Synthetic Sensory Systems within Cardiac Care

A type 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 discover a pattern because data. In this technique you have input data, that goes via a connectionist method of output data. The device adapts and learns through the great number of data that flows through it. The result is an expert decision making, as well as predicting system, with a near 100% accuracy. Small wonder, clinicians have been using AI and expert systems to provide 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 include 1,120 ECG records of Heart Attack patients, and 10,452 records of normal patients. The neural networks were found to be able to use this input data, and establish 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 interesting 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 produce a computational algorithm that evolved out of a far more current technique, namely Online Analytical Processing (OLAP). best heart hospital in hyderabad  They used this methodology to build the foundations of a “Heart Attack Calculator” ;.The benefit of OLAP is that it provides a multidimensional view of data, that enables patterns to discerned in an exceedingly large dataset, that would have been otherwise remained invincible. It requires into account numerous factors and dimensions, while making an analysis. The study team obtained data from about 1000 patients that have been hospitalized as a result of outward indications 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 some other group of similar multi dimensional data from several healthy individuals. All of this data were used as inputs to the OLAP process, to explore the role of these factors in assessing cardiovascular disease risk. At various degrees of the factors, intelligence might be gathered to be properly used as a combination 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 combination of multiple data dimensions or factors, to which a given situation could be in comparison 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 disease relating to the valves and sometimes the chambers of one’s heart, which are often caused as a result of implanted devices in the heart. The mortality of because of the infection might be as high as 60%. The diagnosis of this kind of infection required transesophageal echocardiography, that will be an invasive procedure involving the use of an endoscope and insertion of a probe down the esophagus. Obviously, this is 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 master to gauge these symptoms. Upon being tested with various sample populations (only known cases, and a overall sample of a combination of both known and unknown cases), the best trained ANN could identify Endocarditis cases very effectively, thus eliminating the necessity for this kind of invasive procedure.

With modern day e-health becoming more and more data centric, usage of relevant patient data is gradually becoming extremely convenient. AI and Expert systems with 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 going to be interesting to observe it marks its footprints in Cardiovascular, Neuro, Pulmonary, and Oncology diagnosis and care.


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