This post focuses on the impact that medical algorithms have in the field of healthcare where you must be 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 the healthcare industry:
- Sampling
- Fourier transform
- Probabilistic data-matching
- Proportional integral derivative
- Predictive algorithm
Algorithms play a major role in the area of medical technology from large 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 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 it has limits in processing. Computers help increase the number of lives saved by leveraging sampling algorithms in cognitive medical systems like IBM Watson Health.
IBM Watson Health uses AI and ML to infer treatment insights from patient data using sampling algorithms. Sampling involves selecting a few items from a large population for study. Techniques include:
- Simple random sampling: Randomly selects members, each having equal probability.
- Systematic sampling: Selects members at a fixed interval from a random starting point.
- Stratified sampling: Divides population into groups (strata) and samples from each.
- Clustering sampling: Divides large groups into smaller natural groups and applies sampling.
Fourier Transform
Fourier Transform is used in numerous medical imaging techniques like MRIs and ultrasounds. It breaks signals into sinusoidal components for analysis and reconstruction.
It transforms signals from the time domain into the frequency domain and back. This helps isolate and interpret components of a signal for accurate image construction.
How MRI uses Fourier Transform:
MRI relies on water molecules in the body which respond to magnetic fields. The signals measured during scanning are a combination of sine waves. Fourier Transform decodes these into usable images.
Without Fourier Transform, modern imaging techniques would not be possible.
Probabilistic Data-Matching
This algorithm compares patient data against large databases to find the most likely matches, helping doctors make more informed diagnoses.
Probabilistic algorithms like Naive Bayes Classifier and PAIRS (Physician Assistant AI System) are commonly used to assist in accurate medical diagnosis.
Proportional Integral Derivative (PID)
PID is a feedback mechanism used in medical devices. For example, in Alabama Hospital, it helps manage blood pressure post-surgery by automatically adjusting medication levels.
PID works by reducing the difference between a desired outcome and the measured result using present, past, and predicted error values.
Predictive Algorithm
Predictive algorithms use historical and real-time data to forecast future medical events like cardiac arrests.
Examples include:
- Time Series algorithm
- Regression algorithm
- Association algorithm
- Clustering algorithm
- Decision Tree algorithm
Predictive analytics helps doctors anticipate health conditions early and recommend preventive steps.
As algorithms grow in intelligence, they will play an even bigger role in healthcare. Doctors will consult with algorithms to provide precise, predictive care.
Want to learn more about algorithms?
Read how Mark Zuckerberg used the Elo Rating Algorithm in Facemash: Elo Algorithm: Common link between Facemash and Chess
Let’s take the example of Elihu Feustel, who makes almost a million dollars a day by betting on tennis. You would think that he would recognize Serena Williams if he passed by her on the street. However, this isn’t the case, he neither recognizes any of the famous tennis players nor does he know their playing styles etc. So, just how does he know who to bet on? He relies on a big data model that the live betting market uses. This algorithm collects data from existing (almost 260,000) matches. This data is then manipulated to allow users to determine which player/match they should bet on.
