The Centre for Data Analytics and Cognition (CDAC) at La Trobe University has teamed up with international cancer researchers to develop an artificial intelligence patient-reported information multidimensional framework (PRIME) to detect and analyse a patient's mental health status while undergoing cancer treatment.
According to CDAC director and La Trobe University head of analytics discipline, Damminda Alahakoon, using PRIME can help understand a patient's behaviour, emotions, and decision-making based on data shared by the patient.
He said the data can be text provided by a patient to an online chatbot, an online cancer support group, or other online support services.
"PRIME addresses the challenges associated with understanding the unlabelled and unstructured nature of this data, allowing it to efficiently identify trends and anomalies -- such as when a patient is struggling emotionally -- and effectively adapt to the changing nature of that data," he said.
See also: 5 practical reasons to embrace artificial intelligence (TechRepublic)
Alahakoon said PRIME can help health professionals better monitor their patient.
"PRIME can be that crucial first step in identifying a patient's mental health needs and alerting health professionals to those who are at high risk if untreated," he said.
PRIME was developed jointly with a team of oncology urologists treating prostate cancer patients at the Austin Hospital in Melbourne, and later with psycho-oncology researchers from the University of Toronto and De Souza Institute in Canada.
Austin oncologist Damien Bolton said a lack of insight into emotional distress of cancer patients meant most clinical decisions are based on research trials, rather than individual patient needs.
"Men use online support group environments to address their unmet needs, express emotions, and voice their worries freely," he said. "PRIME can help analyse the outcomes of those patient-reported information."
The first study carried out by using PRIME, published in PLOS One, analysed 609,960 conversations from 22,233 patients, which comprised of 93,606,581 words.
In hope that it will relieve some pressure off the healthcare system.
Together they have developed remote data monitoring technology.
In initial testing, the AI model performed better than human experts.
The funding will dispersed via grants through the federal government's Medical Research Future Fund.
Leveraging Google Cloud to identify associations between microorganisms and genes to predict different disease states.