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Artificial intelligence reveals cardiac patients’ risk of complications

Text: Tarja Rannikko | Photo: Janne Ruotsalainen | 14.12.2017

Artificial intelligence can detect the risks posed to cardiac patients and prevent complications caused by heart disease at an early stage. The application improves patient safety, while creating savings in growing healthcare expenditure.

​Finland's patient population is well documented, but integrating such data has been difficult due to the multiple sources used. The databases cover different healthcare units, research and service operations located at a distance from where patient care is performed. This fragmented data has never been brought together as comprehensively as now.

Niku Oksala, an Associate Professor of Surgery at Tampere University Hospital (TAYS), Faculty of Medicine and Life Sciences, University of Tampere and project leader and clinician, has studied practical measurements and the management of clinical research under the Digital Health and Wellbeing project launched by VTT, the University of Tampere and the TAYS Heart Hospital.

As a physician, he is aware that cardiac patients are not served sufficiently well. Heart disease is a national epidemic in Finland and cardiac patients are at high risk of re-hospitalisation due to a serious complication of some kind. On these grounds, Oksala regards the project's focus on forming a cardiac patient database as justified.

Oksala describes a typical situation in a reception, where a doctor performing a diagnosis examines the patient data gathered by the healthcare unit in question; during a short appointment, there is no time to read the entire patient history.

– Huge amounts of scattered data are gathered on cardiac patients during the treatment process. We aim to collect data and make it mutually compatible so that analyses and new predictive models can be used to assess the patient's risk of further complications, such as arrhythmia or infarction, says Oksala.

Jussi Hernesniemi MD, PhD. Department of Cardiology and School of Medicine University of Tampere (left), Niku Oksala, an Associate Professor of Surgery at Tampere University Hospital (TAYS) (middle) and Kari Antila, a Senior Scientist and Project Manager at VTT use artificial intelligence developed by VTT in the home care of cardiac patients.

Patients at risk identified more easily than before

The study involves using artificial intelligence developed by VTT in the home care of cardiac patients. In practice, this means that a small ECG recorder is attached to the patient's chest upon leaving the hospital, which can also be connected to the Internet for monitoring purposes during the measurement period.

– Risk ratings are calculated based on the accumulated data. A patient with a high risk rating is given a monitoring device based on the new technology, which monitors cardiac function during the rehabilitation period, says Kari Antila, a Senior Scientist and Project Manager at VTT. He is also managing and supervising data analysis for the project.

The costs are far lower if a patient is invited in for treatment in good time, rather than only when the situation has reached an advanced stage.

– Our project enables the computer-aided collection of data based on the entire patient history and its comparison with TAYS's patient database covering almost a 100,000 individuals. This enables us to identify the risks concerning patients with a similar history to those in the comparison population, says Antila.

Systematic data analysis based on high technology

The documented patient database is internationally unique in terms of its scope and will provide significant benefits for many other, major ongoing studies.

– A systematic analysis is only possible with the help of high technology. No single cardiologist can do this, even with good experience of thousands of treated patients, Antila points out.

As yet, no practical assessment has been made of the project now under way, but further projects are already being planned. Antila describes the methods developed by VTT, which have been used to identify the risk of dementia, for example:

– Experience is available of a corresponding project, but tailoring is always needed in practice.

Most of our material is also applicable to other, interesting patient cases. Of course, the potency of the data has to be tested separately for each patient group.

The project to improve the safety of cardiac patients is also part of Tekes' 'Bits of Health' programme, which aims to turn Finland into a major digital health business environment and create internationally successful companies.

The project is being implemented in collaboration between the University of Tampere, VTT, Polytechnic University of Milan (Politecnico di Milano), TAYS Heart Hospital and the industrial partners GE Healthcare Finland Oy, Bittium Corporation, Clothing+ Oy and Fimlab Oy. It is being funded by Tekes' Bits of Health programme, the University of Tampere , VTT, GE Healthcare Finland Oy, Bittium Corporation and Fimlab Oy.

 

Health and quality of life through AI

An intelligent algorithm is helping to predict the risk of illness in employees, thereby even reducing sickness absences.

According to Finnish and EU-level studies, investments in the prevention of sickness pay themselves back two or threefold through reduced healthcare costs. The most cost-efficient way of improving quality of life and decreasing healthcare costs for both individuals and society is to promote the health of individuals and encourage them to take the initiative in reducing their health risks.

To meet this challenge, the healthcare technology company Odum Oy and VTT have developed an application, which guides individuals at risk to complete an electronic health checkup and take the initiative in preventing illnesses. Our aim is to decrease illness-related absences by 30 per cent among application users and add 10 healthy years to their lives.

– Based on an algorithm developed by VTT, we can predict the risk of illness among members of the working population over the next 12 months, with 80 per cent accuracy, says VTT's Mark van Gils, the scientific coordinator of the project.

– It took over five years to develop the algorithm, which required the expertise of VTT's research team and health data gathered by Odum on over 120,000 people of working age, says Jukka Suovanen, the CEO of Odum.

 

 

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