Machine-Learning Models Can Help Detect Sepsis in Newborns Earlier

The world can be a harsh place, particularly in the first few months after a baby is born. During those precious moments, a newborn is exposed to a flurry of new experiences and stimuli including, unfortunately, foreign bacteria. Sepsis, the result of a bacterial infection in the circulatory system, is a major cause of infant mortality even in developed nations.

Rapid diagnosis of ill babies is important but can be a challenge in hospitals due to ambiguous clinical signs and test inaccuracies. Now, researchers at the Children’s Hospital of Philidelphia (CHOP) have found that by feeding machine-learning models regularly collected clinical data, they could identify cases of sepsis in newborns hours before they usually would. The research team published its findings in the journal PLOS ONE.

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To develop machine-learning models capable of detecting sepsis, the research team trained algorithms on retroactive sets of data with the goal of identifying sepsis at least four hours before clinicians had suspected the illness.

Using electronic health record data, such as vital signs like blood pressure and temperature, from 618 infants in the CHOP neonatal intensive care unit from 2014 to 2017, the team trained eight machine-learning models to compare vital signs to 36 potential indicators of infant sepsis. Because the data was retroactive, the research team was able to compare the machine-learning models’ accuracy to clinical findings. Of the eight models, six were able to accurately identify cases of sepsis up to four hours earlier than clinicians had.

Dr.Algorithm
The team concluded that with additional data to train on the models could become even more accurate over time. “Because early detection and rapid intervention is essential in cases of sepsis, machine-learning tools like this offer the potential to improve clinical outcomes in these infants,” said Aaron J. Masino, lead author of the study. According to Masino, the team’s findings are a key step in developing a real-time tool for use in hospitals. By following up with more clinical studies the team plans to evaluate the effectiveness of such a system in the hospital setting.


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AI helps identify new breast cancer types

Using artificial intelligence (AI), researchers have distinguished five types of breast cancer, which were earlier lumped into one. Researchers applied AI and machine learning (ML) to gene sequences and molecular data from breast tumours, to reveal crucial differences among these cancer types.

According to researchers, two of them are more likely to respond to immunotherapy, one was more likely to relapse on tamoxifen. “We are at the cusp of a revolution in healthcare as we get to grips with the possibilities AI and ML can open up,” said study leader author .

“Our study has shown that AI can recognise patterns in breast cancer that are beyond the limit of the human eye, and to point us to new avenues of treatment among those who have stopped responding to standard hormone therapies,” the researcher said.

The majority of breast cancers develop in the inner cells that line the mammary ducts and are “fed” by oestrogen or progesterone. These are classed as ‘luminal A’ tumours and often have the best cure rates.

However, patients within the group respond differently to standard treatments, like tamoxifen, or new treatments — needed if patients relapse — such as immunotherapy.

The researchers applied the AI-trained computer software to a vast array of data available on the genetics, molecular and cellular make-up of primary ‘luminal A’ breast tumours, along with data on patient survival.

Once trained, the AI was able to identify five different types of disease with particular patterns of response to treatment.

Women with a cancer type labelled ‘inflammatory’ had immune cells present in their tumours and high levels of a protein called PD-L1, suggesting they were likely to respond to immunotherapies.

Another group of patients had ‘triple negative’ tumours, which don’t respond to standard hormone treatments but various indicators suggest they might also respond to immunotherapy.

Patients with tumours that contained a specific change in chromosome 8 had worse survival than other groups when treated with tamoxifen and tended to relapse much earlier. These patients may benefit from an additional or new treatment to delay or prevent late relapse.

The markers identified in the study don’t challenge the overall classification of breast cancer, but find additional differences within the current sub-divisions of the disease, with important implications for treatment.

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Machine learning could predict death or heart attack with over 90% accuracy

A study claimed that machine learning, modern bedrock of artificial intelligence, could predict death or heart attack with more than 90 per cent accuracy.

Machine learning is used every day. Google's search engine, face recognition on smartphones, self-driving cars, Netflix and Spotify recommendation systems -- all use machine learning algorithms to adapt to the individual user.


By repeatedly analysing 85 variables in 950 patients with known six-year outcomes, an algorithm 'learned' how imaging data interacts. It then identified patterns correlating the variables to death and heart attack with more than 90 per cent accuracy.

Study author said, "These advances are far beyond what has been done in medicine, where we need to be cautious about how we evaluate risk and outcomes. We have the data but we are not using it to its full potential yet."

Doctors use risk scores to make treatment decisions. But these scores are based on just a handful of variables and often have modest accuracy in individual patients.

Through repetition and adjustment, machine learning can exploit large amounts of data and identify complex patterns that may not be evident to humans.

The Dr. explained, "Humans have a very hard time thinking further than three dimensions (a cube) or four dimensions (a cube through time). The moment we jump into the fifth dimension we are lost. Our study shows that very high dimensional patterns are more useful than single dimensional patterns to predict outcomes in individuals and for that, we need machine learning."

The study enrolled 950 patients with chest pain who underwent the center's usual protocol to look for coronary artery disease.

A coronary computed tomography angiography (CCTA) scan yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing, and calcification. Those with scans suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were obtained from medical records including sex, age, smoking, and diabetes.

During an average six-year follow-up there were 24 heart attacks and 49 deaths from any cause. The 85 variables were entered into a machine learning algorithm called 'LogitBoost', which analysed them over and over again until it found the best structure to predict who had a heart attack or died.

"The algorithm progressively learns from the data and after numerous rounds of analyses, it figures out the high dimensional patterns that should be used to efficiently identify patients who have the event. The result is a score of individual risk," said the Dr.

"Doctors already collect a lot of information about patients. We found that machine learning can integrate these data and accurately predict individual risk. This should allow us to personalise treatment and ultimately lead to better outcomes for patients," added the Dr.

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Machine Learning Can Identify Kids Suffering From Developmental Disorder Due to Alcohol Exposure in Womb

Researchers have developed a new tool that uses Machine Learning (ML) technology to screen children suffering from a type of developmental and neurobehavioural disorder, caused due to alcohol exposure while in the womb, quickly and at an affordable rate.
It is estimated that millions of children will be diagnosed with foetal alcohol spectrum disorder (FASD). This condition, when not diagnosed early in a child's life, can give rise to secondary cognitive and behavioural disabilities.

FASD is still quite difficult to diagnose. A professional diagnosis can take a long time with the current work taking as much as an entire day.

But, the ML tool, developed by researchers, uses a camera and computer vision to record patterns in children's eye movements as they watch multiple one-minute videos, or look towards or away from a target. It then identifies patterns that contrast to recorded eye movements by other children who watched the same videos or targets.

The eye movements outside the norm were flagged by the researchers as children who might be at risk for having FASD and need more formal diagnoses by healthcare practitioners, according to study published in a journal. "There is not a simple blood test to diagnose FASD. It is one of those spectrum disorders where there is a broad range of the disorder. It is medically very challenging and it is co-morbid with other conditions," said Laurent Itti, Professor at the USC.

"The new screening procedure only involves a camera and a computer screen, and can be applied to very young children. It takes only 10 to 20 minutes and the cost should be affordable in most cases," added a researcher. While this computer vision tool is not intended to replace full diagnosis, it could provide important feedback so that parents can ensure that their children are seen by professionals and receive early cognitive learning and potentially behavioural interventions, the researchers noted. 

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