Neural network for elderly care could save millions

Illustration by Matti Ahlgren / Aalto University

A deep neural network model helps predict healthcare visits by elderly people, with the potential to save millions.

If healthcare providers could accurately predict how their services would be used, they could save large sums of money by not having to allocate funds unnecessarily. Deep learning artificial intelligence models can be good at predicting the future given previous behaviour, and researchers based in Finland have developed one that can predict when and why elderly people will use healthcare services.

Researchers at the Finnish Centre for Artificial Intelligence (FCAI), Aalto University, the University of Helsinki, and the Finnish Institute for Health and Welfare (THL) developed a so-called risk adjustment model to predict how often elderly people seek treatment in a healthcare centre or hospital. The results suggest that the new model is more accurate than traditional regression models commonly used for this task, and can reliably predict how the situation changes over the years.

Risk-adjustment models make use of data from previous years, and are used to allocate healthcare funds in a fair and effective way. These models are already used in countries like Germany, the Netherlands, and the US. However, this is the first proof-of-concept that deep neural networks have the potential to significantly improve the accuracy of such models.

‘Without a risk adjustment model, healthcare providers whose patients are ill more often than average people would be treated unfairly,’ Pekka Marttinen, Assistant Professor at Aalto University and FCAI says. Elderly people are a good example of such a patient group. The goal of the model is to take these differences between patient groups into account when making funding decisions.

According to Yogesh Kumar, the main author of the research article and a doctoral candidate at Aalto University and FCAI, the results show that deep learning may help design more accurate and reliable risk adjustment models. ‘Having an accurate model has the potential to save several millions of dollars,’ Kumar points out.

The researchers trained the model by using data from the Register of Primary Health Care Visits of THL. The data consists of out-patient visit information for every Finnish citizen aged 65 or above. The data has been pseudonymized, which means that individual persons can not be identified. This was the first time researchers used this database for training a deep machine learning model.

The results show that training a deep model does not necessarily require an enormous dataset in order to produce reliable results. Instead, the new model worked better than simpler, count-based models even when it made use of only one tenth of all available data. In other words, it provides accurate predictions even with a relatively small dataset, which is a remarkable finding, as acquiring large amounts of medical data is always difficult.

‘Our goal is not to put the model developed in this research into practice as such but to integrate features of deep learning models to existing models, combining the best sides of both. In the future, the goal is to make use of these models to support decision-making and allocate funds in a more reasonable way,’ explains Marttinen.

The implications of this research are not limited to predicting how often elderly people visit a healthcare centre or hospital. Instead, according to Kumar, the researchers’ work can easily be extended in many ways, for example, by focusing only on patient groups diagnosed with diseases that require highly expensive treatments or healthcare centers in specific locations across the country.

The research results were published in the scientific publication series of Proceedings of Machine Learning Research.

Further information
Yogesh Kumar
Doctoral Candidate
Aalto University, Finnish Centre for Artificial Intelligence
yogesh.kumar@aalto.fi

Pekka Marttinen
Assistant Professor
Aalto-yliopisto, Finnish Centre for Artificial Intelligence
Phone +358 50 5124362
pekka.marttinen@aalto.fi

When physician and AI work together, the patient benefits

The machine learning method by Iiris Sundin and her colleagues taken into account uncertainties of the world. This is important in decision-making. Photo Matti Ahlgren / Aalto University

Doctoral student Iiris Sundin learned in her studies that a machine learning model could make use of a physician's silent knowledge which usually is never written down. This kind of model predicts best how a given patient will react to specific treatment.

Artificial Intelligence opens up new avenues into health care, for example, but its potential is as of now not fully put to use. There are many reasons, but the most important one became clear to Iiris Sundin as she was starting her doctoral studies on machine learning: machine and Man must learn to work together.

"When I acquainted myself with the research of my advisor, Professor Samuel Kaski, on user modeling where the machine tries to understand the human, I realized the huge potential. Cooperation can mean other things than the human operating Excel or passively staring at projections on the screen," says Sundin, a doctoral student at the Department of Computer Science at Aalto University.

Samuel Kaski is the director of FCAI. Sundin's research combines Medicine, technology and many other interests of hers. She has also always wanted to work at something that benefits other people. On the other hand, she was interested in mathematics and programming.

The role of AI and machine learning in health care is researched a lot. Sundin's point of view is unique in having the machine make use of the doctor's knowledge to define the best possible care for the patient.

There are uncertainties in the world that must factor in when decisions are taken

Physicians have vast quantities of knowledge that is never written down and that is impossible to feed directly into a learning algorithm. Sundin and her colleagues have found out just how the machine could make use of such knowledge in e.g. figuring out the efficacy of a given type of medication.

Sundin and her colleagues have at their disposal, for example, data on gene specimens collected from cancerous cells, courtesy of FIMM, Institute for Molecular Medicine Finland. Researchers took a look at the mutations in the specimens and tried to predict which cancer medicine would work best for each patient.

