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How AI is assisting to detect seniors suffering from dementia
Researchers from the National Centre for Healthy Ageing (NCHA), a partnership between Monash University and Peninsula Health, have developed a novel method for improving dementia detection of seniors in hospital by combining traditional methods with artificial intelligence (AI).
About 50 million people worldwide live with dementia, a number expected to triple by 2050, according to the World Alzheimer Report.
In Australia, there is an urgent need to substantially improve the methods for identifying people with dementia. Accurate identification is critical to understanding the size of the problem nationally, and to be able to effectively plan services. However, routine health data used for this purpose probably underestimates the number of people with dementia.
Regular healthcare contact and hospital visits provide an important opportunity to address this issue. Currently, in hospitals, dementia is recorded based on gathering of information in the medical records by medical coders, who find it difficult to look through the vast amount of written information in the records.
In a study involving more than 1,000 individuals aged 60 plus in Victoria’s Frankston-Mornington Peninsula area, algorithms using traditional data approaches with AI in electronic health records demonstrated high accuracy in identifying whether or not an older person may have dementia.
Supported by national health bodies, the initiative could transform how dementia is identified, counted for national estimates, and managed in healthcare settings.
Given the global rise in dementia cases and the difficulty in accurately identifying patients through conventional medical coding, this approach can transform the landscape in this medical field.
The research team based at Peninsula Health, involving NCHA’s healthy ageing data platform group and clinicians from Australia and the US, have tackled this problem using AI, and found that a particular type of AI called natural language processing (NLP) applied to written text in medical records significantly enhances dementia identification capacity.
Their peer-reviewed paper, Dual-Stream Algorithms for Dementia Detection: Harnessing Structured and Unstructured Electronic Health Record Data, showed that algorithms combining traditional methods with AI demonstrated very high accuracy for detecting the presence of dementia from information in electronic health records.
Lead author Taya Collyer says the study is based on people aged 60 plus with dementia diagnosed by specialists using gold standard methods, and a comparison group without dementia.
“Accessing high-quality curated electronic health records from our healthy ageing data platform helped assemble the data efficiently to address this problem. Special software was used to harness the large amount of free text data in a way that NLP could then be applied,” she says.
“We then developed dementia-finding algorithms through a traditional stream for usual structured data and an NLP stream for text records.”
For the traditional stream, in addition to standard codes for dementia, information was also obtained that reflected demographics, socio-economic status, medications, emergency and clinic health utilisation and in-hospital events such as confusion or distressed behaviour.
For the NLP stream, the team used clinical experts to guide the analysis to ensure its clinical relevance.
NCHA director and project lead Velandai Srikanth (pictured) says the future impact of this novel approach is exciting, not only for better determining the number people with dementia, but also for the efficient identification of people with high probability of dementia who may need care and support but who may be missed otherwise.
“Given that clinical recognition of people diagnosed with dementia presenting to hospitals is poor, using this new approach we could be identifying people earlier for appropriate diagnostic and clinical care. I am sure that many people are missing out on good care because we are not very good at identifying them or their needs.”
