Сборник предназначен студентам, интернам, резидентам, магистрантам
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sbornikkonferenczii19iyunya2023
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Benefits The application of Machine Learning (ML) in Clinical Decision Support Systems (CDSS) has the potential to benefit various stakeholders in healthcare. Here are three main beneficiaries of ML in CDSS: 1. Healthcare Providers: ML in CDSS can assist healthcare providers, such as physicians, nurses, and clinicians, in making more accurate and informed decisions. ML algorithms can analyze large volumes of patient data, including medical records, lab results, imaging data, and research findings, to provide personalized recommendations, risk assessments, and treatment options. By leveraging ML, healthcare providers can enhance diagnostic accuracy, identify potential risks or complications, and optimize treatment plans, leading to improved patient outcomes and quality of care. 2. Patients: ML in CDSS can empower patients by providing them with personalized insights and recommendations. ML algorithms can analyze individual patient data, including medical history, genetic information, lifestyle factors, and symptom patterns, to offer tailored guidance, preventive measures, and treatment options. Patients can benefit from ML-based CDSS by receiving timely interventions, improved disease management strategies, and increased involvement in their own healthcare decisions. This can lead to better patient engagement, satisfaction, and overall health outcomes. 3. Healthcare Systems and Institutions: ML in CDSS can have a positive impact on healthcare systems and institutions as a whole. By leveraging ML algorithms to analyze population-level data, healthcare systems can identify patterns, trends, and 106 risk factors associated with specific diseases or conditions. This can aid in proactive public health interventions, resource allocation, and strategic planning. ML-based CDSS can also help in optimizing healthcare workflows, reducing medical errors, and improving efficiency in healthcare delivery. By harnessing the power of ML, healthcare systems can work towards achieving cost-effective, patient-centered care and population health management. Overall, the integration of ML into CDSS holds the potential to revolutionize clinical decision-making, enhance patient care, and optimize healthcare systems. It can empower healthcare providers, patients, and healthcare institutions with data- driven insights and personalized recommendations, leading to improved healthcare outcomes and a more efficient and effective healthcare ecosystem. жүктеу/скачать 4,77 Mb. Достарыңызбен бөлісу:
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