Medical algorithms, healthcare algorithms, medical industry maths

5 medical algorithms that are transforming the healthcare industry

This post focuses on the impact that medical algorithms have in the field of healthcare where you must be a 100% right at all times. There is no room...

This post focuses on the impact that medical algorithms have in the field of healthcare where you must be a 100% right at all times. There is no room for errors because even the trivial errors can create a major impact. However, even the smartest and best-trained professionals are prone to errors. Tragedies due to human error are common in the medical industry.

Today, by using algorithms, doctors and care providers are able to determine exactly where to point the lasers for maximum impact with minimum collateral damage. Algorithms and genetic algorithms have made the way we treat patients more effective.

Here we list Medical algorithms used in healthcare industry

  • Sampling
  • Fourier transform
  • Probabilistic data -matching
  • Proportional integral derivative
  • Predictive algorithm

Algorithms play a major role in the area of medical from large medical equipment to simple microcontrollers. Let’s look at the top algorithms that are used in the medical industry.

Sampling

The medical industry generates large amounts of data, which must be mined and sorted. Some facts about the medical industry include:

  •  Every year almost a million medical studies are published.
  •  Additionally, 150,000 cancer-related studies are published annually.

The human brain is brilliant but there is a limit to the amount of information that it can process and store. While computers may not be able to outdo doctors, they can be used to increase the number of lives that are saved. This is where cognitive medical comes into play.

Welcome to the age of cognitive medical; meet IBM Watson Health.

IBM Watson Health is a technology platform used to store data that is collected from cancer patients and generate relevant information about the treatment that is required. To do this, Watson uses various Artificial Intelligence (AI) and Machine Learning (ML) algorithms. One of the algorithms that Watson Health uses is a sampling algorithm. By using this algorithm, Watson Health infers information from a group of people without actually examining an actual individual.

Sampling is a method of studying few selected items, instead of a big number of units. This small selected item is called sample, and a large number of units is termed as population. For example, at a fruit shop, you check one or two apples to decide the concentration of good apples. Thus, with the sample, we infer about the population. With such a huge density of data available in the medical field, we use various sampling techniques:-

What is probability sampling - Healthcare Algorithm,Probabilistic Data matching in Healthcare - Algorithm, medical algorithms, sampling, medical algorithms used in healthcare, healthcare algorithms, medical science algorithms,

 

  • Simple random sampling This is a  basic technique where we select a sample for studying a larger population. Each individual or sample is chosen randomly and every member of the population has an equal chance of being selected.
  • Systematic sampling: This is a technique where sample members from a larger group are selected based, on a random starting point and a fixed periodic interval. The interval is known as a sampling interval. It is calculated by dividing the population size by using the desired sample.
  • Stratified sampling: The researcher divides the population into separate groups, called strata. Then, a probability sample (often simple random sample) is drawn from each group.
  • Clustering sampling: is used when many ‘natural’ but heterogeneous groups are present in the statistical population. These groups(or population) are divided into small classes and any one of the sampling methods is applied to them (preferably simple random).

Using sampling technique, computers provide information about individuals leading to concrete results without actually examining them. Explaining us one of the ways how medical algorithms in the medical industry works.

Fourier Transform

The Fourier transform algorithm is often called one of the most important algorithms of our time. This algorithm applies to almost all aspects of our everyday life.

In mathematical terms, Fourier transform is a function, which is derived from a given function and represented by a series of sinusoidal functions.

What does Fourier transform means?

It means that you can take a sandwich and by using the Fourier Transform, you can isolate the various ingredients like bread, butter, tomatoes, lettuce, bacon, salt, pepper, and a cheese slice.

That is what the Fourier Transform does. It takes something and breaks it down to basic components.

Why? because it’s easier to manipulate and change something if you know what it’s basic components are.

How do you get your sandwich back? Combine the ingredients back.

A Fourier transform is a mathematical transformation applied to transform a signal or image from time domain to frequency domain which has many applications. A Fourier image converts a real value to complex value. And then for use in a different domain, the information is converted from complex value to a real value

via GIPHY

Let’s see how MRI works using Fourier Transform

Magnetic Resonance Imaging (MRI) uses the concept that the human body is made up of 70% of water. It is due to these water molecules that our body consists of many micro magnets.

These magnets all align themselves in one direction when an external magnetic field is applied— an MRI machine in this case.

As the MRI process begins, each proton starts rotating at a speed that is proportional to the provided magnetic field (which is around 63 MHz). 63 MHz is in a radio frequency range (RF), so when RF power is sent the protons, they tend to respond. An additional magnetic field ‘gradient’ is applied across. This results in the ‘R’esonance of the MRI. The RF is moved across the body to capture various images.

