Phony Neural Structures for Cardiac Care

A product that evolved out of Artificial Intelligence is Artificial Neural networks (ANN), often interchangeably referred to as Neural Networks. It is really a mathematical or computational model that processes interconnected data (artificial neurons) to locate a pattern because data. In this technique you have input data, that goes through a connectionist method of output data. The device adapts and learns through the great number of data that flows through it. The end result is a professional decision making, or even predicting system, with a near 100% accuracy. Small wonder, clinicians have been using AI and expert systems to offer better and timely healthcare for 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 have the ability to make use of this input data, and set up a relationship and pattern. This leaning phase was internalized by the system, and started identifying patients with abnormal ECGs with a 10% better accuracy than most clinicians/cardiologists on staff.

These are other factors in determining Heart Attacks, an appealing research work had been published in a scientific journal from the Inderscience group, the International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP) under the name “A computational algorithm for the danger 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 develop a computational algorithm that evolved out of a more current technique, namely Online Analytical Processing (OLAP).  heart hospital in hyderabad They used this methodology to create the foundations of a “Heart Attack Calculator” ;.The benefit of OLAP is so it supplies a multidimensional view of data, which allows patterns to discerned in a very large dataset, that would have been otherwise remained invincible. It requires into account numerous factors and dimensions, while making an analysis. The research team obtained data from about 1000 patients which were hospitalized because of symptoms of Acute Coronary Syndrome. This data included details on the family history, physical activities, body mass index, blood pressure, cholesterol, and diabetes level. This was then matched to a different pair 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 degrees of the factors, intelligence might be gathered to be used 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 an easy 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 contamination concerning the valves and occasionally the chambers of the center, that are often caused because of implanted devices in the heart. The mortality of as a result of infection might be as high as 60%. The diagnosis of such an infection required transesophageal echocardiography, which can be an invasive procedure involving the utilization of an endoscope and insertion of a probe down the esophagus. Obviously, this was a risky, uncomfortably and expensive procedure. The researchers at Mayo, fed the info from these 189 patients int the ANN, and had it undergo three separate “trainings” to understand to evaluate these symptoms. Upon being tested with different sample populations (only known cases, and then the overall sample of a variety of both known and unknown cases), the very best trained ANN could identify Endocarditis cases very effectively, thus eliminating the need for such an invasive procedure.

With present day e-health becoming more and more data centric, usage of relevant patient data is gradually becoming extremely convenient. AI and Expert systems having its ANN and computational algorithms, has tremendous opportunities to accelerate diagnosis, and effect patient care with speed and more and more accuracy. As AI advances, it will undoubtedly be interesting to see how it marks its footprints in Cardiovascular, Neuro, Pulmonary, and Oncology diagnosis and care.

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