{"title":"医疗保健人工智能预测导论","authors":"Anindya Chakravorty","doi":"10.38192/1.8.2.7","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by the increasing availability of healthcare data and rapid progress of analytics techniques.1 AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, modern deep learning, and natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. ","PeriodicalId":75015,"journal":{"name":"The Homoeopathic physician","volume":"30 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Introduction to Artificial Intelligence Prediction for Healthcare\",\"authors\":\"Anindya Chakravorty\",\"doi\":\"10.38192/1.8.2.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by the increasing availability of healthcare data and rapid progress of analytics techniques.1 AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, modern deep learning, and natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. \",\"PeriodicalId\":75015,\"journal\":{\"name\":\"The Homoeopathic physician\",\"volume\":\"30 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Homoeopathic physician\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.38192/1.8.2.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Homoeopathic physician","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.38192/1.8.2.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Introduction to Artificial Intelligence Prediction for Healthcare
Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by the increasing availability of healthcare data and rapid progress of analytics techniques.1 AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, modern deep learning, and natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology.
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