Machine Learning or ML for short, is a subcategorization of AI technologies, where algorithms undertake processing of massive data sets to identify patterns, undergo learning from them, and carry out tasks in an autonomous fashion without the guidance of any human agent on precisely how to tackle the issue that is being encountered. Lately, the broad availability of powerful hardware and cloud computing has the outcome of wider adoption of Machine Learning in differing spheres of human lives, from leveraging it for recommendations on social media to its adoption for process automation in factories. And its proliferation will only expand further.

ML in healthcare is a space that has evolved with the passage of time as well. With the massive output of data produced for every patient, machine learning algorithms in healthcare have massive potential. So, there’s no wonder that there are various successful ML use cases and implementation within the healthcare/medical space at the current moment. This blog will take a look at some important ones.

What is the scope of Machine Learning in Healthcare? What can it do?

ML facilitates the development of models to rapidly undertake analysis of data and provide results, harnessing historical and real-time data.

With the assistance of machine learning, healthcare service providers can make improved decisions on patient’s diagnoses and treatment avenues, which caused a cumulative enhancement of healthcare services.

Prior, it was a challenge for healthcare professionals to gather and undertake analysis of the massive volume of data for efficient forecasting and treatments as there were no technologies or tools that were available at the time. Today with the help of ML, it’s been comparatively simple, as big data technologies like Hadoop are mature enough for adoption on a broader basis, heralding an exponential increase in proliferation.

As a matter of fact, (more than half, 54% to be exact), are leveraging or evaluating Hadoop for deployment as a big data processing tool to obtain crucial insights on healthcare according to the Ventana Research Survey. A whopping 94% of Hadoop users out of the current users carry out analytics on massive data sets which they believe was not viable or feasible prior.

ML algorithms can additionally be beneficial in furnishing important and crucial statistics, real-time data and advanced analytics with regards to the patient’s disease, laboratory test outcomes, blood pressure, family histories, clinical trial information, etc. to healthcare practitioners.

Within the healthcare space, machine learning assists in gathering real-time information connected to patient’s illnesses, diseases, and conditions, laboratory results and clinical trials. In view of these possibilities, there are various spheres where ML can be deployed to alter the outlook for healthcare.

Illness Prediction – The current day, sophisticated approach to healthcare is that prevention is better than cure. Prevention of the illness, condition, or disease with proactive intervention at a preliminary stage is considered better than treatment application after diagnosis occurs. Conventionally, practitioners and professionals leverage a risk calculator to evaluate the possibility of disease development.

These calculators harness fundamental data like demographics, medical conditions, life routines, and more to calculate the possibility of developing a specific disease. Such calculations are carried out leveraging equation-based mathematical strategies and utilities. The hurdle here is the low precision rate with a similar equation-driven approach.

For, instance, the Framingham Study can forecast the hospitalization with just 56% of precision for a longer-term cardiovascular condition. However, with the latest developments in strategies like big data and ML, it is possible to get results and outcomes that are more precise for the purposes of disease forecasting. Physicians and practitioners are collaborating and partnering with computer scientists and statisticians to produce improved tools to forecast the diseases.

Specialists within the space are operating on the methodologies to detect, develop, and fine-tune ML models and algorithms which can provide better illness prediction. To produce a strong and more precise ML model, we can harness data gathered from research that has been carried out, patient demographics, medical health records, and other sources.

The variation between conventional and machine learning strategy for illness prediction is the several dependent variables to consider. In a conventional strategy, they look at very minimal variables that you can count on your figure like weight, age, height, gender, and more (owing to computational restrictions)

On the other hand, ML being processed on computing devices can view a massive number of variables, which has the outcome of improved precision in healthcare data. According to one of the latest research pieces, the researcher got improved diagnostic precision, leveraging entire medical records by considering approximately 200 variables. – drastically improving illness prediction in the process.

Drug Discovery – Drug research, discovery, and development is a really expensive prospect and is time-intensive to boot. Conventionally, developing a new drug takes more than a decade to make an entry into a market that is valued at approximately 2.6 billion dollars, according to the Tufts Centre for the Study of Drug Development.

A drug discovery initiative intends to identifying a compound that reacts with the targeted molecules of the body, having the outcome of an illness to cure. However, there is a massive possibility that the core or supporting drug compound reacts to non-targeted molecules in the body in an adverse fashion, which can potentially create threatening and hazardous side effects.

As pharmaceutical enterprises cannot forecast a possible drug compound impact on targeted molecules leveraging conventional computational tech, the prospect for drug failure is increased within clinical trials. This situation makes drug discovery very expensive and time-intensive process. Better predictive strategies leveraging ML can save a ton of resources in this scenario.

ML-driven approach (taking into account the massive volume of clinical data for approved and failed drugs) to detect a toxic compound that might create side effects can help in saving several resources prior to entering into clinical trials.

Approximately 90% of drugs cannot make it through the trial processes. Through automation of the compound molecules reaction processes leveraging machine learning, pharmaceuticals can enhance the drug discovery and developmental procedure and reduce the time taken to market. Going by a research carried out by Carnegie Mellon, automation of the drug discovery process can minimize expenditure by approximately 70%.

Electronic Health Record – EHR are made up of fully medical and health-related information in a singular system to ensure data availability and accessibility.

Machine Learning-driven Electronic Health Record Model Transfer strategy assists in the application of predictive models across varying EHR systems. These models can receive training leveraging datasets from a single EHR and can be utilized to forecast an outcome for another system.

With ML-based Electronic Health Records, users can forecast illness outcomes from other EHR system harnessing heterogenous data models. These frameworks consist of heterogenous data sources, with information that comes in several variations, both unstructured and structured, like imagery, textual data, medical imagery and more. Recording this data is not a concern, however it is difficult to deploy this data for analysis and forecasting owing to inconsistent formats.

ML tech like image processing, OCR (Optical Character Recognition), Natural Language Processing (NLP), and others can assist to convert this information into the uniform format from several sources and several systems.

This strategy facilitates the implementation of the Machine Learning model and forecast the prospective outputs.

Conclusion of Part 1

That brings us to the conclusion of the first part of this blog series, on ML’s use cases within healthcare. We just looked at the scope of ML in healthcare, a few broader areas where its power could be leveraged. In the next part of the blog, we will be going into detail into some of the specific use cases.

What we saw right now was the few prospective spheres where ML can assist the healthcare space out of several scenarios. We can observe, with ML applications, healthcare and medical segment can progress into a new realm and totally transform the healthcare operations.