Сборник предназначен студентам, интернам, резидентам, магистрантам
Figure 1 Machine Learning Algorithm Schema Source
жүктеу/скачать 4,77 Mb. Pdf көрінісі
|
sbornikkonferenczii19iyunya2023
- Бұл бет үшін навигация:
- The future is here
| Figure 1
Machine Learning Algorithm Schema Source: Sanchez-Martinez S, Camara O, Piella G, Cikes M, González-Ballester MÁ, Miron M, Vellido A, Gómez E, Fraser AG and Bijnens B (2022) Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging.Front. Cardiovasc. Med.8:765693. doi: 10.3389/fcvm.2021.765693 The future is here Machine Learning (ML) is being utilized across various domains in healthcare to improve patient care, enhance efficiency, and advance medical research. Here are three main domains where ML is prominently used: 1. Medical Imaging and Diagnostics: ML algorithms are employed to analyze medical images such as X-rays, MRI scans, CT scans, and mammograms. These algorithms can assist in the detection and diagnosis of various conditions, including tumors, lesions, and abnormalities. ML models can learn from vast image datasets to identify patterns and markers that may be indicative of specific diseases, enabling earlier detection and more accurate interpretation of medical images. 2. Predictive Analytics and Risk Stratification: ML is employed to develop predictive models that assess the risk of certain medical events or conditions. By analyzing large datasets containing patient information, ML algorithms can identify risk factors, predict disease progression, and estimate the likelihood of future health outcomes. This information can aid in personalized treatment planning, preventive interventions, and proactive patient management. 3. Electronic Health Records (EHR) and Clinical Decision Support: ML algorithms are applied to analyze electronic health records, which contain comprehensive patient information such as medical history, laboratory results, medication records, and demographics. ML models can identify patterns and trends within these records to provide decision support to healthcare professionals. This can include suggesting appropriate treatments, predicting adverse events, optimizing medication management, and alerting to potential drug interactions or patient risks (Fig. 2) . These are just a few examples of how ML is utilized in healthcare. ML is a rapidly evolving field, and its applications continue to expand into other domains such as genomics, precision medicine, wearable devices, and telemedicine. The integration of ML technologies has the potential to transform healthcare by enabling 104 more accurate diagnoses, personalized treatment plans, and improved patient outcomes. жүктеу/скачать 4,77 Mb. Достарыңызбен бөлісу:
|