Precision medicine is an emerging approach that provides customized or personalized medical treatment and patient care. In the conventional medical treatment approach, medicines are prescribed using the signs-and-symptom approach, and these approaches rely on one-size fit all principle. In contrast to this, precision medicine use signs-and-symptoms and phenotype information to prescribe customized medicines. Phenotype information includes externally visible traits such as age, sex, the colour of hair, height, cultural background etc. The novelty of the precision medicine approach is the incorporation of a wide array of individual data, including clinical, lifestyle, genetic and further bio-marker information during the diagnosis phase.
Precision medicine can be viewed as a complex iterative process that aims to improve upon precision during each iteration by recommending therapies to smaller and smaller groups of patients that share common phenotype data. Hence, precision medicine consists of the execution of the following activities iteratively.
Deep Phenotyping this step aims to gather data from as many sources as possible, including medical history, lifestyle, physical examination, imaging, comprehensive molecular analysis, and gene expression analysis. Such gathered data turns out to be 'big data because it is voluminous, varied, exponentially increasing, and verified data. Handling such big-data needs computationally efficient methods and algorithms
Building Diagnosis or Prognosis models The objectives of this step are: to identify the most relevant attributes helping to perform diagnosis/prognosis of a disease, to identify differentiating variables, and to cluster the patients having similar traits, including genotypic and phenotypic. This clustering of patients is essential to devise targeted therapies.
Predicting Treatment Response The objective of this step is to predict how patients respond to different medicines or schedule of medicines.
All the above activities can be perceived and represented as information processing tasks. Hence these activities can be implemented using the techniques and algorithms developed in Artificial Intelligence, Big-data algorithms, and deep learning methods. Further, the development of electronic health records and the availability of high-performance computing devices also play a crucial role in implementing the precision medicine approach to medical treatment and health care.
The development of precision medicine techniques is disease-specific, aiming to identify gene/protein causing disease and a prescriptive drug. For example, Chronic myeloid leukaemia, a kind of cancer disease, is associated with the BCR-ABL gene, and Imatinib is a prescriptive drug.
So far, precision medicine-based techniques have developed for many diseases, including Thrombosis, HIV/AIDS, and Coronary artery disease are few examples. Also, precision medicine-based techniques have multiple advantages, which include
it improves disease detection,
it customizes disease-prevention strategies and
it prescribes more effective drugs.