It is safe to say that there are too many manual processes in medicine. When in training, I write lab scores, diagnoses, and other graphic notes on paper. I know this is an area where technology can help improve my workflow and hope it will also improve patient care. There are lots of ways that how Machine learning in health care can be beneficial.
Since then, progress in electronic medical records has been extraordinary, but the information they provide is not much better than the old paper charts they replaced. If technology wants to improve care in the future, then the electronic data provided to doctors needs to be enhanced by analytical power and machine learning.
By using this sophisticated type of analysis, we can provide better information to doctors at the point of patient care. We are having easy access to blood pressure and other vital signs when I see my patients routinely and hopefully.
Imagine how much more useful it would be if I were also shown my patient’s risk of stroke, coronary artery disease, and kidney failure based on 50 recent blood pressure readings, laboratory test results, race, gender, family history, socioeconomic status, and the latest clinical trial data.
We need to advance more information to doctors so that they can make better decisions about patient diagnoses and treatment options while understanding the possible outcomes and costs for each. The value of machine learning use cases in health insurance is its ability to process large datasets outside the scope of human capabilities, and then reliably transforms the analysis of the data into clinical insights that help doctors plan and provide care, which ultimately leads to better outcomes, costs lower than attention, and increasing patient satisfaction.
Applied Machine Learning in Health Services:
Machine learning in medicine recently made headlines. Google has developed a machine-learning algorithm to help identify cancer tumors on a mammogram. Stanford uses deep learning for healthcare algorithms to detect skin cancer. A JAMA article recently reported the results of a deep machine learning algorithm capable of diagnosing diabetic retinopathy in retinal images. It is clear that machine learning places another arrow in the vibration of clinical decision making.
However, machine learning is suitable for some processes better than others. Algorithms can provide direct benefits to scientific disciplines with methods that can be reproduced or standardized. Also, those who have large drawing datasets, such as radiology, cardiology, and pathology, are strong candidates. Machine learning can be trained to see images, identify abnormalities, and point to areas that need attention, thereby increasing the accuracy of all these processes. Long-term, machine learning will be beneficial for family practitioners or internists at the bedside. Machine learning can offer objective opinions to improve efficiency, reliability, and accuracy.
On Health Catalyst, we use proprietary platforms to analyze data and return it in real-time to doctors to assist in clinical decision making. At the same time, a doctor looks at the patient and includes symptoms, data, and test results into ESDM, there is a learning machine behind the scenes seeing everything about the patient, and encouraging the doctor with information that is useful for making a diagnosis, ordering a test, or suggesting preventive filtering. In the long run, the capability will reach all aspects of medicine when we get integrated data that is more useful. We will be able to combine more massive data sets that can be analyzed and compared in real-time to provide all types of information to providers and patients.
Ethics of Using Algorithms in Health:
It has been said before that the best machine learning tool in health care is the doctor’s brain. Could there be a tendency for doctors to view machine learning as an unwanted second opinion? At one point, automatic workers worry that robots will get rid of their jobs. Similarly, there may be doctors who fear that machine learning is the beginning of a process that can make them obsolete. But it’s an art of medicine that can never be replaced. Patients will always need a human touch and a caring and caring relationship with the people who provide care. Both machine learning, as well as other future technologies in medicine, will eliminate this but will be a tool that doctors use to improve ongoing care.
Healthcare needs to move from thinking about machine learning as a futuristic concept to seeing it as a real-world tool that can be used today. If machine learning has a role in health care, then we must take a new approach. We have to find individual use cases where machine learning capabilities provide value from specific technological applications (e.g., Google and Stanford). This will be a step-by-step path for incorporating more analytics, machine learning, and prediction algorithms into everyday clinical practice.
Medicine has a method for investigating and proving that treatment is safe and effective. This is a lengthy trial process – and bases the decision on evidence. We need this same process in place when we look at machine learning to ensure its safety and efficacy. We need to understand the ethics involved in giving away some of what we do to a machine.
There are unlimited opportunities for machine learning in health care:
Some people might ask whether this is only a technological mode or whether it will provide real value in health care. Health Catalyst believes that the introduction and widespread use of machine learning in health care will be one of the most essential life-saving technologies ever introduced. We believe that opportunities are actually not limited to technology to improve and accelerate clinical, workflow, and financial results. The following are a few examples:
Reduce Readmissions. Machine learning can reduce re-acceptance in a way that is targeted, efficient, and patient-centered. Doctors can receive daily guidelines about which patients are most likely to be accepted again and how they can reduce that risk.
Prevent hospital-acquired infections (HAI). Health systems can reduce HAI, such as central line-related blood flow infections (CLABSI) —40 percent of CLABSI patients die – by predicting which patients have a primary channel that will develop CLABSI. Doctors can monitor high-risk patients and conduct interventions to reduce those risks by focusing on patient-specific risk factors.
I am reducing the Duration of Staying in a Hospital (LOS). The health system can reduce LOS and increase other outcomes such as patient satisfaction by identifying patients who tend to experience an increase in LOS and then ensure that best practices are followed.
