The common diagnoses you’ll hear in healthcare data analysis.
One canonical task in healthcare data analysis is to predict the diagnoses. If hospitals can predict it earlier, nurses and physicians can intervene preventatively to obviate the onset or deterioration of those diseases. We’ll look at a few common clinical conditions in this blog post to bootstrap ourselves with the medical concepts required in healthcare data analysis projects.
Note that this is a sister blog post to the 10 Significant Clinical Data Variables in Layman’s Terms.
1. Acute Renal Failure
Acute renal failure, aka acute kidney failure, aka acute kidney injury, occurs when the patient’s kidneys suddenly become unable to filter water products, which causes the waste level to build up in the body. It may be caused by impaired blood flow to the kidneys, damaged kidneys, or urine blockage in the kidneys. Acute renal failure can be fatal, but may be reversible. It requires intensive treatment.
2. Acute Cerebrovascular Disease
Acute cerebrovascular (blood vessels in the brain) disease is a category of diseases that pertain to the blood vessels in the brain. It could be blockage, clot formation, bleeding, or any other problems that affect the blood flow in the brain, which may result in stroke. Acute cerebrovascular disease can be caused by injuries, other diseases, and even just the accumulation of unhealthy lifestyle habits.
3. Acute Myocardial Infarction
Acute myocardial (muscle in the heart) infarction (blockage of blood supply), aka heart attack, is a life-threatening condition where the blood flow to the heart is somehow cut off. It’s usually caused by plaque buildup in the arteries that obstructs the blood flow.
4. Cardiac Arrhythmia (Dysrhythmia)
Cardiac arrhythmia is irregular heart beats. It may be too fast (tachycardia) or two slow (bradycardia). Its symptoms include pain, fatigue, anxiety, sweating, and so on. It could go away by itself, but it could also be a preamble to something serious.
5. Chronic Obstructive Pulmonary Disease