標(biāo)題: Titlebook: Artificial Intelligence and Machine Learning in Health Care and Medical Sciences; Best Practices and P Gyorgy J. Simon,Constantin Aliferis [打印本頁(yè)] 作者: CLAST 時(shí)間: 2025-3-21 16:39
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書(shū)目名稱(chēng)Artificial Intelligence and Machine Learning in Health Care and Medical Sciences影響因子(影響力)學(xué)科排名
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書(shū)目名稱(chēng)Artificial Intelligence and Machine Learning in Health Care and Medical Sciences網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Artificial Intelligence and Machine Learning in Health Care and Medical Sciences被引頻次
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書(shū)目名稱(chēng)Artificial Intelligence and Machine Learning in Health Care and Medical Sciences年度引用
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書(shū)目名稱(chēng)Artificial Intelligence and Machine Learning in Health Care and Medical Sciences讀者反饋
書(shū)目名稱(chēng)Artificial Intelligence and Machine Learning in Health Care and Medical Sciences讀者反饋學(xué)科排名
作者: Fecundity 時(shí)間: 2025-3-22 00:05 作者: 評(píng)論性 時(shí)間: 2025-3-22 03:24 作者: Diatribe 時(shí)間: 2025-3-22 06:35
Reporting Standards, Certification/Accreditation, and Reproducibility,rks; (b) recent efforts for accrediting health care provider organizations for AI readiness and maturity; (c) professional certification; and (d) education and related accreditation in the space of educational programs of data science and biomedical informatics specific to AI/ML.作者: Glutinous 時(shí)間: 2025-3-22 09:26 作者: BLAZE 時(shí)間: 2025-3-22 13:04
https://doi.org/10.1007/978-3-540-85017-5ormal vs. heuristic systems: computability, incompleteness theorem, space and time complexity, exact vs. asymptotic complexity, complexity classes and how to establish complexity of problems even in the absence of known algorithms that solve them, problem complexity vs. algorithm and program complex作者: ALIBI 時(shí)間: 2025-3-22 17:37
eCustomer Relationship Management,ders who may already know about some or all of these methods. The former will find here a useful introduction and review. The latter will find additional insights as we critically revisit the key concepts and add summary guidance on whether and when each technique is applicable (or not) in healthcar作者: invulnerable 時(shí)間: 2025-3-23 00:36
eCustomer Relationship Management,ity of biomedical ML focuses on predictive modeling and does not address causal methods, their requirements and properties. Yet these are essential for determining and assisting patient-level or healthcare-level interventions toward improving a set of outcomes of interest. Moreover causal ML techniq作者: 殺子女者 時(shí)間: 2025-3-23 03:47
eCustomer Relationship Management,AI/ML methods that can address them. The stages are explained and grounded using existing methods?examples. The process discussed equates to a generalizable Best Practice guideline applicable across all of AI/ML. An equally important use of this Best Practice is as a guide for understanding and eval作者: DNR215 時(shí)間: 2025-3-23 07:56
eCustomer Relationship Management, feasibility, exploratory, or pre-clinical ones. The steps outlined span from requirements engineering to deployment and monitoring and also emphasize a number of contextual factors determining success such as clinical and health economic considerations. AI’s “knowledge cliff” is discussed and the n作者: Substance-Abuse 時(shí)間: 2025-3-23 09:56 作者: 長(zhǎng)處 時(shí)間: 2025-3-23 16:57
https://doi.org/10.1007/978-3-540-89328-8rce data elements are transformed into data design features that are specified in the data design through an iterative process of mapping data elements to concepts, value sets, and phenotype expressions. Data that meet the data design criteria are extracted into a data mart where the quality of the 作者: Hangar 時(shí)間: 2025-3-23 19:58 作者: 潔凈 時(shí)間: 2025-3-23 23:20
