Uses of Machine Learning in Healthcare

Now you can see, so many cases are coming due to coronavirus that spread from Wuhan, China. With the help of technologies, doctors are able to cure the patients. This thing only possible with the help of Machine learning.

Machine learning helps to identify the potentially harmful content and then it sends for evaluation to humans reviewers for assessment. Machine learning uses in healthcare to improve the health of the patients and reduce the cost by using superior diagnostic tools and effective treatment plans for patients.

Apart from this, there are many benefits of Machine Learning in Healthcare:

Identify the diseases and Diagnosis

Nowadays we can say, machine learning is more accurate and faster at diagnosis as compared to doctors. Machine learning in healthcare is the detection and diagnosis of diseases and conditions that are difficult to treat. This can include anything from cancers that are tough to detect at the initial stage. Researchers have developed machine learning algorithms to identify the cancerous tumors on mammograms.

Aid in Coronavirus Response

Machine learning and data analytics will help accelerate solutions and reduces the impact of the virus. It helps in expediting the drug development process, predict infection levels and help in screen patients more quickly. By the use of machine learning to identify those that produce the antibodies that help that person neutralize the virus. 

Patient risk Identification

Nowadays, the healthcare sector also started using tools built from machine learning models which helps in predicting various diseases like cancer, heart attack, tumors, strokes & more serious complications. These tools use data from the patient’s medical records,

daily evaluations, and measurements of vital signs in real-time, such as heart rate, sugar level, and blood pressure, to alert doctors about patient risks so they can immediately take preventive actions.  

Visual data Detection for tumors

Earlier days technologies were not so advanced as compared to now. So, Many diseases were not being able to detect at an early stage & due to lack of treatment at the right time people died. One of the most dangerous diseases is a tumor. Researchers have developed deep learning algorithms trained on previously captured radiographic images to recognize the early development of tumors in various areas of the body such as lungs, breasts, brain, etc. Algorithms can be trained to recognize complex patterns in radiographic imaging data.

Neurosurgeons are more confident than ever about their patient’s brain tumor diagnosis, thanks to the integration of a new system that will allow them to quickly see the diagnostic tissue & tumor margins in near real-time. Without the need for a pathology lab, neuropathologists can review the images, reducing the long waiting time needed for conventional processing, staining, and analysis.

Accelerating Medical Research Insight  

The use of NLP tools and neural networks to parse literature will provide usefully insights for medical researchers in the years ahead. NLP is also being used to mine unstructured data for insights in EHRs, such as data from electrocardiogram tests or copies of manually written notes that have been submitted to a patient’s record, but not included in form fields. CTakes is one example of the open-source NLP projects by Mayo clinic, Boston children’s hospital, and other organizations to develop a tool that analyzes unstructured data in EHRs for insights extraction.

Smart Health Records

Maintaining the up to date health records is a lengthy process and technology has played its part in easing the process of data entry. The fact is that even now, most of the process requires lot of time to complete. The primary function of machine learning in healthcare is to ease the process that saves time, energy and money. Documents classification methods are increasingly gaining momentum using vector machines and OCR recognization based on machine learning such as google’s cloud vision API and MATLAB’s machine learning-based recognization technology for handwriting.   

Using Convolutional Neural Networks for Diagnosis of Skin Cancer 

CNN’s are important tools for detecting and classifying images. Many researchers have used them to create machine learning models for skin cancer detection with 87-95% accuracy using TensorFlow, sci-kit-learn, Keras and other open-source tools. In comparison, dermatologists detect melanomas with an accuracy rate of 65 percent to 85 percent. Models are trained using thousands of images of malignant and benign skin lesions.

Outbreak Prediction

Also now, AI-based technology and machine learning are being used to predicting and forecast epidemics around the world. Scientists have access to a large amount of data collected from satellites, real-time social media notifications, website information, etc.with the help of Artificial neural networks it collects information and predicts everything from malaria outbreaks to serve chronic infectious diseases. Predicting these outbreaks are particularly helpful in third-world countries because they are lack in crucial medical infrastructure and educational systems.

Conclusion

Machine learning Algorithms provide disciplines with reproducible or standardized processes with immediate benefits. Those with large image datasets are also good candidates, such as radiology, cardiology, and pathology. Machine learning can be trained to look at pictures, detect abnormalities and point to areas that require attention, thus improving the accuracy of all these processes. Machine learning at the bedside can support the family practitioner or internist in the long term. Machine learning offers an unbiased opinion to improve performance, reliability, and accuracy.

Also Read: How Machine Learning Can Help your Business

If you want to know more about Machine learning like regarding the course, how uses of machine learning in different sectors and many more, then join “Nearlearn”. Nearlearn is the Best Machine Learning Online Training institute in Bangalore and offering a 50% discount on Machine learning training. We also offer courses like Deep Learning, Artificial Intelligence, Data Science, Data Analytics, PythonTraining, Blockchain Training. They provide skill trainers having 10+ years of industry experienced. Nearlearn provides both online training and classroom training. During the course provide internship facilities to students where they get fully practical experience.