Unnatural Neural Cpa networks throughout Cardiac Care
A type that evolved out of Artificial Intelligence is Artificial Neural networks (ANN), often interchangeably known as Neural Networks. It is just a mathematical or computational model that processes interconnected data (artificial neurons) to discover a pattern because data. In this process you’ve input data, that goes through a connectionist way of output data. The machine adapts and learns through the multitude of data that flows through it. The end result is a specialist decision making, or even predicting system, with a near 100% accuracy. Small wonder, clinicians have been using AI and expert systems to supply better and timely healthcare to their patients.
In a study through the late 1990s, researchers Lars Edenbrandt, M.D, Ph.D., and Bo Heden, MD., Ph.D., of the University Hospital, Lund, Sweden, ventured to add 1,120 ECG records of Heart Attack patients, and 10,452 records of normal patients. The neural networks were found to manage to utilize this input data, and establish a relationship and pattern. This leaning phase was internalized by the machine, and started identifying patients with abnormal ECGs with a 10% better accuracy than most clinicians/cardiologists on staff.
Speaking of other factors in determining Heart Attacks, a fascinating research work had been published in a scientific journal from the Inderscience group, the International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP) beneath the name cardiology hospital in hyderabad “A computational algorithm for the chance assessment of developing acute coronary syndromes, using online analytical process methodology” (Volume 1, Issue 1, Pages 85-99, 2009). Four Greek researchers had ventured to produce a computational algorithm that evolved out of an even more current technique, namely Online Analytical Processing (OLAP). They used this methodology to construct the foundations of a “Heart Attack Calculator” ;.The advantage of OLAP is so it provides a multidimensional view of data, that enables patterns to discerned in an exceedingly large dataset, that could have been otherwise remained invincible. It takes into account numerous factors and dimensions, while making an analysis. The investigation team obtained data from about 1000 patients which have been hospitalized because of outward indications of Acute Coronary Syndrome. This data included details on their family history, physical activities, body mass index, blood pressure, cholesterol, and diabetes level. This was then matched to some other group of similar multi dimensional data from several healthy individuals. All this data were used as inputs to the OLAP process, to explore the role of those factors in assessing cardiovascular disease risk. At various levels of the factors, intelligence could be gathered to be utilized as a variety of dimensions, for future diagnosis of the extent of risk.
The ANN is more a “teachable software”, that absorbs and learns from data input. When properly computed, even at a fast pace with a tried and tested algorithm, it develops patterns within the input data, or a variety of multiple data dimensions or factors, to which a given situation can be compared to, and a prognosis declared.
In 2009, some researchers in Mayo Clinic studied 189 patients with device related Endocarditis diagnosed between 1991 and 2003. Endocartitis is an infection relating to the valves and at times the chambers of the center, that are often caused because of implanted devices in the heart. The mortality of as a result of infection could be as high as 60%. The diagnosis of this infection required transesophageal echocardiography, that will be an invasive procedure involving the usage of an endoscope and insertion of a probe down the esophagus. Naturally, this was a risky, uncomfortably and expensive procedure. The researchers at Mayo, fed the information from these 189 patients int the ANN, and had it undergo three separate “trainings” to understand to gauge these symptoms. Upon being tested with various sample populations (only known cases, and a overall sample of a variety of both known and unknown cases), the most effective trained ANN was able to identify Endocarditis cases very effectively, thus eliminating the requirement for this invasive procedure.
With contemporary e-health becoming more and more data centric, access to relevant patient data is gradually becoming extremely convenient. AI and Expert systems using its ANN and computational algorithms, has tremendous opportunities to increase diagnosis, and effect patient care with speed and more and more accuracy. As AI advances, it is likely to be interesting to observe how it marks its footprints in Cardiovascular, Neuro, Pulmonary, and Oncology diagnosis and care.
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