Now since the human body is 3D, so the X & Y gradients are also determined for an image.

The signals that we measure in an MRI is a combination of the signals that are captured while the human body is being traced. A signal is composed of a series of sine waves where each sine wave has an individual frequency and amplitude. The Fourier transform allows us to work out, what that frequency and amplitude are. Since the signal is encoded with magnetic-field, ingredients which actually makes the frequency and phase relate to the position of the object. Once the frequencies are separated, then the amplitude of the image can be plotted.

Without the Fourier Transform, medical imaging would not have been possible. Ultrasounds, MRIs, and other medical-imaging techniques use the Fourier Transform algorithm to convert the images that are then converted into a readable format. Fourier had been an essential algorithm in medical science, surprisingly how medical is quickly adapting to technology.

Probabilistic data-matching

If you are a doctor who is treating Vito Corleone, you might look into the electronic records that are related to Vito Corleone’s medical history (You do know that he is a Don, right?).

So how does probabilistic data matching come into the picture?

Probabilistic data will be used to look for all the possible information with reference to all available medical data. It will sort the data by giving preference to that data which has a likelihood of matching with Don’s medical data.

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Probabilistic data matching example

Probabilistic matching uses a likelihood ratio theory to assign comparison outcomes to the correct or “more likely” decision.

A patient whose symptoms are similar to a specific disease may have the relevant data analyzed against existing information. On the basis of the available matches, a physician will be able to determine which disease is the best possible match.

This allows the physician to create an accurate treatment plan thus giving the patient, a better chance of receiving the right treatment on time.

Probabilistic algorithms such as Niave Bayes Classifier and PAIRS (Physician assistant Artificial Intelligence System) are being used for efficient inference in large models to provide additional evidence for research and medical cases.It is one of the commonly used medical diagnosis algorithms.

Proportional Integral Derivative (PID)

In the Cardiac Unit of Alabama Hospital, the Mean Arterial Pressure of a patient is managed by a computer. This computer controls the infusion of vasodilating agents and it has helped around 1100 hypertensive patients after heart surgery.

This computer uses the digital version of a PID controller algorithm to perform the intensive task. PID is a control feedback mechanism, which controls the computer that eventually calculates an error value as the difference between the desired set point and the measured values.In simple terms, this algorithm reduces the difference between the desired output and the expected output.

How does a PID work?

PID is a control feedback mechanism, which controls the computer that eventually calculates an error value as the difference between the desired set point and the measured values.In simple terms, this algorithm reduces the difference between the desired output and the expected output.

 

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The P in the symbol accounts for the present value of the error, I for the previous value, and D is the possible future value of the error. What the controller does is it tries to reduce the error by manipulating the various factors that are associated with the mechanism. Based on the combination of P, I, and D the best result with the least errors is processed. Probably every electronic circuit that has a mechanical or thermal system attached to it will use a PID circuit. A famous example of a machine learning medical algorithm.

Predictive Algorithm

Some predictive analytics medical algorithms claim that they can use real-time data from an ICU to predict events like cardiac arrests 24 hours before they happen. It is difficult for the human mind to memorize all the information and data that it has learned over a period of time. Comes in the predictive analytics algorithm which matches the data, information over a period of time. It also analyzes and judges the data over the current information, and then predicts the outcome based on the analysis.

Example of Predictive Algorithm, what is predictive algorithm, how does predictive algorithm works, how does healthcare use predictive algorithm,Medical algorithms, healthcare algorithms, medical industry maths, Probabilistic Data matching in Healthcare - Algorithm, medical algorithms, sampling, medical algorithms used in healthcare, healthcare algorithms, medical science algorithms,

Various predictive algorithms include:

  • Time Series algorithm
  • Regressions algorithm
  • Association algorithm
  • Clustering algorithm
  • Decision Tree algorithm

With all the changes coming in the field of medicines and medical worldwide, algorithms are the next step.In a few years, physicians will be consultants and algorithms will work together to create a greater impact on human lives.

Very similar to doctors in sci-fi movies, patients will soon receive information from their doctors such as,  “Dear Don Vito, based on our analysis, you might suffer from chronic heart disease in next 3 years…” This information will help Don Vito to improve his habits and restructure his life, which might even save the doctor’s life for correct information!

If you would like to know more about algorithms –

Read here how Mark Zuckerberg used Elo Rating Algorithm for his first social network Facemash – Elo Algorithm: Common link between Facemash and Chess


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