In the healthcare field, one of the advantages it offers is the ability to process large data sets and reliably find trends that help improve patient care.
Machine Learning (ML) is a scientific discipline at the intersection of computer science and statistics, which combines computational and statistical methods to identify patterns in sample data. It is a branch of artificial intelligence that allows machines to learn without being programmed to do so. An essential ability to make systems not only intelligent, but autonomous and capable of identifying patterns in data to turn them into predictions.
For example, Netflix, Facebook, Google and Siri use Machine Learning to personalize the experience of their services. Machine learning basically consists of feeding a computer with a lot of information so that it can then find patterns in the data and act accordingly. For example, Facebook's machine learning algorithm analyzes how each user interacts with content on the platform and, based on that, decides what content users should see next, making each user's Facebook feed different and personalized.
Machine learning basically consists of feeding a computer with a lot of information so that it can then find patterns in the data and act accordingly.
In healthcare, one of the advantages of machine learning is the ability to process huge data sets and reliably find trends or insights that can improve or alter current levels of patient care. For example, Microsoft is working on a way to automatically distinguish tumors from healthy tissue in radiological images, and facial recognition applications are also being developed to help detect genetic disorders.
Machine Learning and rare diseases
A major challenge for patients with rare diseases is obtaining an accurate diagnosis. Typically, patients may have waited eight years to get one, usually due to a lack of knowledge and awareness of their disease on the part of healthcare professionals. ML can help in these cases, and there are some interesting developments in the field of rare diseases where machine learning is being used to try to improve diagnoses. Even so, one of the most important features of ML is that it relies heavily on large data sets, something that is not easily accessible in the case of rare diseases.
In the field of rare diseases, there are some interesting developments in which machine learning is being used to try to improve diagnoses.
Work is currently underway along several lines to harness the potential of ML in addressing rare diseases. The most common uses of Machine Learning in dealing with rare diseases are listed below:
1. Identify patients with rare diseases
ML can be used to identify features in high-dimensional data that correlate highly with the phenotype of a patient or sample and subsequently predict the presence or absence of a rare disease. For example, supervised ML models can be trained on electronic health records, genetic data, or medical images to identify potential new patients with a rare disease.
2. Drug discovery or repurposing
ML can help identify potential drug candidates to treat rare diseases. For example, supervised and unsupervised algorithms trained on genetic and molecular data from high-throughput screens can identify new therapeutic targets to treat a rare disease. In addition, algorithms using knowledge graphs (also known as semantic networks), genomic data, and approved drug databases can identify potential therapeutic candidates for rare diseases.
3. Improving clinical trial design
Optimizing study design and identifying appropriate participants can significantly reduce costs and increase the likelihood of clinical trial success. For example, ML can be used to identify subgroups of patients who are more likely to respond well to a particular treatment. It can also be used to predict drug response in patients with rare diseases.
4. Molecular subtyping of the disease
Rare diseases often exhibit overlapping and heterogeneous phenotypes. ML can be used to identify molecular subtypes of the disease for better understanding. For example, unsupervised ML approaches can help identify new subtypes of a rare disease using molecular and genetic data. Similarly, it can help identify important molecular features exhibited by these subtypes.
5. Predicting patient prognosis
Rare diseases are often accompanied by a lack of in-depth knowledge of the disease mechanism. Biomarkers or clinical features that correlate strongly with adverse outcomes can be beneficial in predicting a patient's prognosis. Supervised ML algorithms may be useful in identifying factors that contribute to the risk of adverse outcomes or progression to advanced disease in patients with rare diseases. Patient stratification can help identify subpopulations of patients who may benefit from early and aggressive interventions.
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