A rare disease is one that affects fewer than 200,000 people in the US, according to the US FDA. A research claims that 300 MM individuals globally suffer from one of the 7,000 uncommon diseases that exist. Seventy percent of uncommon genetic diseases begin in childhood, and two thirds of rare diseases are genetic in origin.
The numbers are astounding, and patients often struggle to receive the right diagnosis and care due to our limited understanding of many uncommon diseases. However, only 5% of rare diseases have access to effective treatments. The research and commercialization of treatments for rare diseases is a difficult field for any corporation, thus pharmaceutical companies have historically been unwilling to invest in this market.
Investments in the field are harder to justify due to the economy and the low disease awareness.
Yet, in recent years, governments have used a variety of grants and incentives to encourage the creation and marketing of medicines for rare diseases. They consist of:
Market exclusivity that is extended,
Funding for drug development, tax credits
expedited approval processes (e.g., FDA Breakthrough Therapy designation)
lessening the statistical burdens associated with clinical development
We are witnessing a boom in rare illness research and an increase in the approval of rare disease medications as a result of these focused efforts. For the foreseeable future, it is anticipated that the positive trend will continue.
Identification and management challenges
The diagnosis of rare diseases is fraught with difficulties and obstacles. According to data, it usually takes patients with uncommon diseases 7.6 years to receive a proper diagnosis. Before receiving a proper diagnosis, patients frequently see numerous doctors; it’s likely that the patient will first receive two or three incorrect diagnoses.
Disorders that affect fewer than five individuals per 10,000 people are considered rare in Europe. Yet only when each rare disorder is taken into account, are they truly rare. As there are more than 7,000 uncommon diseases known to exist, there is still a significant global health burden. According to recent statistics, the population prevalence ranges from 3.5% to 5.9%. However, given the lack of epidemiological information on many rare conditions, the actual prevalence is probably higher.
There are numerous difficulties that are specific to rare diseases.
The first step is to raise awareness, which, given the small afflicted population, may be fairly low. Here, the patient advocate group might be quite important.
The diagnosis process is extremely difficult and uncomfortable. During this time, patients switch doctors frequently, receive a number of incorrect diagnoses, and search for a doctor with the right training to make a diagnosis. This journey is filled with heartache and frustration, and for some people it can be too late because many diseases can be fatal.
Treatment Choice comes in third. The disorders and available therapies are very intricate. To make sure that both patients and doctors are aware of the advantages of the treatment options, they need a lot of scientific and clinical communications. Education is therefore vitally important.
The process of getting access to therapy can be very difficult. Patients who are ill require a lot of support to get access to these pricey medicines because getting through the insurance and managed care systems may be quite difficult.
Patient Management comes last. Programs for patient support are essential for continual instruction and assistance. The process of identifying an illness, diagnosing it, and then treating it is quite drawn-out and complicated. Strong compliance and adherence to treatment will be made possible by patient support services.
The management of rare diseases is highly challenging due to the diagnostic odyssey of the patients. In order to support the decision-making process with automated and quantitative methods, fresh tech-enabled solutions are needed. Machine Learning (ML) offers a wide range of effective inference techniques in this circumstance. Yet, using advanced statistical approaches to match health conditions raises problematic technological, methodological, and ethical issues.
Possible Applications of Machine Learning (ML)
Thankfully, developments in ML and data science have opened up new avenues for aiding in the diagnosis and treatment of these conditions. In order to find new treatments, ML algorithms can be used to identify patterns in massive amounts of patient data[4]. In order to facilitate early intervention and prompt treatment to avert the full manifestation of disease, ML can also assist in the detection of early warning signals of rare conditions.
Moreover, ML can be used to evaluate the efficacy of current medicines, enabling them to be optimized while minimizing unwanted effects. Moreover, quick analysis of huge data sets from clinical trials and other sources might guarantee quicker, more precise diagnosis and treatment.
The diagnosis and treatment of uncommon illnesses could be revolutionized by artificial intelligence (AI) and machine learning (ML). Clinicians can delve deeper into patient data with the aid of digital tools and algorithms, uncovering connections and patterns that are impossible to spot with conventional methods. Gaps in each patient’s needs can be found through comprehending their complexity, opening the door for individualized therapy.
Unprecedented insights into the causes, symptoms, and treatment options are possible with AI/ML. Also, new medications that assist improve the lives of those who are impacted can be developed using these tools and algorithms to identify potential drug targets.
Handling Restrictions and Ethical Issues
Yet it’s important to understand that ML also has significant limits when it comes to rare diseases. For instance, in order to train and identify specific patterns, ML systems need enormous amounts of data. Yet, there is little data available that may be utilized for ML because such illnesses are uncommon. Due to the less-than-ideal resilience of the data, this could result in erroneous results since the algorithms might not be able to recognize patterns with high precision.
Furthermore, even if ML finds patterns, it can still require assistance to fully identify the causes of these issues. So, to understand the complexity of rare conditions and offer appropriate remedies, human interaction is necessary. Data pattern identification flaws can also be a problem since, given the sparse data, the algorithms might identify a bogus pattern.
Concerns about ethical issues in applying ML to patient data have emerged in recent years. The moral ramifications of how the patient data obtained is handled must be taken into account when new investments in AI and data analytics are made.
For organizations employing ML for rare diseases, a code of ethics may be necessary to guarantee that the technology is exclusively used for the benefit of patients and not for commercial gain. Data privacy must be prioritized while gathering and using this type of patient information.
For effective patient outcomes and increased quality of life, patients with rare diseases need a customer-centric support approach that is intensely focused on helping them understand their disease, get education and treatment assistance, and build trust.
