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Machine Learning and Personalized Medicine: Not So Strange Bedfellows!

The excitement and promise of personalized medicine is evolving and growing over time


On a parallel track, Machine Learning (ML) continues to make its mark across industries, from manufacturing to healthcare. Personalized medicine is the tailoring of medical and drug treatments to the individual characteristics of each patient using the person's unique DNA and genetic profile to identify what makes them at risk for certain conditions and in determining treatments for their best clinical and behavioral health outcomes. 🔹 Benefits of personalized medicine 🔹 1️⃣ To reduce trial and error-based treatment decisions leading to more proactive action. 2️⃣ To bring down the burdens associated with a condition both in terms of health and finance. 3️⃣ Emphasis would be on preventive care versus rather than the reactive treatment. The challenge lies in identifying an optimal treatment as the number of possible predictors of good response like genetic and other biomarkers, plus treatment options are growing rapidly along with added complexity. This is where ML technology comes in. ML can identify patterns of data to predict or detect unseen health and disease patterns. ML algorithms can then create target-based treatment plans based upon clinical, genomics, laboratory, nutrition, and lifestyle-related data. 🔹 Role of Machine Learning 🔹 1️⃣ The use of multi-modal data helps in deeper analysis of large datasets which improves the understanding of human health and disease. 2️⃣ ML is capable of identifying hidden patterns of data, thus many future diseases can be prevented. 3️⃣ Reduction of healthcare costs for unnecessary screenings and invasive testing for diseases such as lung cancer, diabetes, heart diseases, etc. In order to maximize the collaboration of ML and personalized medicine, the knowledge of stakeholders, such as providers, lab/radiology technicians, pharmacists, data analysts and programmers would need to be upskilled so that everyone involved can use the insights for better decision-making. Finally, outcomes gleaned from personalized medicine and ML can also translate to wider population health programs by identifying, screening and risk stratifying patient groups who would benefit from specific preventive care or treatment protocols based on their genetic profiles. Exciting possibilities abound! Learn More Here ➡️ https://lnkd.in/ebyfuJXb Are there ways that ML and personalized medicine can create better outcomes for your organization? Tell us! 📝 #machinelearning #artificialintelligence #technology #medicine #personalizedmedicine #data #healthcare


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