Machine Learning in Healthcare - PerfectionGeeks
Machine learning (ML) is a subclass of artificial intelligence technology, where algorithms process large data collections to detect patterns, learn from them, and perform tasks autonomously without being on precisely how to address the issue.
In recent years, the vast availability of powerful hardware and cloud computing has resulted in the wider adoption of ML in other areas of human lives, from using it for advice on social media to adopting it for strategy automation in manufacturers. And its adoption will only increase further.
Healthcare is an enterprise that maintains up with the times as well. With the amount of data developed for each patient, machine learning algorithms in healthcare have great potential. So, that’s no surprise that there are multiple successful machine learning applications in healthcare right now. Let’s learn more about them.
Tasks that Machine Learning in Healthcare Can Take
Machine learning techniques can be applied to solve a broad variety of tasks. When it reaches to applications of machine learning in healthcare, these studies include:
- Classification — machine learning algorithms can help to define and label the kind of illness or medical case you’re dealing with;
- Recommendations — machine learning algorithms can offer vital medical details without the necessity to actively search for them;
- Clustering — machine learning can help to group equivalent medical cases to explore the practices and conduct research in the future;
- Prediction — using existing d
- data and common tendencies, machine learning can make a prognosis on how the future events will unfold;
- Anomaly detection — using machine learning in healthcare, you can see the items that stand out from common patterns and decide whether they need any actions to be performed;
- Automation — machine learning can take standard repetitive jobs that take too much time and effort from doctors and patients, like data entry, appointment scheduling, inventory management, etc.;
- Ranking — machine learning can put the relevant data first, making the search for it easier.
Advantages of Machine Learning in Healthcare
Using machine learning in healthcare processes can be extremely helpful to the business. Machine learning was made to deal with extensive data sets, and patient files are exactly that – many information points that require thorough analysis and organizing.
Moreover, while a healthcare expert and a machine learning algorithm will most likely reach the same conclusion based on the same data set, using machine learning will get the results much quicker, allowing to start the treatment earlier.
Another point for utilizing machine learning methods in healthcare is eliminating human involvement to some degree, which decreases the possibility of human error. This especially concerns strategy automation tasks, as tedious routine work is where humans err the most.
Examples of Machine Learning in Healthcare
Clinical Decision Support Systems
Clinical decision support mechanisms help interpret large volumes of data to determine disease, decide on the next therapy stage, resolve any potential problems, and overall improve patient care efficiency. CDSS is a powerful tool that helps physician do their job efficiently and fast, and it decreases the probability of getting the wrong diagnosis or defining ineffective treatment.
This usage of machine learning in medicine (healthcare) has been around for a while but has become more general in recent years. The reason behind it is the wider acceptance of the electronic health record system (EHR) and digitalization of different data topics, including medical illustrations.
Making sure that all the patient documents are updated regularly is difficult, as data entry is a tedious task. However, it is also essential for adequate decision-making and better patient care.
One of the benefits of machine learning in healthcare is using optical character recognition (OCR) technology on physicians’ handwriting, creating the data entry fast and seamless. This data can then be analyzed by other machine learning tools to improve decision-making and patient care.
Machine Learning in Medical Imaging
For the most extended time, medical images, like X-rays, have been analog. This has determined the use of technology for anomaly title, case grouping, and overall disease research. Fortunately, the digitalization of the approach has led to more possibilities with these kinds of data analysis, including with the help of machine learning. And, according to a recent meta-analysis, machine learning algorithms do the job as well as (and, in some cases, even better) human experts, with 87.0% sensitivity and 92.5% specificity for the deep learning algorithms and 86.4% sensitivity, and 90.5% specificity for human physicians.
One of the well-known victorious instances of machine learning in healthcare is the InnerEye project from Microsoft. Its initial emphasis was on 3D radiological images, where ML tools were built to distinguish healthy cells and tumors.
