Newswise — Stroke is one of the most dangerous and commonly misdiagnosed medical conditions. Black and Hispanic people, women, Medicare seniors, and people in rural areas are less likely to be diagnosed in time for treatment to work. Using methods of learning and data available when patients were admitted to the hospital, we developed a model that predicts stroke more accurately than current models.
The study, by researchers at Carnegie Mellon University (CMU), Florida International University (FIU), and Santa Clara University (SCU), Journal of Medical Internet Research.
Diagnosis errors are a major public health problem, with preventable deaths from stroke due to such errors occurring more than 30 times more frequently than deaths from myocardial infarction. Diagnosing stroke is difficult because there are many conditions that mimic stroke, including seizures, migraines, and alcoholism. These issues can lead to delays and exacerbate health problems.
Automated screening tools that analyze available data and suggest stroke diagnoses have great potential to help address this problem. Scientists are using artificial intelligence and machine learning to identify hidden insights in large amounts of data and generate new patient predictions.
“By interpreting detailed data such as clinical records and imaging findings, machine learning techniques have been instrumental in stroke detection,” said co-author of the study, Heinz College of Management Science and Medicine, CMU. Rema Padman, Director of Informatics, explains: “However, when patients are first triaged in hospital emergency departments, such information may not be readily available, especially in rural or underserved areas.”
Padman and her colleagues sought to develop a data-based stroke prediction algorithm. that is Widely available during patient admission. They also assessed the added value of social determinants of health (SDoH) in predicting stroke. These include the circumstances in which people are born, grow up, live and age, and the factors that contribute to these circumstances.
Their study looked at more than 143,000 patient visits seen at acute care hospitals in Florida between 2012 and 2014. Researchers also looked at her SDoH data from the US Census’ American Community Survey. Their model includes data on basic demographics (age, gender, race, ethnicity), number of chronic conditions, and primary payers (Medicare, Medicaid, or Individuals), as well as the number of health care providers and payers on admission. It has built-in variables that you collect periodically. insurance).
The researchers’ model was accurate (84% accuracy in predicting stroke), sensitive, and outperformed existing scales (which tend to miss up to 30% of strokes). This model could be used to predict the likelihood of a patient’s condition to be stroke at hospital presentation based on the patient’s demographics and social determinants of health available at admission, prior to obtaining an imaging study. Suggested. Or laboratory test results, says the authors.
“The moderate sensitivity of existing models raises concerns that they miss a significant proportion of stroke patients,” says Min Chen, associate professor of information systems and business analytics at the FIU School of Management, co-author of the study. explains Mr. “In hospitals where medical resources and clinical staff are scarce, our algorithms will complement current models to help quickly prioritize patients for appropriate intervention.”
“Because our model does not require clinical records or diagnostic test results, it may be particularly useful in addressing the challenge of misdiagnosis when working with walk-in patients with mild and atypical symptoms.” Leavey School of Business analysis, another co-author of the study: “Emergency departments in small or non-stroke centers where providers have limited daily exposure to stroke and limited access to sensitive diagnostic tools. It could also be useful in rural areas where
Among the limitations of this study, the authors noted that because their study was retrospective, confirmation of stroke cases relied on the International Classification of Diseases Code and did not require review of patient records. They further caution that their algorithm should not be considered the gold standard for stroke diagnosis, but rather a model that complements existing stroke scoring systems used in hospitals. Finally, their findings are limited by the social determinants of health variables available in administrative data.
