====== Machine learning algorithm ====== {{rss>https://pubmed.ncbi.nlm.nih.gov/rss/search/12guA9dSu1tLGuNw7o6c3dC29ysLLQtKu6fm7-s1NJxnDBXEYc/?limit=15&utm_campaign=pubmed-2&fc=20231026164804}} A [[machine learning]] [[algorithm]] is a set of mathematical and [[statistic]]al [[technique]]s that enables a [[computer]] [[program]] to learn from [[data]] and make [[prediction]]s or [[decision]]s without being explicitly programmed. ---- A machine learning algorithm enables systems to "learn" from patterns in data rather than being explicitly coded with rules for every possible scenario. The process typically involves using data to train a model, which then makes predictions or decisions when new data is encountered. Here’s a more detailed breakdown of key concepts related to machine learning algorithms: Key Concepts: Training Data: The dataset used to "teach" the model. It contains examples with known outcomes (labeled data) that the algorithm learns from. For example, if we’re building a model to diagnose diseases, the training data might include patient records with associated diagnoses. Features: These are the input variables (also called predictors) that the model uses to make predictions. Features can be anything from age and weight in medical datasets to pixel values in images or time-series data from wearable devices. Target Variable: The outcome or label that the model is trying to predict. For instance, in a medical prediction scenario, the target variable might be whether or not a patient will develop a disease. Model: The mathematical representation created by the algorithm after being trained on data. Once trained, the model can be used to make predictions on new, unseen data. Supervised Learning: In this type of machine learning, the algorithm is trained on labeled data. This means the model learns by example, using input-output pairs where the correct answers (labels) are already known. Common algorithms include: Linear Regression (for predicting continuous variables like blood pressure) Logistic Regression (for binary classification, e.g., whether a tumor is benign or malignant) Random Forests, Support Vector Machines (SVM), and Neural Networks. Unsupervised Learning: The algorithm is trained on data that does not have labeled outcomes. Instead, it identifies patterns or groupings (clusters) in the data. Common unsupervised learning techniques include: K-means Clustering (to find groups of similar patients based on medical history) Principal Component Analysis (PCA) (to reduce the dimensionality of data). Reinforcement Learning: The algorithm learns by interacting with an environment, receiving rewards or penalties based on actions taken. This is often used in scenarios like robotics or personalized treatment plans, where the model improves over time by learning from trial and error. Generalization: The ability of a machine learning model to perform well on new, unseen data after being trained on a specific dataset. A well-trained model will generalize well, meaning it doesn’t just memorize the training data (which leads to overfitting) but can apply its learning to new cases. Overfitting and Underfitting: Overfitting occurs when a model is too complex and learns noise or irrelevant patterns from the training data, leading to poor performance on new data. Underfitting happens when the model is too simple and fails to capture important patterns in the data. Model Evaluation: After training, a model's performance is assessed using various metrics: Accuracy: The proportion of correct predictions. Precision and Recall: Metrics often used in medical diagnosis to assess the trade-off between false positives and false negatives. Confusion Matrix: Shows the performance of a classification model by comparing predicted vs. actual outcomes. By using these mathematical and statistical techniques, machine learning algorithms can predict future outcomes based on existing data and refine their performance with more experience (data). ---- Machine learning algorithms and machine learning models are closely related but distinct concepts in the field of machine learning: Machine Learning Algorithm: Definition: A machine learning algorithm is a set of rules, patterns, or statistical techniques used to learn patterns or relationships from data. It's the underlying mathematical or computational formula that processes input data and optimizes model parameters to make predictions or decisions. Purpose: Algorithms are responsible for training a machine learning model. They define how the model adjusts its internal parameters (weights and biases) based on the input data and the desired output (labels or target values). Examples: Common machine learning algorithms include linear regression, decision trees, support vector machines, k-means clustering, and neural network backpropagation. These algorithms have specific mathematical or computational procedures for learning from data. [[Machine Learning Model]]: Definition: A machine learning model is the result of applying a [[machine learning algorithm]] to a specific dataset. It consists of the algorithm's learned parameters, which encode the patterns or relationships discovered during the training process. Purpose: Models are used for making predictions or decisions on new, unseen data. They generalize from the training data to make inferences about data they have not encountered before. Examples: Once trained, a linear regression model would have coefficients for each feature, while a decision tree model would have a tree structure with split nodes and leaf nodes. Neural networks have layers of neurons with associated weights and biases. In summary, a machine learning algorithm is the underlying mathematical or computational process that enables a model to learn from data, while a machine learning model is