What makes medicine such a difficult and resource-heavy field is that every case has its specifics. People often have a sleuth of states that need simultaneous treatment. So, tough decisions must be made to create an effective treatment plan, accounting for drug interactions and underestimating potential side effects.
How to use machine learning in healthcare to crack this problem? Well, IBM has figured that out with their Watson Oncology system that utilizes the patient history to deliver multiple potential treatment choices.
Prevention is as necessary for healthcare as disease treatment. One of the most significant parts of preventive medicine is changing one’s behavior to get rid of unhealthy habits and establish a healthful lifestyle.
One of the advantages of machine learning in healthcare is that it can be used to point out something we don’t see. That’s exactly what Somatix does. This machine learning- based application follows the patient’s everyday activity and points out their unconscious habits and training so that they can concentrate on getting rid of them.
Predictive Approach to Treatment
When it comes to the most dangerous diseases, identifying them in the earlier steps can increase the chances of successful treatment significantly. This also helps to determine the possibility of any potential worsening of the patient’s state before it happens.
One of the points for the extent of machine learning in healthcare is that it can be used to successfully predict some of the most difficult diseases in at-risk patients. This contains the identification of signs of diabetes (using a Naïve Bayes algorithm), liver and kidney diseases, and oncology.
One of the most important duties for a physician is to gather a patient’s history properly. This can often be difficult, as the patient is not a professional and doesn’t know which data is relevant to disclose.
Using machine learning in healthcare management, healthcare experts can choose the most relevant queries they should ask a patient based on different indicators. This will help gather relevant data and, at the same time, get a prediction of the most likely conditions.
Elderly and Low-Mobility Groups Care
Machine learning and medicine can help low-mobility groups (including the elderly and people using wheelchairs) enhance their regular lives with smart reminders and scheduling help, predict and avoid potential injuries by identifying common obstacles and determining the optimal paths, and acquire help as soon as required.
While these answers are effective, they are not as general as required. However, healthcare organizations have already taken steps to create them widely known. For instance, in Japan, there is a plan to have 75% of elderly care performed by an AI.
Surgical systems need great precision, adaptability to altering circumstances, and a steady system for an extended period. While trained surgeons have all these qualities, one of the possibilities in machine learning for healthcare is for robots to fulfill these duties.
Right now, robotic surgery can be effectively used as a help for human surgeons. Namely, machine learning can be utilized for better surgery modeling and planning, evaluating the surgeon’s skills, and facilitating surgical tasks like suturing.
Drug Discovery and Production
Based on the earlier obtained data on active ingredients in drugs and how they impact the organism, ML algorithms can model an active component that would work on another identical disease.
Such an approach can be used to create a personal medication for patients with a unique set of diseases or certain special needs. In the future, this machine learning tool could be used in a mixture with nanotechnology for better drug delivery.
Clinical research and practices are expensive and have long cycles. There is a good reason behind this – new drugs and medical practices should be proven to be safe before being used widely. However, there are cases when the solution is required to be removed as soon as possible – like with the vaccines for COVID-19.
Fortunately, there is a way to make the procedure shorter with the help of machine learning algorithms. It can be used to select the best selection for the trial, collect more data points, analyze the continuous data from the trial participants, and lower the data-based mistakes.
Infectious Disease Outbreak Prediction
COVID-19 pandemic has shown us how spontaneous we were to an infectious disease outbreak of this size. It is worth mentioning that professionals in the area have warned the government about the possibility of such an event for years.
Now, we have tools based on machine learning that can help to catch the symptoms of an epidemic early on. The algorithms analyze the satellite data, news, social media reports, even video sources to predict whether the disease has the potential to rise out of control.
In health care industries, the brilliance of different data science models, without any human brains, can deliver accurate and efficient outcomes with little to no time.
The machine learning models allow health care sectors in multiple ways, by establishing accurate and efficient suggestions or by decreasing the manual tasks of healthcare specialists, hence allowing them to concentrate on the research area and improve their performance in urgent cases.