A design that evolved out of Artificial Intelligence is Artificial Neural networks (ANN), often interchangeably referred to as Neural Networks. It is 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 way of output data. The system 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 provide better and timely healthcare to their patients.
In a study during 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 manage to use this input data, and establish a relationship and pattern. cardiology hospital hyderabad 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 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 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 far more current technique, namely Online Analytical Processing (OLAP). They used this methodology to construct the foundations of a “Heart Attack Calculator” ;.The benefit of OLAP is that it supplies a multidimensional view of data, which allows patterns to discerned really large dataset, that would have been otherwise remained invincible. It will take into account numerous factors and dimensions, while making an analysis. The research team obtained data from about 1000 patients that have been hospitalized as a result 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 was then matched to a different pair of similar multi dimensional data from a group of healthy individuals. All of 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 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 a quick 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 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 at times the chambers of the center, which can be often caused as a result of implanted devices in the heart. The mortality of due to the infection could possibly be as high as 60%. The diagnosis of such an 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 master to gauge these symptoms. Upon being tested with various sample populations (only known cases, and then the overall sample of a combination of both known and unknown cases), the best trained ANN surely could identify Endocarditis cases very effectively, thus eliminating the need for such an 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 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.