the result of applying that algorithm to specific training data, capturing the learned patterns. The model is the tangible entity that can be deployed and used for predictions or decisions. The algorithm, on the other hand, is the abstract concept that describes the learning process itself. ---- Machine learning algorithms are the core components of machine learning models, which are trained on data to recognize patterns, make predictions, or perform specific tasks. Here are some common types of machine learning algorithms: ===== Classification ===== [[Supervised Machine Learning]]: [[Unsupervised Machine Learning]]: [[Clustering]] Semi-Supervised Learning: These algorithms combine elements of both supervised and unsupervised learning to make predictions when only a portion of the data is labeled. Reinforcement Learning: Reinforcement learning focuses on training agents to make sequential decisions by learning from trial and error. Common reinforcement learning algorithms include: Q-Learning Deep Q-Networks (DQN) Policy Gradient Methods Proximal Policy Optimization (PPO) Actor-Critic Models Deep Learning: Deep learning algorithms are neural networks with multiple layers. They are used for complex tasks like image and speech recognition, natural language processing, and more. Key deep learning architectures include: Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) Transformer Models (e.g., BERT, GPT) Ensemble Methods: Ensemble methods combine the predictions of multiple models to improve overall performance and reduce overfitting. Common ensemble methods include: Bagging (e.g., Random Forest) Boosting (e.g., AdaBoost, Gradient Boosting) Stacking Hybrid Models: Hybrid models combine elements of different machine learning approaches to address specific problems or combine their strengths. Meta-Learning: Meta-learning is about training models to learn how to learn. It focuses on improving a model's learning and adaptation capabilities. Self-Supervised Learning: Self-supervised learning involves creating supervised-like tasks from unlabeled data to pre-train models before fine-tuning them on specific tasks. Online Learning: Online learning algorithms are designed to learn and adapt in real-time as new data becomes available. This classification provides an overview of the different types of machine learning algorithms and the tasks they are designed to tackle. The choice of algorithm depends on the nature of the problem and the characteristics of the data. ===== Supervised Learning Algorithms ===== Linear Regression: Used for predicting a continuous numeric output based on input features by fitting a linear equation to the data. Logistic Regression: Applied in classification tasks to predict binary outcomes (e.g., yes/no) or multiclass outcomes (e.g., cat, dog, bird). Support Vector Machines (SVM): Used for classification and regression tasks by finding a hyperplane that best separates data points in different classes or fits a regression model. Decision Trees: Tree-like structures that make decisions by recursively splitting data into subsets based on the most significant features. Random Forest: An ensemble method that combines multiple decision trees to improve prediction accuracy and reduce overfitting. Gradient Boosting Machines (GBM): An ensemble technique that combines weak learners (usually decision trees) to create a strong predictive model. Neural Networks: Deep learning models composed of interconnected artificial neurons that can be used for various tasks, including image recognition, natural language processing, and more. ===== Classification ===== ==== Unsupervised Learning Algorithms ==== K-Means Clustering: Used to partition data into clusters based on similarity or distance metrics, often used for customer segmentation and image compression. Hierarchical Clustering: Builds a hierarchical representation of data by iteratively merging or splitting clusters. Principal Component Analysis (PCA): A dimensionality reduction technique used to transform data into a lower-dimensional space while preserving as much variance as possible. t-Distributed Stochastic Neighbor Embedding (t-SNE): A technique for visualizing high-dimensional data by reducing it to a lower-dimensional space while preserving neighborhood relationships. Autoencoders: Deep learning models used for unsupervised feature learning and data compression. Reinforcement Learning Algorithms: Q-Learning: A model-free reinforcement learning algorithm that learns to make decisions by maximizing expected cumulative rewards. Deep Q-Networks (DQN): Combines Q-Learning with deep neural networks to handle complex environments and state spaces. Policy Gradient Methods: Algorithms that learn directly the optimal policy for an agent in a reinforcement learning setting. Semi-Supervised Learning Algorithms: These algorithms combine elements of both supervised and unsupervised learning, typically by using a small amount of labeled data and a larger amount of unlabeled data to improve model performance. Anomaly Detection Algorithms: Used to identify rare or unusual patterns in data, often employed in fraud detection, network security, and quality control. Natural Language Processing (NLP) Algorithms: Specialized algorithms for text data, including tokenization, word embeddings (e.g., Word2Vec, GloVe), and recurrent neural networks (e.g., LSTM, GRU) for language understanding and generation. Computer Vision Algorithms: Algorithms designed for image and video data, such as convolutional neural networks (CNNs) for image classification, object detection, and image segmentation. Machine learning algorithms are selected and tailored to specific tasks based on the nature of the data, the problem to be solved, and the desired outcomes. The choice of algorithm, along with appropriate data preprocessing and feature engineering, plays a crucial role in the success of machine learning projects. ---- Machine [[learning]] can be defined as a situation where a [[machine]] is given a [[task]] in which the machine performance improves with [[experience]] ((Haykin S.S. Neural Networks and Learning Machines. Volume 3 Pearson; Upper Saddle River, NJ, USA: 2009.)) Its a domain of [[artificial intelligence]] that allows [[computer algorithm]]s to learn patterns by studying [[data]] directly without being explicitly programmed ((Mitchell, TM. Machine Learning . Vol. 1. New York: McGraw-Hill Science/Engineering/Math; 1997.)) ((Noble WS. What is a support vector machine? Nat Biotechnol . 