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AI Helps Hospitals Priorities Patients for Urgent Intensive Care and Ventilator Support

By HospiMedica International staff writers
Posted on 23 Jan 2023
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Image: Researchers have developed an AI-enabled system for prioritizing pneumonia patient treatment (Photo courtesy of Swansea University)
Image: Researchers have developed an AI-enabled system for prioritizing pneumonia patient treatment (Photo courtesy of Swansea University)

Researchers have developed a ‘digital twin’ that can help hospitals to prioritize patients for urgent intensive care and ventilator support. The new innovative system could potentially allow patients to be seen more quickly and receive the most effective treatment based on data from previous pneumonia sufferers.

The three-tiered system developed by a research team at Swansea University (Swansea, UK) uses deep learning methods to build patient-specific digital twins to identify and prioritize critical cases among patients with severe pneumonia. A digital twin is a virtual representation (or computer program) of a real-world physical system or product – it is updated from real-time data, and uses simulation, machine learning and reasoning to aid in decision-making.

“A human digital-twin is a digital replica of a human system or sub-system. This replica is a personalized digital representation, in terms of structure or functioning or both, of an individual or patient’s system,” said Professor Perumal Nithiarasu, Author and Associate Dean for Research, Innovation & Impact in the Faculty of Science & Engineering. “A human digital-twin is a digital replica of a human system or sub-system. This replica is a personalized digital representation, in terms of structure or functioning or both, of an individual or patient’s system. It can provide real-time feedback on how a patient’s health is likely to vary based on their current known condition using periodic input data from the patient’s vitals (such as heart rate, respiration rate).”

“The proposed digital-twin is built on pre-trained deep learning models using data from more than 1895 pneumonia patients. Overall, results indicate that the prediction for ITU and mechanical ventilation prioritization is excellent,” added Professor Nithiarasu. “The data used to train the models is for non-COVID-19 patients with pneumonia. However, this model may be employed in its current form to COVID-19 patients, but transfer learning with COVID-19 patient data will improve the predictions.”

“The COVID-19 pandemic has put an unprecedented stress on an already strained healthcare infrastructure. This situation has forced healthcare providers to prioritize patients in critical need to access ITUs and mechanical ventilation,” explained Dr. Neeraj Kavan Chakshu, Co-Author and IMPACT Fellow. “In the case of COVID-19 (and in other similar forms of influenza), more precise and dynamically evolving system may be necessary to address the sudden increase in severity and the need for mechanical ventilation.”

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