From there they created three machine learning algorithm models (logistical regression, random forest and artificial neural networks) that mined the …

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.

LGI Flag, a machine learning-based solution developed by health care technology pioneer Medial EarlySign, will be implemented this month in SLUCare patient-care offices. The clinical risk identification tool will use ordinary medical data to help flag patients at greater risk of harboring lower GI disorders associated with chronic occult bleeding such as colorectal cancer, precancerous adenomas, polyps, irritable bowel disease, ulcers, and diverticulitis. The system flags patients using ordinary data, collected over the course of routine care, and sophisticated machine learning techniques, enabling health care providers to focus attention on patients who are most likely to benefit from further evaluation and possible intervention, review their charts, and determine next steps. "The ability to identify high-risk patients sooner and intervene with them earlier enables us to help improve care and long-term survival rates," said William Manard, MD, SLUCare Family and Community Medicine physician and Chief Medical Informatics Officer. "Earlier detection and treatments for lower GI disorders can also lead to improved and more manageable outcomes. This is an ideal way to use technology to deliver a higher quality of care for our patients in the greater St. Louis community."

A computer's ability to predict a patient's lifespan simply by looking at images of their organs is a step closer to becoming a reality, thanks to new research led by the University of Adelaide. The research, now published in the Nature journal Scientific Reports, has implications for the early diagnosis of serious illness, and medical intervention. Researchers from the University's School of Public Health and School of Computer Science, along with Australian and international collaborators, used artificial intelligence to analyze the medical imaging of 48 patients' chests. This computer-based analysis was able to predict which patients would die within five years, with 69% accuracy - comparable to'manual' predictions by clinicians. This is the first study of its kind using medical images and artificial intelligence.

Researchers have created an artificial intelligence program that can predict when patients will die from a heart disorder. It is hoped that the software could be used by doctors to make informed decisions about how to treat patients before a condition deteriorates. The software works by analyzing MRI scans taken of patients with pulmonary hypertension. It looks at over 30,000 points in the heart as it contracts on each heartbeat and then builds a virtual 3D heart of each patient. Coupled with eight years' worth of health records, the artificial intelligence was able to assess what aspects predicted when a patient was likely to die up to five years into the future.

Length of Stay (LOS) is a critical factor in managing hospital quality & economic outcomes in Healthcare. The metric is calculated by summing the total number of days for all discharges & dividing it by the total number of discharges. Insurance programs such as Medicare are moving to a model where they are compensating Hospitals the same amount for a specific surgery (e.g. Joint replacement) regardless of the number of days spent in the hospital. Therefore, hospitals & the overall healthcare ecosystem are motivated to reduce LOS.