2006;24(12):1565-1567.)). ML methods are already widely applied in multiple aspects of our daily lives, although this is not always obvious to the casual observer; common examples are email spam filters, search suggestions, online shopping suggestions, and speech recognition in smartphones ((Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science . 2015;349(6245):255-260.)). [[Machine learning]] algorithms have the capacity to use extensive [[dataset]]s involving numbers of features to separate groups ((Samuel AL (1988) Some studies in machine learning using the game of checkers. In: Computer games I. Springer, New York, pp 366–400)) ((Obermeyer Z, Emanuel EJ (2016) Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med 375(13): 1216)) ((Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML et al (2017) Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery 83(2):181–192)) ((Azimi P, Mohammadi HR, Benzel EC, Shahzadi S, Azhari S, Montazeri A (2015) Artificial neural networks in neurosurgery. J Neurol Neurosurg Psychiatry 86(3):251–256)) ((Watson RA (2014) Use of a machine learning algorithm to classify expertise: analysis of hand motion patterns during a simulated surgical task. Acad Med 89(8):1163–1167)). Machine learning [[algorithm]]s can be divided into 3 broad categories — [[supervised learning]], [[unsupervised learning]], and [[reinforcement learning]]. Supervised learning is useful in cases where a property (label) is available for a certain [[dataset]] (training set), but is missing and needs to be predicted for other instances. Unsupervised learning is useful in cases where the challenge is to discover implicit relationships in a given unlabeled dataset (items are not pre-assigned). Reinforcement learning falls between these 2 extremes — there is some form of feedback available for each predictive step or action, but no precise label or error message. ===== Neurosurgery ===== {{rss>https://pubmed.ncbi.nlm.nih.gov/rss/search/1NCQ0JrPU2JYIwAmc7u0p7g-g5ZaVO9l_ahHLuIFY8qVVCOMsL/?limit=15&utm_campaign=pubmed-2&fc=20230904112421}} ---- ---- Although rates of [[postoperative]] [[morbidity]] and [[mortality]] have become relatively low in patients undergoing [[transnasal transsphenoidal]] surgery (TSS) for [[pituitary neuroendocrine tumor]], [[cerebrospinal fluid fistula]]s remain a major driver of postoperative morbidity. Persistent CSF fistulas harbor the potential for [[headache]] and [[meningitis]]. Staartjes et al., trained and internally validated a robust deep neural network-based prediction model that identifies patients at high risk for intraoperative CSF. [[Machine learning algorithm]]s may predict outcomes and adverse events that were previously nearly unpredictable, thus enabling safer and improved patient care and better patient counseling ((Staartjes VE, Zattra CM, Akeret K, Maldaner N, Muscas G, Bas van Niftrik CH, Fierstra J, Regli L, Serra C. Neural network-based identification of patients at high risk for intraoperative cerebrospinal fluid leaks in endoscopic pituitary surgery. J Neurosurg. 2019 Jun 21:1-7. doi: 10.3171/2019.4.JNS19477. [Epub ahead of print] PubMed PMID: 31226693. )). ---- [[Variance]] between [[provider]]s in the neurosurgical field leads to inefficiencies and poor patient [[outcome]]s. [[Evidence-based guidelines]] (EBGs) have been developed as a means of pooling the body of [[evidence]] in the [[literature]] to provide [[clinician]]s with the most comprehensive [[data]]-driven [[recommendation]]s. However, these EBGs are not being implemented well into the clinician [[workflow]], and therefore clinicians are left to make [[decision]]s with incomplete [[information]]. Underutilized are [[electronic health record]]s (EHRs), which house enormous health [[data]], but which have failed to capitalize on the power of that '[[big data]].' Early attempts at EBGs were rigid and not adaptive, but with the current advances in data [[informatics]] and [[machine learning algorithm]]s, it is now possible to integrate 'big data' and rapid data processing into clinical decision support tools. As we strive towards variance reduction in healthcare, the integration of 'big data' and EBGs for decision-making are key. Stopa et al., proposed that EHRs are an ideal platform for integrating EBGs into the clinician workflow. With this model, it will be possible to build EBGs into the EHR software, to continuously update and optimize EBGs based on the flow of patient data into the EHR, and to present data-driven clinical decision support at the point of care. Variance reduction in neurosurgery through the integration of evidence-based decision support in [[electronic health record]]s will lead to improved patient [[safety]], reduction of medical [[error]]s, maximization of available data, and enhanced decision-making power for clinicians ((Stopa BM, Yan SC, Dasenbrock HH, Kim DH, Gormley WB. Variance reduction in neurosurgical practice: The case for analytics driven decision support in the era of Big Data. World Neurosurg. 2019 Feb 21. pii: S1878-8750(19)30414-0. doi: 10.1016/j.wneu.2019.01.292. [Epub ahead of print] PubMed PMID: 30797905. )).