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Every time when we need to make a decision, we tend to base it on those belief i.e |
our attitude towards the world at that given time |
Time is a very important dimension here because with time we update our beliefs |
Although the ability to always search for the motivation to change or update self-beliefs are important for us to make a better decision yet human factors like ego, blind superstition, etc comes as a tyrannical force to prevent us from changing or updating our self-beliefs |
The idea of updating beliefs in light of new evidence have also become an important part of the scientific process, Karl Popper refers this kind of occurrence as the Idea of Falsification |
For instance, our previous view regarding the form of the Earth was that it was flat, but with the advent of new scientific tools, we can today conclude that it is roughly a sphere |
The point is that even if the world was thought to be flat at the time, we nevertheless built roads, railroads, and other infrastructures given that the earth was flat |
Therefore, we cannot hold individuals from the past who held this notion to be true responsible; as it was still a useful belief at that given time |
And later we updated our beliefs, and maybe in the future from a different dimension (if exists) Earth’s form can be different from the sphere |
Updating Beliefs about Ghost!! (Just a toy example) Scenario Assume you are moving into a new house; this is how the opening scene of the majority of horror movies start |
You had some strange experiences and made the decision to keep watch every night for a few days to see what would happen |
We are going to start with our prior belief that we had about the ghost, incorporate the evidence we collect everyday and form new opinions about the existence of the ghost |
We can reflect these prevailing notions mathematically; Bayesian formula: Quantifying and Updating Beliefs If you peek at the right side of our equation; this side incorporates both our existing beliefs and the likelihood of the evidence (data); is our existing belief on which is called a Prior and P( is our likelihood... |
Thus the left side P( )is our updated belief on |
For instance, back to our example; we can assume the parameter as the probability of the ghost |
Using Bayesian approach The unknown probability of ghost( ) would be treated as a random variable and given a distribution, but we already have some existing beliefs on the presence of ghosts |
In bayesian Statistics they are known as Prior and can be expresses as a distribution; Let’s plot some of the Prior beliefs before we discuss its underlying distributions |
Figs: Prior Distributions The above plots are some of our prior distributions which will be explained below, the shaded region indicates that the mass of probability is in that region |
Prior from Fig 1 (first from the left)** implies that I am new to the planet or that I have no awareness of these phenomena because no one has ever told me about them |
The first prior is hence neutral and any chances on probability of ghost from 0 to 1 is equally likely |
However, we can do better because we all have some beliefs about the ghost; the rest of these priors suggest that we all hold certain beliefs about ghosts and their existence |
Prior from Fig 2 (second from the left)** suggests that I am more likely to believe that ghosts don’t exist; there are, however, some chances |
Similarly, Prior from Fig 3 suggests that there is a 50/50 possibility of ghost |
Prior from Fig 4 suggests that “I have greater beliefs on ghosts,” ; it’s like saying I believe in Ghost but i have not meet one |
Note: This is just a part of an imaginary exercise; if you are doing a generalizable study you would want to use priors based on some scientific theory or an existing experiment |
Or any other priors depending upon the type of your study |
Bayesian Updating Moving on with “I kind of don’t believe in ghosts”(second from the left) |
This suggests that the our prior belief on absence of ghosts ( =0) is extremely likely and existence of ghosts ( =1) is extremely implausible |
This does not indicate that there is no chances of ghost, just because the absence of ghosts ( =0) is quite likely |
It only suggests that the existence of ghosts ( =1) is exceedingly improbable (see the figure above; it still has some mass around its tail i.e towards 1) |
Let’s now enter the monitoring phase and begin gathering and documenting each of our paranormal encounters |
Every day is like flipping a coin (Bernoulli trials): if we experience some paranormal activity, we record 1; otherwise, we record 0 |
See the mathematical instructions at the end of the article |
Fig: Updating our Beliefs We begin with our preexisting belief about the ghost; if we experience any paranormal experiences, such as discovering someone under the bed, we then shift our beliefs to the right ( =1); But since it is a bayesian shift we obtain the full distribution i.e the entire mass of the probability |
For instance, on Day 2, the dashed distribution represents our prior distribution whereas the solid-line distribution represents our updated belief |
But an intriguing fact is that we will use this updated distribution on day 2 as our prior distribution on day 3 while we monitor the data and revise our opinions |
Change In beliefs The process of updating continues until day 9, which is the present |
Using the same Bayesian formula we previously encountered, we compute our updated distribution for each day |
The updated distribution are called the posterior distribution |
Fig: Updated Beliefs The black dotted distribution reflects our initial prior belief regarding ghosts; however, the black solid line shows our current beliefs, which lean more toward a 50/50 chance of ghost |
Our understanding of the ghost has been updated as a result of our experience, and based on the updated distribution (posterior distribution), it appears that it is now time to call the priests |
These systems for updating beliefs are hardwired into our brain |
This is how we form an opinion on something too |
