‘Precision medicine’ refers to the tailoring of medical treatment depending on a patient’s individual characteristics, e.g., genes, environmental factors, and lifestyle. If physicians could accurately predict an individual patient’s responses to different treatment options, the best option could be selected. For most diseases, the efficacy and safety of standard treatments are highly variable. Thus, individual-specific treatment protocols have been hailed as an emerging revolution in medicine, with the potential to improve patient care and deliver cost savings to health services [1].
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Currently, precision medicine in real-world clinical practice is mainly associated with treatment based on cancer subtype and genotype. For example, olaparib is a monotherapy for ovarian cancer in women with BRCA1/2 mutations [2]. However, there are still few examples of real-world precision medicine. Current clinical practice still relies heavily on subjective judgment and limited individual patient data [3]. A ‘one-drug-fits-all’ approach is often used, in which a particular diagnosis leads to a specific type of treatment. Alternatively, trial-and-error practices are common, in which various treatment options are tried in the hope that one will work.
Machine learning (ML) has been described as ‘the key technology’ for the development of precision medicine [4]. ML uses computer algorithms to build predictive models based on complex patterns in data. ML can integrate the large amounts of data required to “learn” the complex patterns required for accurate medical predictions. ML has excelled in diagnostics, e.g., in neurodegenerative diseases [5], cardiovascular disease [6], and cancer [7]. ML approaches have also been used to predict treatment outcomes for a range of conditions, including schizophrenia [8], depression [9], and cancer [10].
In 2014, IBM launched ‘Watson for Oncology,’ which aimed to use ML to recommend treatment plans for cancer, based on combined inputs from research, a patient’s clinical notes, and the clinician [11]. However, this project has so far failed to deliver the kinds of commercial products which had been envisioned [12]. Other reports have used ML to predict treatment outcomes for cancer. One study used ML to predict patient survival based on microscopy images of cancer biopsy tissue and genomic markers [13]. Another study used ML to predict response to treatment in…

Source: https://swisscognitive.ch/2021/07/19/research-paper-machine-learning/