找回密碼
 To register

QQ登錄

只需一步，快速開(kāi)始

掃一掃，訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Explainable AI in Healthcare and Medicine; Building a Culture o Arash Shaban-Nejad,Martin Michalowski,David L. Buc Book 2021 The Editor(s)

[復(fù)制鏈接]
查看: 48931|回復(fù): 69
樓主
發(fā)表于 2025-3-21 16:16:26 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine
副標(biāo)題Building a Culture o
編輯Arash Shaban-Nejad,Martin Michalowski,David L. Buc
視頻videohttp://file.papertrans.cn/320/319282/319282.mp4
概述Highlights the latest advances in explainable AI in health care and medicine by presenting significant findings on theory, methods, systems, and applications.Includes revised versions of selected pape
叢書(shū)名稱(chēng)Studies in Computational Intelligence
圖書(shū)封面Titlebook: Explainable AI in Healthcare and Medicine; Building a Culture o Arash Shaban-Nejad,Martin Michalowski,David L. Buc Book 2021 The Editor(s)
描述This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence.
出版日期Book 2021
關(guān)鍵詞Health Intelligence; Precession Medicine; Precession Health; Digital Medicine; Big Data; Predictive Analy
版次1
doihttps://doi.org/10.1007/978-3-030-53352-6
isbn_softcover978-3-030-53354-0
isbn_ebook978-3-030-53352-6Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine影響因子(影響力)




書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine被引頻次




書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine被引頻次學(xué)科排名




書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine年度引用




書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine年度引用學(xué)科排名




書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine讀者反饋




書(shū)目名稱(chēng)Explainable AI in Healthcare and Medicine讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics
 

0票 0%

Better Implies Difficulty
 

0票 0%

Good and Satisfactory
 

0票 0%

Adverse Performance
 

0票 0%

Disdainful Garbage
您所在的用戶(hù)組沒(méi)有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 22:02:15 | 只看該作者
板凳
發(fā)表于 2025-3-22 02:57:29 | 只看該作者
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs,ch provide important information about a patient’s health status. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables and large amounts of missing data, which complicate the analysis. In this work, we propose a novel kernel which is
地板
發(fā)表于 2025-3-22 05:16:50 | 只看該作者
5#
發(fā)表于 2025-3-22 12:31:39 | 只看該作者
6#
發(fā)表于 2025-3-22 14:23:31 | 只看該作者
7#
發(fā)表于 2025-3-22 17:55:51 | 只看該作者
8#
發(fā)表于 2025-3-22 23:41:32 | 只看該作者
A Dynamic Deep Neural Network for Multimodal Clinical Data Analysis,o foster precision medicine, e.g. by finding new disease phenotypes or performing individual disease prediction. However, to take full advantage of deep learning methods on clinical data, architectures are necessary that (1) are robust with respect to missing and wrong values, and (2) can deal with
9#
發(fā)表于 2025-3-23 02:44:30 | 只看該作者
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heartility data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to perform unsupervised stress detection: (1) traditional K-Means clustering with engineered time and frequency domain features (2) con
10#
發(fā)表于 2025-3-23 08:21:11 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-19 19:35
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
肃宁县| 康乐县| 遂川县| 大田县| 塔河县| 手游| 松潘县| 五河县| 垫江县| 吴旗县| 临潭县| 宜宾县| 廉江市| 三台县| 蕲春县| 潍坊市| 社会| 乌鲁木齐市| 商南县| 德阳市| 建平县| 深水埗区| 西乌珠穆沁旗| 玛纳斯县| 池州市| 吉木乃县| 谢通门县| 阿拉善盟| 屯留县| 资中县| 依安县| 江陵县| 哈巴河县| 德化县| 合川市| 革吉县| 科技| 和平区| 图木舒克市| 建始县| 安宁市|