In a collaboration project between Helsinki University Hospital (HUS), Kuopio University Hospital and Turku University Hospital (all Finland), a team of researchers have presented the first artificial intelligence (AI) based algorithm that has the potential to assist in treating patients with severe TBI in intensive care units (ICUs). Patients with the most severe cases of TBI are usually treated in ICUs, however, despite the high-quality care, recent observational studies have reported mortality rates of approximately 30%. Patients who suffer from severe TBI are unconscious, therefore, it is a challenge to accurately monitor their condition. In ICUs many tens of variables, such as intercranial pressure and mean arterial pressure, are continuously monitored to assess the patient's condition. One variable alone could yield hundreds of thousands of data points per day, making it impossible for ICU staff to fully analyze.
A recent Finnish study published in Scientific Reports presents the first artificial intelligence (AI)-based algorithm designed for use in intensive care units for treating patients with severe traumatic brain injury. The project is a collaborative project between three Finnish university hospitals: Helsinki University Hospital, Kuopio University Hospital and Turku University Hospital. Traumatic brain injury (TBI) is a significant global cause of mortality and morbidity with an increasing incidence, especially in low-and-middle income countries. The most severe TBIs are treated in intensive care units (ICU), but in spite of the proper and high-quality care, about one in three patients dies. Patients that suffer from severe TBI are unconscious, which makes it challenging to accurately monitor the condition of the patient during intensive care.
The accurate detection of disease outcomes still remains a challenging obstacle for physicians. As a result, machine learning (ML) has emerged as a popular tool for researchers. It can aid in discovering and identifying patterns and relationships from complex datasets, while predicting future outcomes. Now, researchers at Aalto University, the University of Helsinki, and the University of Turku in Finland report they have developed a machine learning model that can predict how combinations of different cancer drugs kill various types of cancer cells. The new AI model was trained with a large set of data obtained from previous studies, which had investigated the association between drugs and cancer cells.
Espoo: A team of researchers have developed a machine learning model that accurately predicts how combinations of different cancer drugs kill various types of cancer cells. The new AI model was trained with a large set of data obtained from previous studies, which had investigated the association between drugs and cancer cells. 'The model learned by the machine is actually a polynomial function familiar from school mathematics, but a very complex one,' says Professor Juho Rousu from Aalto University. The study was led by researchers at Aalto University, the University of Helsinki, and the University of Turku in Finland. The research results were published in the prestigious journal Nature Communications.
As dusk fell on the Finnish city of Lahti on a still chilly day in May 2016, a crew of workers let themselves into the yard of an empty daycare center. Underneath the swings and jungle gyms, they installed squares of forest floor--scruffy shrubs, shin-high berry bushes, wispy meadow grasses, and velvety mounds of moss--harvested from the woods somewhere in a less developed part of the country. Around the edges, they put in soft green sod. In the morning, when the children arrived, they found their playground--formerly a drab patchwork of asphalt, gravel, and sand--transformed overnight into micro-oases of wilderness. This scenario played out three more times that month at daycares in Lahti, and 500 miles to the west, in the city of Tampere.