Systems of program natural selection, which test and gradually eliminate processes to determine the best practices, have quietly changed the way computers work in healthcare.
Many computers now incorporate systems that learn over time, appropriately called machine learning. A level beyond that comes to evolutionary computing, a system in which a computer is able to perform "natural selection" on various processes to determine which one is the best to use to solve a problem, according to Forbes.
Here are five ways in which evolutionary computing is affecting healthcare.
1. The use of population data for drug development. Population health software aggregating data from clinical trials determining the effects of a drug can survey the results for particular patients and possibly predict which ones will benefit most. That will streamline the process of drug testing and help pharmaceutical companies determine its effects more accurately. Theragence, Inc. is pursuing software to do just that at present, according to Forbes.
2. Precision medicine. An ongoing analysis of what is working for an individual and what is not can provide a more personalized approach to treatment. Currently, physicians must try various treatments and interpret the data to determine what is working, but a computer could manage it with the right processes.
3. A better understanding of emotional states and mental health. A computer can analyze a patient's word choices and how they change over time, detecting changes in emotion over time on a spectrum of anxiety, hostility, happiness, depression, confidence and compassion. A software called EffectCheck can execute that process and analyze it for a report on an individual's mental health.
4. Improved stem cell therapies. Stem cell therapies, a hot-button topic for many years, are now being tested and implemented in many medical fields. However, many details about how they grow are poorly understood. Computers can more closely analyze the before and after effects of why stem cells transform the way they do and report back, altering algorithms as need be.
5. The prediction of virus mutation over time. Particularly with HIV, mutations are endless within viruses and difficult to track. Evolutionary computing techniques can help clinicians and researchers understand how HIV evolves within patients and help predict how it will evolve in the future, possibly leading to more effective therapies.