Predict chronic disease. Machine learning can help the hospital system identify patients with chronic illnesses that are undiagnosed or misdiagnosed, predict the possibility that patients will develop chronic diseases, and present patient-specific preventive interventions.
Here are some of the applications and advantages of machine learning in healthcare –
1. Helps in Identifying Diseases and Diagnosis
One of the primary advantages of machine learning in healthcare is the identification and diagnosis of disease and ailments, which are otherwise considered to be as hard to diagnose. This can include anything from the cancers which are tough to catch at the time of the initial stage to other genetic diseases. IBM Watson Genomics, which is even one of the above prime examples of how integrating cognitive computing with the genome-based tumor sequence, can help in making a fast diagnosis.
2. Drug Discovery and Manufacturing
One of the primary clinical benefits of machine learning in healthcare lies in the early-stage drug discovery process. This also includes the Research & Development technologies such as next-generation sequencing and precision medicine, which can even help in finding alternative paths for therapy of multifactorial diseases. As of now, machine learning benefits techniques involve unsupervised learning, which can identify the patterns in data without even offering any predictions. Project Hanover, which is developed by Microsoft, is using the Machine Learning-based technologies for multiple initiatives, which include the AI-based technology for cancer treatment and personalizing drug combination for the Acute Myeloid Leukemia.
3. Medical Imaging Diagnosis
Machine learning deep learning in health care are both responsible for the breakthrough technology called the Computer Vision. This has found one of the best acceptances in the InnerEye initiative developed by Microsoft, which works on the image diagnostic tools for the analysis of the picture. As machine learning becomes much more accessible and as they grow in their explanatory capacity, expect to see more data sources from the full range of medical imagery becomes a part of this AI-driven diagnostic process.
4. Personalized Medicine
Machine learning in health care helps in the customized treatments that can not only be more efficient and effective by pairing individual health with predictive analytics, but it is also ripe are for further research and better assessment of the disease. As of now, Physicians are limited to choosing from a specific set of diagnoses or to even eliminate the risks to the patient, which is based on his symptomatic history and are available genetic information.
But the Machine Learning in the medicine is making great strides, and the IBM Watson Oncology is at the front part of this movement by using the medical history of the patient to help generate the multiple treatment options. In the coming years, we will see a number of biosensors and devices with sophisticated health measurement capabilities to hit the market, thus allowing more data to become much more readily available for some of the cutting-edge Machine Learning based healthcare technologies.
5. Machine Learning-based Behavioral Modification
Behavioral modification is an essential part of preventive medicine, and ever since the proliferation of the machine learning benefits in healthcare, countless startups are cropping up in the fields of cancer prevention and identification, patient treatment, etc. Somatix is a B2B2C based data analytics company that has unveiled a machine Learning-based apps to recognize gestures which we use and make in our daily lives, thus which allows us to understand our unconscious behavior and make some of the necessary changes.
6. Smart Health Records
Maintaining proper health records is an exhaustive process, and while the technology has played a preeminent role in easing the process of data entry, the truth is that even now, a significant part of the methods takes a lot of time to complete it. The primary role of how to do Machine learning in healthcare is to ease up the processes to save both the time, money, and efforts. Document classification methods with the help of a vector Machine Learning based OCR techniques of recognition are slowing gathering the stream, such as the MATLAB’s Machine Learning and Google Cloud Vision API based handwriting recognition technology.
MIT in today’s world is offering and working on the cutting-edge technology of developing the next generation of the small and intelligent health records, which will use the Machine Learning based tools from the ground level up to help with the clinical treatment diagnosis and suggestions. It is one of the major machine learning use cases in the health insurance part.
7. Clinical Trial and Research
Many people have an issue in mind that how to do and use machine learning in the healthcare industry? Well, Machine Learning has a wide range of potential applications in the field of research and clinical trials. As anybody in the pharma industry would even tell you, as the clinical trials with urgent care cost a lot of money and can also take with years to complete in many of the cases.
Applying the Machine Learning based predictive analysis on identifying the potential clinical trial candidates can help the researchers to draw with a pool from a wide range of data points, such as social media, previous doctor visits, etc. ML has also found with some of the usages in making sure the real-time data access and monitoring of the trial participants, funding the best sample size to be tested and using the power of electronics record, thus, which helps in reducing the data-based errors.
8. Data Collection
Crowdsourcing is played at majority all the rage in the medical field in today’s scenario, which allows the researchers and practitioners to access the full range of information by people based on their own consent. This live health data has some of the significant ramifications in the way in which the medicine will be perceived down the line. Apple has its own ResearchKit, which allows the users to access the interactive apps, thus applying the Machine Learning based facial recognition to try and treat the Parkison and Asperger disease.
IBM has also recently partnered and signed a deal with Medtronic to decipher, accumulate, and make available insulin and diabetes data in the real-time scenario-based on the crowdsourced information. With the latest advancement which is being made on the Internet of Things, the healthcare industry is still working on discovering some of the new ways in which to use this data and thus tackle the tough to rare disease case and to help in the overall improvement of medication and diagnosis.
