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Epi Info is public domain statistical software for epidemiology developed by Centers for Disease Control and Prevention. [1]Spatiotemporal Epidemiological Modeler is a tool, originally developed at IBM Research, for modelings and visualizing the spread of infectious diseases.
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
Entrepreneurs in healthcare have been effectively using seven business model archetypes to take AI solution to the marketplace. These archetypes depend on the value generated for the target user (e.g. patient focus vs. healthcare provider and payer focus) and value capturing mechanisms (e.g. providing information or connecting stakeholders).
Hierarchical temporal memory (HTM) models some of the structural and algorithmic properties of the neocortex. HTM is a biomimetic model based on memory-prediction theory. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an increasingly complex model of the world.
IHME model – Institute for Health Metrics and Evaluation COVID model; MEmilio [35] – an open source high performance Modular EpideMIcs simuLatIOn software based on hybrid graph-SIR-type model [36] with commuter testing between regions [37] and vaccination strategies [38] and agent-based models
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
The need for a "uniform mechanism to educate, evaluate, and certify simulation instructors for the health care profession" was recognized by McGaghie et al. in their critical review of simulation-based medical education research. [5] In 2012 the SSH piloted two new certifications to provide recognition to educators to meet this need. [6]