ML Patient Condition Inference From Bed Sensor Data

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Solution Overview

Problem

Existing hospital equipment lacks the ability to autonomously determine a patient's condition, relying heavily on human caregivers, which is inefficient due to caregiver scarcity and patient overload, and intrusive for patients.

Innovation Solution

Implementing a machine learning compute device with circuitry to obtain and process sensor data from hospital beds, including load cells and pressure sensors, to infer patient conditions using trained inference models, potentially augmented by video data and electronic medical records, allowing for autonomous patient condition assessment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If human caregivers manually assess patient conditions, then patient condition determination is achieved, but caregiver workload increases and time consumption increases

Engineering Contradiction:

Improvepatient condition assessment efficiencyVSAvoidcaregiver time allocation

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patient support apparatus autonomously assesses patient conditions by processing sensor data through trained machine learning models, enabling the system to serve itself without requiring continuous human intervention for condition monitoring

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of manual visual inspection by caregivers with an automated sensing and machine learning-based assessment system that continuously monitors patient conditions through sensor data

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If caregivers continuously monitor patients, then patient condition detection accuracy improves, but caregiver availability decreases due to patient overload

Engineering Contradiction:

Improvepatient condition detection accuracyVSAvoidcaregiver monitoring capacity

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs self-monitoring by continuously processing sensor data from the patient support apparatus and generating condition assessments autonomously, eliminating the need for continuous human monitoring while maintaining high detection accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The automated system enables continuous monitoring of patient conditions without interruption, whereas human caregivers can only provide intermittent monitoring due to workload constraints

Inventive Principle:

Principle #20Continuity of useful action

3Loss of information

If manual patient assessment is performed, then patient condition information is obtained, but patient intrusiveness increases

Engineering Contradiction:

Improvepatient condition informationVSAvoidpatient comfort

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The patent replaces intrusive manual physical examinations with a non-intrusive automated sensing system that collects condition information through sensors integrated into the patient support apparatus, such as pressure sensors and load cells

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12548674B2Technologies for inferring a patient condition using machine learning

Publication Date: 2026.02.10 HILL ROM SERVICES INC

AI summary

A machine learning compute device may include circuitry configured to obtain sensor data from a product associated with a patient. The circuitry may also be configured to obtain response variable data indicative of an actual condition of the patient associated with the sensor data. Additionally, the circuitry may be configured to train, based on the response variable data and the sensor data, an inference model to infer the actual condition of the patient from the sensor data.