Patents by Inventor Shrujal Baxi

Shrujal Baxi has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • PROGNOSTIC SCORE BASED ON HEALTH INFORMATION
    Publication number: 20240078448
    Abstract: A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.
    Type: Application
    Filed: April 11, 2023
    Publication date: March 7, 2024
    Applicant: Flatiron Health, Inc.
    Inventors: Joshua D. Haimson, Shrujal Baxi, Neal Meropol, Geetu Ambwani, Daniel Backenroth, Mahesh Murali, Andrej Rosic, Chengsheng Jiang
  • Prognostic score based on health information
    Patent number: 11651252
    Abstract: A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.
    Type: Grant
    Filed: February 25, 2020
    Date of Patent: May 16, 2023
    Assignee: Flatiron Health, Inc.
    Inventors: Joshua D. Haimson, Shrujal Baxi, Neal Meropol, Geetu Ambwani, Daniel Backenroth, Mahesh Murali, Andrej Rosic, Chengsheng Jiang
  • PROGNOSTIC SCORE BASED ON HEALTH INFORMATION
    Publication number: 20200272919
    Abstract: A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.
    Type: Application
    Filed: February 25, 2020
    Publication date: August 27, 2020
    Applicant: Flatiron Health, Inc.
    Inventors: Joshua D. Haimson, Shrujal Baxi, Neal Meropol, Geetu Ambwani, Daniel Backenroth, Mahesh Murali, Andrej Rosic, Chengsheng Jiang