eCustomer Relationship Management, importance for the success of ML/AI modeling. In modern ML/AI practice OF/UF are typically interacting with error estimator procedures and model selection, as well as with sampling and reporting biases and thus need be considered together in context. The more general situations of over confidence (作者: Irremediable 時(shí)間: 2025-3-24 03:57 作者: 悄悄移動(dòng) 時(shí)間: 2025-3-24 07:03 作者: Abnormal 時(shí)間: 2025-3-24 11:06 作者: hyperuricemia 時(shí)間: 2025-3-24 16:44
eCustomer Relationship Management,ported in the biomedical literature. In this chapter, we will discuss the background, resources and methods used in biomedical natural language processing (NLP), which will help unlock information from the textual data.作者: 思想流動(dòng) 時(shí)間: 2025-3-24 22:13 作者: 悄悄移動(dòng) 時(shí)間: 2025-3-24 23:29 作者: Charade 時(shí)間: 2025-3-25 06:41
Achieving Interorganizational Connectivityal structure and assistive checklists intended to make them operationally useful. We differentiate between macro-, meso- and micro-levels of pitfalls and corresponding best practices-roughly corresponding to high-level principles, concrete differentiations of the above and granular/detailed tools an作者: 察覺(jué) 時(shí)間: 2025-3-25 09:16 作者: 辮子帶來(lái)幫助 時(shí)間: 2025-3-25 13:00 作者: Colonnade 時(shí)間: 2025-3-25 17:33 作者: 機(jī)密 時(shí)間: 2025-3-25 22:33
978-3-031-39357-0The Editor(s) (if applicable) and The Author(s) 2024作者: Mucosa 時(shí)間: 2025-3-26 02:16 作者: MIRTH 時(shí)間: 2025-3-26 04:55 作者: Eructation 時(shí)間: 2025-3-26 11:29
Artificial Intelligence and Machine Learning in Health Care and Medical Sciences978-3-031-39355-6Series ISSN 1431-1917 Series E-ISSN 2197-3741 作者: 鞭子 時(shí)間: 2025-3-26 12:41
https://doi.org/10.1007/978-3-540-89328-8izes main results from the literature comparing empirical performance of AI/ML vs humans. The chapter then addresses foundations of human heuristic decision making (and important related biases), and contrasts those with AI/ML biases. Finally the chapter touches upon how hybrid human/machine intelligence can outperform either approach.作者: Mri485 時(shí)間: 2025-3-26 19:55 作者: APEX 時(shí)間: 2025-3-27 00:05
eCustomer Relationship Management,ported in the biomedical literature. In this chapter, we will discuss the background, resources and methods used in biomedical natural language processing (NLP), which will help unlock information from the textual data.作者: Decrepit 時(shí)間: 2025-3-27 02:05
The Evolution of eBusiness in Healthcarerks; (b) recent efforts for accrediting health care provider organizations for AI readiness and maturity; (c) professional certification; and (d) education and related accreditation in the space of educational programs of data science and biomedical informatics specific to AI/ML.作者: 大洪水 時(shí)間: 2025-3-27 08:44 作者: 短程旅游 時(shí)間: 2025-3-27 12:01
Foundations and Properties of AI/ML Systems,ormal vs. heuristic systems: computability, incompleteness theorem, space and time complexity, exact vs. asymptotic complexity, complexity classes and how to establish complexity of problems even in the absence of known algorithms that solve them, problem complexity vs. algorithm and program complex作者: fertilizer 時(shí)間: 2025-3-27 16:09
An Appraisal and Operating Characteristics of Major ML Methods Applicable in Healthcare and Health ders who may already know about some or all of these methods. The former will find here a useful introduction and review. The latter will find additional insights as we critically revisit the key concepts and add summary guidance on whether and when each technique is applicable (or not) in healthcar作者: mechanical 時(shí)間: 2025-3-27 21:05 作者: 松軟 時(shí)間: 2025-3-28 01:02