9. Better Radiotherapy
One of the most sought after the advantages of machine learning in healthcare is in the field of Radiology. Medical image analysis has many of the discrete variables that can even arise big at any particular moment of time. There are many cancer foci, lesions, etc., which cannot be modeled with the help of complex equations. Since the Machine Learning algorithms even learn from the multitude of the different samples available on the hand, as it even sometimes becomes easier to make some of the diagnosis and find the actual variables.
One of the most famous and popular healthcare use cases for machine learning is the classification of objects such as the lesions into a wide range of categories such as the abnormal or normal, non-lesion or lesion, etc. Google DeepMind Health is also working on to help the researchers in the UCLH to develop with the algorithms which can even detect the difference between the cancerous and healthy tissue and to even improve the radiation treatment for the same.
10. Outbreak Prediction
Artificial Intelligence technologies and Machine Learning are today, one of them is to be used in predicting and monitoring epidemics across the globe. In today’s scenario, the scientist has to access the vast amount of data that is collected from the satellites, website information, real-time social media updates, etc. Artificial Intelligence Neural Networks helps to collate this information and predict that everything from the malaria outbreaks to severe chronic infectious diseases.
Predicting these outbreaks is also very useful in third world countries as they lack some of the crucial medical infrastructure and educational system as well. An example of this is the ProMED – mail, which is an internet-based reporting management platform that helps to monitor the evolving diseases and emerging ones and offers the outbreaks the reports in the real-time scenario.
11. Crowdsourcing research
Crowdsourcing medical data as of now is not just a new idea. As it is even growing at a breakneck pace, with the help of Machine Learning and AI. From sites like Apple, companies are also working to understand the medical issues with the help of crowdsourcing better and quickly. Apart from that, these research methods become much more accessible to people from the communities who are from marginalized that may not otherwise be able to take part. With respect to the participation in research to helps patients feel much more empowered while giving much more essential feedback and reviews.
From the Anesthesia to the treatment from breast cancer to daily drugs, the use of machine learning in healthcare is already being employed in the improvements of the medications. The famed IBM supercomputer Watson is also working with companies like the Pfizer to improve drug discovery, moreover, from the immunological conditions and cancer. Google has also been in this game for so many years and has also been found to be much more impressive with the potentials for the machine learning to guide and improve the ideas around the treatments.
Some of the companies like Medtronics are also working on to utilize machine learning to improve the medications, but, much more on the individual scale. Some of the channeling ideas about precision medicine, they are also working on with the patients to offer personalized feedback on how to better control and treat their diabetes. Personalized medicine will be a crucial strength of patients in the future.
13. Smart Electronic Health Recorder
Machine Learning scope such as the optical characters and document classification can also be used to develop with the smart electronic health record system. The task of this application is also to work on developing a system that can even sort the patient queries with the help of an email or even to transform the manual record system into an automated machinery system. This primary objective of this application is to build with a safe and easily accessible system.
The rapid face pace growth of the electronic health records has also been enriched with the store of the medical data about the patients, which can also be used for the improving healthcare system. It even reduces the data errors, for example, duplicate data.
To develop and build the electronic health recorder system, supervised Machine Learning algorithms like the support vector machine can be used as a classifier or the Artificial Neural Network, which can be applied easily.
14. Robotic Surgery
Robotic Surgery is one of the most significant benchmark machine learning applications in the sector of the healthcare market. This application will now also become with some of the promising areas soon. This application can be evenly divided into four subcategories, such as surgical skill evaluation, surgical workflow modeling, automatic suturing, and improvements of the robotic surgical materials.
Suturing is the process with the help of sewing up an open wound. The automation of the suturing may help to reduce the surgical process length and surgeon fatigue. As a part of the instance, the Raven Surgical Robot, researchers are even trying to apply a machine learning approach to evaluate the region of the surgeon’s performance in robot-assisted minimally invasive surgery.
The University of California and some other universities like the San Diego Advanced Robotics and Controls Labs researchers are trying to explore the machine learning applications to improve the surgical robotics.
15. Prediction of Liver Disease
The Liver is the second most significant and critical internal organ in our body. It plays a preeminent role in the functioning of metabolism. One can quickly attack several liver diseases like Liver Cancer Chronic Hepatitis, Cirrhosis, and so on.
Just a few days back, some of the data mining and machine learning concepts have been used dramatically to predict liver disease easily. It is one of the very many challenging tasks to predict the condition with the help of voluminous medical data. Moreover, the researchers are even trying their best to overcome issues with the help of concepts of machine learning like clustering, classification, and much more.
Indian Liver Patient Dataset can also be used for a liver disease prediction system. This dataset contains the ten variables. Or even the liver disorder dataset can also be used. As a part of the classifier, a Support Vector machine can also bs used. You can also use the MATLAB to develop the liver disease prediction system.
Nowadays, machine learning plays a very crucial role in our day to day life. This technique is used in a wide range of domains such as marketing applications, sales prediction, weather forecasting, and many much more. Moreover, machine learning in healthcare is still not so wide-ranging like some other application and use of machine learning in healthcare because of having the medical complexity and scarcity of data.