We don't just discuss the average person, we acknowledge there are differences, and take it into account when reaching decisions.

It is crucial to remember that there are always uncertainties in the world, and thus, also a physician's knowledge of the effect of different mutations can be uncertain. The machine learning model devised by the research group takes this into account. The results show including expert knowledge in machine learning models and emphasizing data improve prognoses on how a given patient will react to a particular treatment.

So the model developed by researchers depicts realistically how sure people are of the different properties of real matters. "I would that such thinking were more common: we don't just discuss the average person, we acknowledge there are differences, and take it into account when reaching decisions. That way, things can be modeled in a more useful manner."

The researcher gets to tell something new about the world

Even though Sundin was fond of mathematics already as a child, and it comes naturally to her to see the world via mathematical thinking, art held a big role in her life, especially when she was young.

As a schoolgirl, she played the piano, sang in a choir, read books and attended art school. Now, she is into yoga, air acrobatics and camping. A researcher can be interested in many things, and creativity, for example, helps at work, too. "When you e.g. make posters for a conference, it is very useful if you can do some of the visual elements yourself."

The antics of engineering students were something Sundin grew up with. Both her parents graduated from the Helsinki University of Technology, and the family always celebrated First of May with parents’ college pals. All the adults sported the traditional tassel caps of engineering students. So engineering studies came naturally. "The degree of an engineer is really well-rounded and offers a good start. You can choose to be a researcher, industrial work or pretty much anything."

I realized researching was such great fun that I rather do more of the same.

Sundin completed her Master’s in Automation and System Technology. Although she never fancied herself a researcher, her interest in it was kindled when she was working on her dissertation. Sundin was modeling the properties of a drop of fluid on different surfaces.

Research offered a way to bring received mathematical ways of thinking and skills into the real world, and to tell something about that world. "I realized it was such great fun that I rather do more of the same."

”You succeed at work if you are open”

Sundin says she sometimes imagined researchers worked alone in their chambers. That is no longer the case, anyway. "You succeed better at work is you are open, socially adept and like to travel to conferences and network with the people there."

Research work is social. "You succeed better at work if you are open, socially adept and like to travel to conferences and network with the people there,” says Iiris Sundin. Photo Matti Ahlgren / Aalto University

It is crucial for a doctoral student to possess the wish and motivation to understand things. The point of further studies is to delve into one fairly narrow area of learning very profoundly, something which can be demanding. Therefore, it is important to have tenacity and the ability to stick to it. "It is needed, if you want to make it to the end."

Even though Sundin has hitherto worked in basic research, she feels the methods developed need to be taken to a practical level, once the foundation laid by basic research is robust enough.

In the future, she hopes to apply what she has learned and continue work for the benefit of people and also the environment. "I hope to work at some Finnish institute doing research that help Finland make smarter decisions when it comes to the environment."

Authored by Anu Haapala
English translation by Susanna Bell

Deep learning model developed by Finnish AI researchers detects diabetic eye diseases accurately

Professor Kimmo Kaski. Photo Aalto University

Finnish AI researchers have developed a deep learning system that shows great potential in detecting diabetic eye diseases, which could facilitate doctors’ work and decrease healthcare expenses.

According to the research findings published in Nature Scientific Reports, the deep learning model detects the severity grade of diabetic retinopathy and macular edema accurately. Diabetic retinopathy is one of the most common comorbidities of diabetes that, if untreated, may lead to severe vision loss. Macular edema refers to swelling under a specific part of the retina caused by diabetic retinopathy.

The deep learning model identified referable diabetic retinopathy comparably or better than presented in previous studies, although only a very small data set was used for its training. The model turned out to be more accurate in identifying diseases when the training images of patients’ fundus were of high quality and resolution.

Results suggest that such deep learning system could increase the cost-effectiveness of screening and diagnosis and that the system could be applied to clinical examinations requiring finer grading.

Currently, retinal imaging is the most widely used method for screening and detecting retinopathy, and medical experts evaluate the severity and the degree of retinopathy in people with diabetes based on the fundus or retinal images of the patient’s eyes.

As diabetes is a globally prevalent disease and the number of patients with diabetes is rapidly increasing, also the number of retinal images will increase, which in turn introduces a large labor-intensive burden on the medical experts as well as cost to the healthcare. An automated system that would either assist medical experts or work as a full diagnostic tool could alleviate the situation.

The research group consisted of researchers from Aalto University Department of Computer Science, Digifundus Ltd – a Finnish provider of diabetic retinopathy screening and monitoring services –, and Central Finland Central Hospital.

Link to the research article: https://www.nature.com/articles/s41598-019-47181-w

Further information
Kimmo Kaski
Professor, Computational Science
Finnish Center for Artificial Intelligence (FCAI)
Aalto University Department of Computer Science
kimmo.kaski@aalto.fi