International business machines corporation (20240105338). PREDICTING STATES FROM PATIENT-CAREGIVER DYADIC BIOMARKER DATA USING ARTIFICIAL INTELLIGENCE simplified abstract
Contents
- 1 PREDICTING STATES FROM PATIENT-CAREGIVER DYADIC BIOMARKER DATA USING ARTIFICIAL INTELLIGENCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PREDICTING STATES FROM PATIENT-CAREGIVER DYADIC BIOMARKER DATA USING ARTIFICIAL INTELLIGENCE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
PREDICTING STATES FROM PATIENT-CAREGIVER DYADIC BIOMARKER DATA USING ARTIFICIAL INTELLIGENCE
Organization Name
international business machines corporation
Inventor(s)
William Ogallo of Nairobi (KE)
Julian Bertram Kuehnert of Nairobi (KE)
Sekou Lionel Remy of Nairobi (KE)
Isaiah Mulang' Onando of Nairobi (KE)
PREDICTING STATES FROM PATIENT-CAREGIVER DYADIC BIOMARKER DATA USING ARTIFICIAL INTELLIGENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240105338 titled 'PREDICTING STATES FROM PATIENT-CAREGIVER DYADIC BIOMARKER DATA USING ARTIFICIAL INTELLIGENCE
Simplified Explanation
The patent application describes methods, systems, and computer program products for predicting states from patient-caregiver dyadic biomarker data using artificial intelligence.
- Obtaining biomarker data from patient-caregiver dyads
- Determining data-based representations of mental distress and/or social rhythm disruption
- Predicting mental distress and/or social rhythm disruption using artificial intelligence techniques
- Performing automated actions based on the predictions
Potential Applications
This technology could be applied in healthcare settings to improve patient care and caregiver support by predicting and addressing mental distress and social rhythm disruption.
Problems Solved
This technology helps in early detection and intervention for mental health issues in patients and caregivers, leading to better overall well-being and quality of life.
Benefits
The benefits of this technology include improved patient outcomes, reduced caregiver burden, and enhanced overall healthcare management for individuals with mental health challenges.
Potential Commercial Applications
A potential commercial application of this technology could be in healthcare software development for mental health monitoring and intervention, as well as in research settings for studying patient-caregiver dynamics.
Possible Prior Art
One possible prior art could be existing systems or methods for analyzing biomarker data in healthcare settings, but the specific application of predicting mental distress and social rhythm disruption in patient-caregiver dyads using artificial intelligence may be novel.
Unanswered Questions
How does this technology ensure data privacy and security for patient-caregiver dyads?
This article does not address the specific measures or protocols in place to protect the sensitive biomarker data of patient-caregiver dyads.
What are the limitations or potential biases of using artificial intelligence in predicting mental distress and social rhythm disruption?
This article does not discuss any limitations or biases that may arise from using artificial intelligence in predicting mental health states in patient-caregiver dyads.
Original Abstract Submitted
methods, systems, and computer program products for predicting states from patient-caregiver dyadic biomarker data using artificial intelligence are provided herein. a computer-implemented method includes obtaining biomarker data derived from one or more dyads, each dyad comprising at least one patient and at least one caregiver associated with the at least one patient; determining, based on processing the obtained biomarker data, data-based representations of mental distress and/or social rhythm disruption among at least one of the one or more dyads; predicting mental distress and/or social rhythm disruption among a given dyad of at least one patient and at least one caregiver associated with the at least one patient by processing input biomarker data, derived from the given dyad, using artificial intelligence techniques in connection with at least a portion of the data-based representations; and performing automated actions based on the predicting.
