original Abstract Submitted

a computer-implemented method for matching unstructured text to ontology entities in a clinical ontology is described. the method includes receiving one or more clinical notes associated with a patient; for each of the one or more clinical notes: extracting, using a neural network, one or more text spans from unstructured text in each clinical note, each of the one or more text spans identifying a respective input phrase in the unstructured text; for each of the one or more text spans, matching, using a text matcher, the text span with a respective output ontology entity from an ontology, the respective output ontology entity relating to a clinical condition of the patient; and outputting data defining the one or more text spans and the respective output ontology entity for each of the one or more text spans.