A single blood sample from a critically ill COVID-19 patient can be analyzed by a machine learning model which uses blood plasma proteins to predict survival, weeks before the outcome, according to a new study published this week in the open-access journal PLOS Digital Health by Florian Kurth and Markus Ralser of the Charité – Universitätsmedizin Berlin, Germany, and colleagues. Healthcare systems around the world are struggling to accommodate high numbers of severely ill COVID-19 patients who need special medical attention, especially if they are identified as being at high risk. Clinically established risk assessments in intensive care medicine, such as the SOFA or APACHE II, show only limited reliability in predicting future disease outcomes for COVID-19. In the new study, researchers studied the levels of 321 proteins in blood samples taken at 349 timepoints from 50 critically ill COVID-19 patients being treated in two independent health care centers in Germany and Austria. A machine learning approach was used to find associations between the measured proteins and patient survival.

Levels of 14 proteins in the blood of critically ill COVID-19 patients are associated with survival. A single blood sample from a critically ill COVID-19 patient can be analyzed by a machine learning model which uses blood plasma proteins to predict survival, weeks before the outcome, according to a new study published this week in the open-access journal PLOS Digital Health by Florian Kurth and Markus Ralser of the Charité – Universitätsmedizin Berlin, Germany, and colleagues. Healthcare systems around the world are struggling to accommodate high numbers of severely ill COVID-19 patients who need special medical attention, especially if they are identified as being at high risk. Clinically established risk assessments in intensive care medicine, such as the SOFA or APACHE II, show only limited reliability in predicting future disease outcomes for COVID-19. In the new study, researchers studied the levels of 321 proteins in blood samples taken at 349 timepoints from 50 critically ill COVID-19 patients being treated in two independent health care centers in Germany and Austria.

Disclaimer: I'll be talking mainly about logistic-regression and basic feed-forward neural networks, so its helpful to have programmed with those 2 models before reading this piece. OK -- before statisticians and ML folks come running after me after reading the title, I'm not talking about linear regression, for example. Yes, in linear regression, you can use the R-squared (or adjusted R-squared statistic) to talk about explained variance, and since linear regression only involves addition between independent variables (or predictors), they're pretty interpretable. If you were doing a linear regression to predict, say the price of a car Car_Price, based on the number of seats, mileage, maximum-speed, and battery life, your linear model could be –– say Car_Price c1*Seats c2*Mileage c3*Speed c4*Battery_Power –– the fact that variables are only added makes it pretty interpretable. But when it comes to more complex prediction models like Logistic Regression and neural networks, everything about the predictors (or called "features" in ML) becomes more confusing.

Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients’ survival from their data and can individuate the most important features among those included in their medical records. In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients’ survival. This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

The artificial intelligence software, created by researchers at Imperial College London and the University of Melbourne, has been able to predict the prognosis of patients with ovarian cancer more accurately than current methods. It can also predict what treatment would be most effective for patients following diagnosis. The trial, published in Nature Communications took place at Hammersmith Hospital, part of Imperial College Healthcare NHS Trust. Researchers say that this new technology could help clinicians administer the best treatments to patients more quickly and paves the way for more personalised medicine. They hope that the technology can be used to stratify ovarian cancer patients into groups based on the subtle differences in the texture of their cancer on CT scans rather than classification based on what type of cancer they have, or how advanced it is.

The trial, published in Nature Communications took place at Hammersmith Hospital, part of Imperial College Healthcare NHS Trust. Researchers say that this new technology could help clinicians administer the best treatments to patients more quickly and paves the way for more personalised medicine. They hope that the technology can be used to stratify ovarian cancer patients into groups based on the subtle differences in the texture of their cancer on CT scans rather than classification based on what type of cancer they have, or how advanced it is. "The long-term survival rates for patients with advanced ovarian cancer are poor despite the advancements made in cancer treatments. There is an urgent need to find new ways to treat the disease. Our technology is able to give clinicians more detailed and accurate information on the how patients are likely to respond to different treatments, which could enable them to make better and more targeted treatment decisions."

The artificial intelligence software, created by researchers at Imperial College London and the University of Melbourne, has been able to predict the prognosis of patients with ovarian cancer more accurately than current methods. It can also predict what treatment would be most effective for patients following diagnosis. The trial, published in Nature Communications took place at Hammersmith Hospital, part of Imperial College Healthcare NHS Trust. Researchers say that this new technology could help clinicians administer the best treatments to patients more quickly and paves the way for more personalised medicine. They hope that the technology can be used to stratify ovarian cancer patients into groups based on the subtle differences in the texture of their cancer on CT scans rather than classification based on what type of cancer they have, or how advanced it is.