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How is Machine Learning being used in treatment of rare diseases?

July 7, 2020 - by Parul Saini, Webmedy team

The arrival of machine learning (ML) and its associated abilities have created numerous possibilities for intelligent intervention, which, if leveraged perfectly, can significantly help in treating rare diseases. Want to know how? keep reading.

Rare Disease Facts

  • There are over 300 million people living with one or more of over 6,000 identified rare diseases around the world.
  • Each rare disease may only affect a small number of people, scattered around the world, but taken together is substantial and equivalent to the population of the world's third-largest country.
  • Rare diseases currently affect 3.5% - 5.9% of the worldwide population.
  • 72% of rare diseases are genetic whilst others are the result of infections (bacterial or viral), allergies and environmental causes, or are degenerative and proliferative.
  • 70% of those genetic rare diseases start in childhood.

Challenges in Treatment of Rare Diseases

The lack of scientific knowledge and quality information on the disease often results in a delay in diagnosis. Due to the broad diversity of disorders and relatively common symptoms which can hide underlying rare diseases, initial misdiagnosis is common. In addition, symptoms differ not only from disease to disease but also from patient to patient suffering from the same disease.

Using Machine Learning for Treatment of Rare Diseases

  • Patient-level data is possible in plenty today, coming in both structured and unstructured forms and 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.
  • 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.
  • 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 can 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 can help raise 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.


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 create 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.

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