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Figure 2 Electronic Health Records (EHR) and Clinical Decision Support Source
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- A look inside ML
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Electronic Health Records (EHR) and Clinical Decision Support Source : Røst TB, Clausen C, Nytrø Ø, Koposov R, Leventhal B, Westbye OS, Bakken V, Flygel LHK, Koochakpour K and Skokauskas N (2020) Local, Early, and Precise: Designing a Clinical Decision Support System for Child and Adolescent Mental Health Services. Front. Psychiatry 11:564205. doi: 10.3389/fpsyt.2020.564205 A look inside ML Machine Learning (ML) is a branch of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed. ML systems are designed to analyze data, identify patterns, and make informed decisions or predictions based on the patterns discovered. Here are the main principles that underpin the workings of ML: 1. Data Collection: ML relies on vast amounts of data to train its algorithms. This data can come from various sources, such as electronic health records, medical imaging, sensor readings, or patient-generated data. The quality and diversity of the data play a crucial role in the performance and generalization capabilities of ML models. 2. Training and Learning: ML models are trained using labeled data, where the desired output or outcome is already known. During the training process, the model learns to recognize patterns and relationships within the data to make accurate predictions or decisions. This is typically done through the use of statistical techniques and optimization algorithms that adjust the model's parameters to minimize errors or maximize performance. 3. Feature Extraction and Selection: ML algorithms require relevant features or attributes from the data to make predictions or decisions. Feature extraction involves identifying and transforming the raw data into a more suitable representation that captures the underlying patterns. Feature selection involves choosing the most informative features that contribute the most to the model's predictive power while minimizing redundancy. 105 4. Model Evaluation and Validation: ML models need to be evaluated and validated to ensure their accuracy and performance. This is done by using separate datasets that were not used during the training phase. Various metrics and techniques, such as cross-validation or hold-out validation, are employed to assess the model's performance, identify potential biases or overfitting, and fine-tune the model if necessary. 5. Deployment and Monitoring: Once a ML model has been trained and validated, it can be deployed for real-world use. Continuous monitoring is essential to ensure that the model's performance remains reliable and that it adapts to any changes or shifts in the data distribution. Monitoring also helps detect potential biases, ethical concerns, or performance degradation over time. 6. Iterative Improvement: ML is an iterative process where models are continually refined and improved based on feedback and new data. As more data becomes available or new insights are gained, models can be retrained or updated to enhance their accuracy and adaptability. These principles form the foundation of how ML systems work. However, it's important to note that different ML algorithms and techniques exist, such as supervised learning, unsupervised learning, and reinforcement learning, each with its own specific principles and methodologies. жүктеу/скачать 4,77 Mb. Достарыңызбен бөлісу:
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