Artificial intelligence bridges the complexity of intractable medical problems
Authors: Becky McCall
Journal: The Lancet Digital Health (2020)

Drug-resistant infections show little sign of abating. Data from Public Health England show that antibiotic-resistant bloodstream infections have increased by one third since 2015. New economic analysis from Australia reports that urinary tract infections alone cost the national health system AU$909 million annually and could easily hit AU$1·6 billion by 2030, if nothing is done to challenge the rise of antimicrobial resistance. Like a host of other things, COVID-19 has exacerbated the problem. A rapid review and meta-analysis reported that 71·9% of patients with COVID-19 received antibiotics even when not clinically indicated, which can promote antimicrobial resistance. But antimicrobial resistance is far from alone in being an intractable, complex medical (and biological) problem. Heart disease, cancer, and even COVID-19 are just a few others that challenge science and clinical practice, largely because they are multifactorial in nature. Nestled within a web of interactions between genes and environment, the sheer enormity of data on the factors that drive these complex medical problems is insurmountable using conventional methods of analysis alone. However, as artificial intelligence (AI) becomes an ever more powerful tool, so it promises to help us understand both causal risk factors and predict outcomes that can change the course and burden of these diseases. “With complex health issues, there are many pieces of the ecosystem that must be interconnected otherwise profound gaps in the data prevent bridging between cause and effect,” says Dr Vincent Grasso, physician and Healthcare and Life Sciences Lead at US-based AI company, Amelia, an IPsoft company. “With multi-factorial diseases, if you treat the components in isolation then you miss the opportunity to address the overarching problem because they're interconnected.” Advances in AI promise to accommodate the sourcing, processing, and integration of data relating to the various component parts of a complex issue. “Only machine learning provides us with the opportunity to both ingest various disparate data sources and make relevant sense of the issue at stake,” adds Dr Grasso. Drug-resistant infections, and the antimicrobial-resistant bacteria from which they arise, epitomise the global health research challenge presented by a complex biological problem. Often referred to as a One Health problem, the bacteria that drive antimicrobial resistance are pervasive and inescapable, and interact across the human, animal, and environmental uses of antibiotics. Research to understand and tackle antimicrobial resistance is ongoing, but new solutions are in short supply. Advances in AI and machine learning might offer hope. AI acquires predictive capability from reams of data, so might account for things that were unknown or even considered unimportant in isolation. In addressing the complexity of the antimicrobial resistance problem, AI can power a system to source and integrate multiple datasets from genome sequences of bacteria, patient data, and prescribing behaviours through to land use, and wastewater-based epidemiology. Integrating and synthesising these various data can provide timely intelligence and insights on the localised emergence of drug-resistant infections. Outbreak is an Australian-based consortium and comprises a multidisciplinary team with an extensive range of expertise from genomics and AI to biosecurity and law. They are developing an AI-powered knowledge engine that draws on big data and many hundreds of diverse datasets to determine a location-specific and person-specific risk of drug-resistant infections. Some of these data come from unexpected sources, highlighting the novelty and wide scope of the influences on antimicrobial resistance, and acutely, the extent of the challenge faced. “We are drawing on the spread of drug-resistant genes via some unusual but relevant sources—so testing the droppings of the Australian Ibis, as well as other migrating birds, and connecting findings with drug-resistant bacteria in local health-care settings,” says Branwen Morgan, Associate professor at the University of Technology Sydney, Australia. “We're trying to find every dataset that has an impact on the dissemination and evolution of antimicrobial resistance,” she adds. “Data on climate change, for example, will affect antimicrobial resistance because different antibiotic-resistant bacteria persist under different conditions of rainfall or drought and so on, and some antibiotic residues will persist while others break down rapidly.” The project is ambitious but there is a mountain to climb in tackling the challenge of drug-resistant infections now and in the future. Data availability, format, and useability are a recognised stumbling block. “We suffer from a lack of standards around some types of data. We know how to convert centimetres to inches, but this isn't possible for many data forms,” Morgan notes. Despite the ability of AI to overcome the absence of standardisation through the predictive power of big data, many formats are unstructured and unworkable especially in the attempt to link data for humans, animals, and the environment. “Many human and animal drug prescriptions are written in free form, for example—this is a challenge,” she highlights. Echoing Morgan's thoughts, Sema Sgaier, director of the Surgo Foundation, Washington DC, USA, and Harvard-based adjunct assistant professor says, “getting good data can be difficult at times.” Surgo is using causal AI to dig deep into the myriad reasons that underpin people's behaviours. Causal AI poses the question asking why people act in a certain way and aims to uncover the causes for this behaviour and decision-making. By contrast, predictive AI uses data mining and probability to estimate specific, future outcomes. Sgaier says that obtaining trustworthy conclusions from causal AI relies on having accurate and representative data. “To train intelligent models we need high-quality data relating to the right populations, capable of merging with other datasets.” The Surgo Foundation have been active over the pandemic in using various predictive and causal AI tools to understand who is most vulnerable to COVID-19 and why, where the disease is spreading fastest, and how interventions, such as social distancing, are working. In the USA, adoption of social distancing has been patchy. In March 2020, the Surgo researchers surveyed 2500 US respondents to better understand the drivers of social distancing behaviour. Initially using a predictive system, the researchers found the AI did not reveal which variables were the crucial causal drivers of social distancing, but causal AI might. “Social distancing has drivers that are behavioural and structural,” says Surgo researcher, Grace Charles, who led the study. “For example, a financial driver such as having a financial cushion might allow people to social distance.” “Our analysis found that if people sought out information on coronavirus, they would be 1·9 times more likely to social distance than those who did not,” Charles explains. “If people were more worried about the virus they would be 1·4 times more likely to social distance.” Many complex diseases are challenging not only because of causal risk factors but because disease management needs to accommodate many drugs for the primary disease and for multiple comorbidities. Heart disease is a typical example and serves to inform other approaches to tackling complex problems. A group led by Peter Brunner-La Rocca, Professor of Cardiology at Maastricht University Medical Centre is running an AI-led project called PASSION-HF. It aims to develop a digital health decision-support system called ‘Abby’ (a virtual doctor) that will facilitate self-care for patients with heart failure. Abby incorporates a decision-support engine, an interactive physician avatar interface, serious gaming tools, a self-learning feedback system, and patient coaching. Around 98% of patients with heart failure have at least one additional chronic condition, and over half have five or more additional ones. Each comorbidity has its own plethora of drugs, and the complexity mounts. “AI could help significantly to unravel this mystery around the complexity of both the heart failure and the many drugs needed,” Brunner-La Rocca points out. “AI could also help to improve prediction models and to select the most appropriate approach to caring for these patients.” Ultimately, PASSION-HF wants to significantly lower costs, with an expected reduction in outpatient care of over 50%, Brunner-La Rocca highlights. “This will be achieved by enabling the patients to provide over 70% of their care without involvement of health-care professionals.” AI used to boost understanding and management of complex medical problems is at the cutting edge of new research methodology. PASSION-HF aims to have a prototype ready by spring 2021, after which they will start a large, prospective randomised controlled trial to prove superiority above standard of care. Outbreak is at the proof-of-concept stage, but it is gathering momentum to break new ground in the battle against drug-resistant infections. In many ways, AI used as a tool to understand complex disease, still has a lot to learn. Morgan draws an apt comparison. “Like a game of Jenga, we're trying to stack our data and find out what we can remove without the whole thing collapsing.”