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  2. Artificial intelligence in healthcare - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_in...

    As more data is collected, machine learning algorithms adapt and allow for more robust responses and solutions. [99] Numerous companies are exploring the possibilities of the incorporation of big data in the healthcare industry. Many companies investigate the market opportunities through the realms of "data assessment, storage, management, and ...

  3. Health informatics - Wikipedia

    en.wikipedia.org/wiki/Health_informatics

    Medical informatics introduces information processing concepts and machinery to the domain of medicine. Health informatics is the study and implementation of computer structures and algorithms to improve communication, understanding, and management of medical information. [1] It can be viewed as a branch of engineering and applied science.

  4. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. [1]

  5. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high ...

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  7. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    v. t. e. Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]

  8. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    In machine learning applications where logistic regression is used for binary classification, the MLE minimises the cross-entropy loss function. Logistic regression is an important machine learning algorithm. The goal is to model the probability of a random variable being 0 or 1 given experimental data.

  9. Progress in artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Progress_in_artificial...

    Progress in artificial intelligence (AI) refers to the advances, milestones, and breakthroughs that have been achieved in the field of artificial intelligence over time. AI is a multidisciplinary branch of computer science that aims to create machines and systems capable of performing tasks that typically require human intelligence.