How is machine learning being used to improve the treatment of rare diseases?

 According to research published in the Orphanet Journal of Rare Diseases, the diagnosis, medication, and prognosis of rare diseases can be improved, and the current use of the technology in rare diseases can be utilized to lead future analysis and practices of it.

Do you wonder What is a rare disease?

A rare disease is described as a disease that affects less than 200,000 people, yet it’s expected that 400 million people throughout the globe are living with one that's bigger than the entire population of the United States.

The arrival of Machine learning

The world of health and medication has defeated innumerable obstacles over the last few scores. Life sciences organizations have continued to spend significantly in their investigation of globally eroding diseases, drug particle discovery, and advancement, and strictly controlled clinical trials, to create industry-approved blockbuster drugs. These drugs, getting accuracy with time, are curing more diseases and providing patients a possibility of better lives. The pharma journey, however, gets complex with more in-depth clinical and genetic analysis, as organizations continue to discover themselves handling more significant ‘unknowns’ than earlier.

The arrival of machine learning (ML) and its associated abilities create numerous possibilities for intelligent intervention, which, if leveraged perfectly, can significantly increase the rare disease treatment journey. 

This blog explores such areas, and How ML can play a crucial role to promote the correct patient identification and investigation method.

Patient Data

Patient-level data is possible in plenty today, coming in both structured and unstructured forms. Companies are digging patient-level data from origins such as devices (wearables and smartphones), digital platforms (social media and search engines), and medical records (Electronic Health/Medical Records - EHRs/ EMRs and Real-World Evidence - RWE). Patient information from all these data sources is collectively used to generate Protected Health Information (PHI) records, by effectively combining the structured and unstructured data into a combined single-source-of-truth.

Pre-defined business laws, along with AI/ML methods such as Natural Language Processing (NLP) and Text Mining, help provide the PHI master data into various logical disease signs. Consisting of claims, diagnostic, and medical information, each of these indicators aid in a better understanding of disease complexities. By narrowing down the scope to particular rare disease cases, these flags have the power to create a reference of identifiable rare disease signs based on real-life rare disease situations. Also, by assuring a constant feedback loop mechanism, ML algorithms will help make these indicators more reliable over time.

DISEASE PATTERN Creators

Patient indicators can accumulate to create disease-specific personas. These personas are based on demographics, signs, responses, and medical histories of collections of homogenous rare disease patients. Powered by AI/ML-based markers over the disease lifecycle, pre-defined patient personas (or genomes) work as go-to patterns for classifying rare disease indications. Moreover, high-level statistical methods will help check each persona’s relationship with the correlated disease.

Plus, organizations can also tie-up with industry-leading doctors to super-impose these algorithms with personal expertise in dealing with such patient populations. As a consequence, particular disease pattern markers can be received, which are supported by deep patient data report, AI/ML-based algorithms, and expert guidance. When used along with predictive triggers over continuous patient monitoring, these flags append an immense value to the investigation of rare disease patients.

PREDICTIVE TRIGGERS

The disease classification mechanisms regularly work at two levels – the patient and physician. When implemented with the right knowledge, smart technology, and proper diligence, such mechanisms work miracles in saving lives. Pre-defined disease markers will form a fundamental part of the diagnosis chain for tomorrow. Embedded over multiple patient tracking devices such as wearables and smartphones, and combined with doctor reports and dashboards, disease markers will help lift flags at the onset of the smallest disease symptoms. AI/ML-driven triggers can closely monitor patients throughout the clock and foretell rare disease symbols very early in the diagnosis process, reducing the overall diagnosis timeline. When informed, patients and doctors will be ready to act together to eliminate all chances of disease morbidity and take the required steps to better patient outcomes.

Summary

Reading the right kind of data, deriving actionable insights, and combining them into a sustainable people-driven operational plan is the only way for life sciences organizations to demystify rare diseases. A better knowledge of patient journeys will proceed to compress (and strengthen) disease diagnoses. Life sciences companies require to operate closely with healthcare stakeholders to involve patients in their diagnostic cycles. With proper data and assisted technology, patients and physicians can act together to produce better treatment opportunities, and improved patient lives.

The beginning of Machine Learning and its infinite sea of computing opportunities have inspired life sciences companies to adopt the fourth industrial revolution. With this, the trifecta of data, technology, and people, promises to cut bigger healthcare walls, as it has in the past.

 https://webmedy.com/blog/how-is-machine-learning-being-used-in-treatment-of-rare-diseases/