The more strongly we feel about some opinion, the more difficult it is to change our minds |
Even though this occurs automatically inside our brains, teaching it to our computers—is a little more tricky |
You can use any programming language of your choice but the underlying mathematics are the same and are explained next |
Math Section Although there are many methods for computing posterior distribution (updated belief), at its heart, it just uses the Bayesian formula that we had previously encountered |
We could use different approaches like MCMC, analytical solutions and quadratic approximations depending upon the complexity of the problem |
Here, we are computing the posterior, or the updated beliefs, using an analytical solution (Beta-Bernoulli Conjugacy) |
Since everything that can be expressed mathematically can be coded into computers, we will attempt to express our beliefs through mathematical expressions |
Beta-Bernoulli conjugacy Using the Bayesian Formula; We are interested in which is our posterior distribution i.e probability of (\theta) that there is a ghost given the data (X) where X can take value either 0 or 1 |
Defining our Prior and Likelihoods: is a constant and it does not depend on we can remove it from the equation Now we define our likelihood; where X is the random Variable that can take values X can take values either of Since, we have everything we need; plugging the values in above formula; let, and ; using proportio... |
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At Wiseyak, we focus on building platforms to make healthcare accessible to anyone and anywhere |
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Wiseyak Paradigm Shift in Medical Diagnosis: The AI-Frontier Wiseyak 2 years ago Spread the love The Early Paradigm of Healthcare The paradigm shift in medical diagnosis of AI was inevitable |
The healthcare system changed tremendously in the last century |
In the early 1900s, a physician treatment was only possible at a patient’s home |
The very few hospitals that existed provided minimum care |
Also, medical science and technology were so primitive that physicians could not treat accurately and properly most of their patients, leaving them misdiagnosed or even leaving them to die |
Fortunately, physicians became more effective thanks to the advances of the medical devices |
Doctors could use stethoscopes, X-rays, ultrasound, CT and MRI scanners for diagnosis and treatments |
Despite the innovation of computer 1950s, the real application of computer in medical science was still a far-fetched dream until 1970s when some Artificial Intelligence Researchers developed systems that could automate a human physician diagnosis |
Unfortunately, since the system was found to provide a random diagnosis to the patient without any explanation, it was not approved by the medical community at that time resulting in a very little or no adoption of computer technologies in medical diagnosis |
Paradigm Shift in Medical Diagnosis The 2010s can be considered as an inflexion point in the adoption of Artificial Intelligence (AI) medical diagnosis and Clinical Decision Support (CDS) |
Over the past few years with proven use of cases, scientists and medical communities have acknowledged the real application of AI in medical diagnosis |
However, the application and chances of AI to succeed in any domain largely depends on the volume and availability of data through which AI scientists can develop mathematical and statistical models to teach and train the machines for any future predictions or medical diagnosis |
Using trained models and data analysis, AI can offer potential medical diagnosis better and quicker than physicians |
In the healthcare industry, intelligent systems are used much more often than it used to be, especially in cancer diagnosis and treatment |
AI can help save more lives due to early detection and accuracy of diagnosis |
For example, according to the study from the University of Central Florida, engineers developed an AI algorithm that could accurately detect lung cancers |
In general, lung cancer can be seen through a CT scanner |
Hence, they fed their AI system with 1000 CT scans to check if they could find defects in patients’ lung tissues |
The study revealed that the algorithm was 30 per cent more accurate than a human doctor would have been |
Similarly, at the Imperial College of London, an AI system can give the best treatment strategy for a patient with sepsis and at John Radcliffe Hospital in Oxford, they use AI to detect heart defects as well as lung cancer |
In addition, another study concluded promising results in their prediction of cancer survival rate |
The AI tool could predict the survival rate of current ovarian cancer patients with an accuracy of 93% |
BERG from Massachusetts is an AI that recently found treatment measures for Parkinson’s disease |
Atomwise, another AI from California, is being used to detect and treat some of the most serious diseases such as Ebola and multiple sclerosis |
Atomwise is designed to screen millions of genetic compounds and most fascinatingly, deliver results 100 times faster than the traditional pharma companies |
There is a plethora of these AI-driven milestones in medical science |
Thus, citing the examples could be endless |
Deducing, the network of smart system has significantly changed over time from simple detection of diseases to diagnosis, helping in decision making to complete treatment with immeasurable speed and infallibility |
Limitations of AI in Healthcare However, we must be aware of certain limitations of those intelligent systems |
The AI doesn’t have a mind of its own and depends on the humans for its execution (this reasons for the controversial ethical quotient in AIs) |
Since they feed on the data provided, it is not always infallible as humans are erroneous in themselves |
This has resulted adversely |
In 2015, misdiagnosis of illness and a medical error accounted for 10% of all deaths in the USA |
And the IBM Watson developed an application that could recommend the appropriate treatments for patients with cancer |
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