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Titlebook: Data Science for Healthcare; Methodologies and Ap Sergio Consoli,Diego Reforgiato Recupero,Milan Pet Book 2019 Springer Nature Switzerland

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樓主: Neogamist
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發(fā)表于 2025-3-23 09:52:12 | 只看該作者
12#
發(fā)表于 2025-3-23 17:09:45 | 只看該作者
Using Process Analytics to Improve Healthcare Processesllected by modern process-aware (healthcare) information systems provide a wealth of data and can be used to analyze the adherence to these protocols. Process mining is a young research area combining data science (machine learning, data mining, etc.) and business process management. Its main contri
13#
發(fā)表于 2025-3-23 21:15:32 | 只看該作者
14#
發(fā)表于 2025-3-24 01:55:08 | 只看該作者
15#
發(fā)表于 2025-3-24 03:17:25 | 只看該作者
16#
發(fā)表于 2025-3-24 09:04:16 | 只看該作者
Candela González-Arias,Gertrudis Peread comparing in detail the performance of different classification techniques. We demonstrate the value of our approach on the concrete problem of building a classifier for predicting biochemical recurrence, indicating potential cancer relapse after prostate cancer treatment, from clinical patient data.
17#
發(fā)表于 2025-3-24 13:48:16 | 只看該作者
Contact Force Models for Granular Materialsdenosine (MTA) accumulation as a result of the loss of activity of the enzyme S-methyl-5.-thioadenosine phosphorylase (MTAP) is correctly predicted by the Nash equilibrium approach under tight regulation of adenine. Several examples are presented to elucidate the key ideas in modeling cancer metabolism using the Nash equilibrium approach.
18#
發(fā)表于 2025-3-24 18:04:16 | 只看該作者
Assistive Robots for the Elderly: Innovative Tools to Gather Health Relevant Datative of this chapter is to investigate how this can happen given the current state of the art in the field. We will focus our argument on how they can be used as valuable agents for the acquisition of novel data, relevant to the healthcare and well-being domain.
19#
發(fā)表于 2025-3-24 20:39:23 | 只看該作者
Visual Analytics for Classifier Construction and Evaluation for Medical Datad comparing in detail the performance of different classification techniques. We demonstrate the value of our approach on the concrete problem of building a classifier for predicting biochemical recurrence, indicating potential cancer relapse after prostate cancer treatment, from clinical patient data.
20#
發(fā)表于 2025-3-25 01:22:04 | 只看該作者
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