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Medical model is the term coined by psychiatrist R. D. Laing in his The Politics of the Family and Other Essays (1971), for the "set of procedures in which all doctors are trained". [1] It includes complaint, history, physical examination, ancillary tests if needed, diagnosis, treatment, and prognosis with and without treatment.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
The medical model, also known as the normalization model, [22] views disability as a medical disorder, in need of treatment and ultimately cure. [12] Its endpoint is a world where disability no longer exists, as all disabilities have been "cured". [12] In the medical model, physicians are the primary authorities on disability. [21]
The medical model of disability, or medical model, is based in a biomedical perception of disability. This model links a disability diagnosis to an individual's physical body. The model supposes that a disability may reduce the individual's quality of life and aims to correct or diminish the disability with medical intervention. [1]
In 2018, Banerjee et al. [9] proposed a deep learning model for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset ...
Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5]
Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. [10] It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction.
Isogenic human disease models are a family of cells that are selected or engineered to accurately model the genetics of a specific patient population, in vitro.They are provided with a genetically matched 'normal cell' to provide an isogenic system to research disease biology and novel therapeutic agents. [1]