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Rapid assessment and outcome analysis for medical patients

A patient and outcome technology, applied in the field of rapid assessment and outcome analysis for medical patients, can solve the problem of complex order of treatment allocation

Active Publication Date: 2018-11-13
SIEMENS HEALTHCARE GMBH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Additional tests may be prescribed after physician's analysis, causing further delay and complicating ordering of treatments among multiple patients

Method used

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  • Rapid assessment and outcome analysis for medical patients
  • Rapid assessment and outcome analysis for medical patients
  • Rapid assessment and outcome analysis for medical patients

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Experimental program
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Embodiment Construction

[0014] The collection of data from patient images and measurements presents a very complex web of information about the patient. This complex information network can be effectively rectified by modern machine learning algorithms. Machine learning classifiers provide rapid patient assessment and outcome analysis. Modern machine learning and artificial intelligence algorithms are well suited to manage large amounts of heterogeneous data. Provide consistent forecasts in an automated fashion. Machine learning algorithms have excellent predictive power in complex tasks, thus demonstrating expert-level performance. Comprehensive patient assessment models combine all available information from the patient to present a complete understanding of the patient's state and enable clinicians to guide treatment.

[0015] Using heterogeneous data sources, machine learning classifiers automatically classify patient populations and highlight additional sources of information that, if collect...

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PUM

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Abstract

Machine learning (46) is used (26) to assess data for a patient in an emergency, providing (28) rapid diagnosis based on a large amount of information. Assistance in triage (30) may be provided. Giventhe large variety of patients and conditions that may occur, the machine learning (46) may rely on synthetically generated (42) images for more accurate prediction. The machine learning (46) may accurately predict (28) even with missing information and may be used to determine (34) what missing information for a given patient is more or less important to obtain.

Description

Background technique [0001] The current embodiments relate to medical diagnosis and / or prognosis in the emergency assessment of a patient. Every year, millions of patients with a wide variety of pathologies are examined in emergency departments. For such patients, a large amount of data is typically collected, including blood pressure measurements, ECGs, past medical history, symptom summaries, and imaging data such as computed tomography (CT), ultrasound, or magnetic resonance imaging (MRI) images. Patient treatment is then determined based on the information in this collection. This information needs to be processed as quickly as possible, especially to identify patients requiring urgent medical intervention. This process is currently handled manually, putting a lot of pressure on emergency departments. Analysis of such massive data collections may even delay triage or treatment in emergency situations. Additional tests may be ordered after the physician's analysis, caus...

Claims

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Application Information

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IPC IPC(8): A61B5/00G16H50/70G16H50/20
CPCA61B5/72A61B5/7271G16H50/20G16H50/70G16H50/30
Inventor S.拉帕卡L.M.伊图T.帕塞里尼P.沙尔马D.科马尼丘
Owner SIEMENS HEALTHCARE GMBH
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