In the era of Surgical Science, it is definitely practical to have a second thought from arithmetic calculation of differential diagnoses for fail proof diagnosis exactly in the way augmented reality and/or virtual reality are used to assist an orthopaedic surgeon during execution of some surgeries. Big Data mining to improve medical diagnostics quality is a separate aspect while predictive assistance for medical imaging for the radiologists, orthopaedic surgeons do exits to some extent. Developing scalable and sustainable technologies for medical diagnosis automation and predictive modelling are critical to improve the quality of healthcare delivery NOT necessarily thinking about the cost. Electronic healthcare data visualization exists at present but commonly they are good for nothing. Only few are designed to be vendor agnostic, compatible in importing data from various sensor devices. Current system of Medical and Dental Education lacks any defined way to teach how to apply the arithmetic way to calculate and reach to diagnosis by a human. This definitely creates clinician to clinician variation of quality of diagnosis. Predictive Big Data Analytics & Medical Diagnosis Automation to Reach Precise Diagnosis Eliminating Human Bias is an Important Aspect of Medical Big Data. Biomedical Engineering and few medical specialities – Orthopaedics, Dentistry, Radiology takes part in development of medical IEEE Standards.
Current Definitions in Predictive Big Data Analytics & Medical Diagnosis Automation
In Japan, doctors have used artificial intelligence to diagnose a rare type of leukaemia and identify life-saving therapy faster than manual work. But such usage is obvious in 2016. We are not talking about probabilistic systems like Promedas. Clinical decision support system (CDSS) is well defined at present which is supported by working definition proposed. CDSSs constitute a major topic in artificial intelligence in medicine. In radiology and orthopaedics, computer-aided detection (CADe) are procedures which assists doctors in the interpretation of medical images. CAD systems help for typical appearances and to highlight conspicuous sections, such as possible diseases. Automated Medical Diagnosis is a different aspect which is in current usage, for example with Fuzzy Stochastic Models.
Real Hurdles of Predictive Big Data Analytics & Medical Diagnosis Automation
Adverse events during post-operative period including cardiac arrests and death are frequently preceded by several predictive features which can be used for data analysis. Such system actually worked by many developers :
We are not discussing about this aspect of modernised EHR, although they are quite important aspect. We expect that the patient monitors will receive upgrade (and will have Free Software compatible licence) to become capable of such basic works instead of fuzzy logic.
Sadly, universal format of diseases with sign, symptoms does not exist. Non-surgical medical science has still has hundreds of bias in only data collection. One easy example can be cardiology’s collection of data for hypertension. Data sets are based on devices without considering any parameter of material sciences or biomechanics. Rubber will have normal Young’s modulus, elasticity fatigue; they were not taken in to consideration. From point of view of data analytics, these values can influence the provisional diagnosis. On the other hand, processing, indexing medical images with common Big Data softwares like Apache Hadoop and Apache Solr as example is not a dream.
Predictive Big Data Analytics & Medical Diagnosis Automation : Need of IEEE Standard
To reach diagnosis we use arithmetic math like logical flow based on symptoms. Orthopaedics definitely need an IEEE Standard for saving data set against the symptoms of the diseases and disease. The symptoms of the diseases part which needs to be extracted from medical big data. If 2 million doctors reached the diagnosis of polymyositis for a data set, each point will carry some probability for being present or absent. That data needs update on regular basis as variation of symptoms across time is not abnormal. Knowledge graphs use JSON, they are similar. This project has created nice hierarchy :
Some log analysis software like Logstash probably can do the rest.
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