Fake Neural Companies during Cardiac Care
A model that evolved out of Artificial Intelligence is Artificial Neural networks (ANN), often interchangeably called Neural Networks. It is really a mathematical or computational model that processes interconnected data (artificial neurons) to find a pattern because data. In this technique you have input data, that goes through a connectionist method of output data. The system adapts and learns through the multitude of data that flows through it. The effect is an expert decision making, as well as predicting system, with a near 100% accuracy. Small wonder, clinicians have now been using AI and expert systems to supply better and timely healthcare to their patients.
In a study throughout 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 be able to utilize 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, a fascinating research work have 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 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 a far more current technique, namely Online Analytical Processing (OLAP). They used this methodology to build the foundations of a “Heart Attack Calculator” ;.The advantage of OLAP is so it supplies a multidimensional view of data, that enables patterns to discerned in a very large dataset, that would have been otherwise remained invincible. It takes into consideration numerous factors and dimensions, while making an analysis. The research team obtained data from about 1000 patients that have been hospitalized because of symptoms of Acute Coronary Syndrome. This data included details on their family history, physical activities, body mass index, blood pressure, cholesterol, and diabetes level. This is then matched to some other pair of similar multi dimensional data from a group of healthy individuals. All this data were used as inputs to the OLAP process, to explore the role of these factors in assessing cardiovascular disease risk. At various quantities of the factors, intelligence could possibly 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 confirmed 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 involving the valves and occasionally the chambers of one’s heart, that are often caused because of implanted devices in the heart. best cardiology hospital in hyderabad The mortality of as a result of infection could possibly be as high as 60%. The diagnosis of this infection required transesophageal echocardiography, which is 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 learn to judge these symptoms. Upon being tested with different sample populations (only known cases, and then the overall sample of a combination 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 current day e-health becoming more and more data centric, access to relevant patient data is gradually becoming extremely convenient. AI and Expert systems with 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 is likely to be interesting to see how it marks its footprints in Cardiovascular, Neuro, Pulmonary, and Oncology diagnosis and care.