Principles of Rigorous Development and of Appraisal of ML and AI Methods and Systems,AI/ML methods that can address them. The stages are explained and grounded using existing methods?examples. The process discussed equates to a generalizable Best Practice guideline applicable across all of AI/ML. An equally important use of this Best Practice is as a guide for understanding and eval作者: FUSC 時(shí)間: 2025-3-28 06:08 作者: Leaven 時(shí)間: 2025-3-28 08:10 作者: 刺激 時(shí)間: 2025-3-28 11:15 作者: Interferons 時(shí)間: 2025-3-28 17:52 作者: TATE 時(shí)間: 2025-3-28 19:49 作者: 吼叫 時(shí)間: 2025-3-29 00:27
,From “Human versus Machine” to “Human with Machine”,izes main results from the literature comparing empirical performance of AI/ML vs humans. The chapter then addresses foundations of human heuristic decision making (and important related biases), and contrasts those with AI/ML biases. Finally the chapter touches upon how hybrid human/machine intelli作者: 過(guò)于平凡 時(shí)間: 2025-3-29 06:56
Lessons Learned from Historical Failures, Limitations and Successes of AI/ML in Healthcare and the supporting their use. Examples include: the Gartner hype cycle; the infamous “AI winters”; limitations of early-stage knowledge representation and reasoning?methods; overfitting; using methods not built for the task; over-estimating the value and potential or early and heuristic technology; developi作者: 效果 時(shí)間: 2025-3-29 07:41 作者: 不能強(qiáng)迫我 時(shí)間: 2025-3-29 13:20 作者: 逗它小傻瓜 時(shí)間: 2025-3-29 19:15 作者: crease 時(shí)間: 2025-3-29 20:44 作者: 青春期 時(shí)間: 2025-3-30 02:00 作者: DALLY 時(shí)間: 2025-3-30 04:42 作者: 損壞 時(shí)間: 2025-3-30 10:44 作者: CONE 時(shí)間: 2025-3-30 13:06
eCustomer Relationship Management,izable Best Practice guideline applicable across all of AI/ML. An equally important use of this Best Practice is as a guide for understanding and evaluating any ML/AI technology under consideration for adoption for a particular problem domain.作者: 花束 時(shí)間: 2025-3-30 18:29
https://doi.org/10.1007/978-3-540-89328-8s to concepts, value sets, and phenotype expressions. Data that meet the data design criteria are extracted into a data mart where the quality of the data can be assessed. Once data are of sufficient quality and meet expectations, ML features are developed for use in machine learning models.作者: embolus 時(shí)間: 2025-3-30 23:07
Foundations of Causal ML,r determining and assisting patient-level or healthcare-level interventions toward improving a set of outcomes of interest. Moreover causal ML techniques can be instrumental for health science discovery.作者: Irrepressible 時(shí)間: 2025-3-31 03:04 作者: Regurgitation 時(shí)間: 2025-3-31 09:05
Data Preparation, Transforms, Quality, and Management,s to concepts, value sets, and phenotype expressions. Data that meet the data design criteria are extracted into a data mart where the quality of the data can be assessed. Once data are of sufficient quality and meet expectations, ML features are developed for use in machine learning models.作者: Facet-Joints 時(shí)間: 2025-3-31 12:05 作者: infantile 時(shí)間: 2025-3-31 14:53
eCustomer Relationship Management,riving principles of the present volume. We outline the contents of the volume, both overall and chapter-by-chapter, noting the interconnections. We discuss the intended audience, and differences from other AI/ML books. We finally discuss format, style/tone, and state a few important caveats and disclosures.作者: 有角 時(shí)間: 2025-3-31 21:16 作者: 腐蝕 時(shí)間: 2025-3-31 22:16 作者: 正式通知 時(shí)間: 2025-4-1 01:58 作者: 頌揚(yáng)國(guó)家 時(shí)間: 2025-4-1 08:46 作者: fertilizer 時(shí)間: 2025-4-1 13:56
