Publications by authors named "Daniel Kleefisch"
Article Synopsis
- Scientists looked at health data from very sick patients with a condition called sepsis to see if machine learning can help predict who might survive better than using regular methods.
- They tested two machine learning methods using data from a big group of patients and found that these methods were much better at predicting survival than the standard way of checking changes in scores.
- The results showed that using daily scores from the first week could really help doctors know who might be in trouble, which could lead to better patient care in the future.
Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing.
View Article and Find Full Text PDFBackground: The COVID-19 pandemic has taken a toll on health care systems worldwide, which has led to increased mortality of different diseases like myocardial infarction. This is most likely due to three factors. First, an increased workload per nurse ratio, a factor associated with mortality.
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