標題: Titlebook: Handbook of Artificial Intelligence in Healthcare; Vol. 1 - Advances an Chee-Peng Lim,Ashlesha Vaidya,Lakhmi C. Jain Book 2022 The Editor(s [打印本頁] 作者: Wilson 時間: 2025-3-21 16:56
書目名稱Handbook of Artificial Intelligence in Healthcare影響因子(影響力)
書目名稱Handbook of Artificial Intelligence in Healthcare影響因子(影響力)學(xué)科排名
書目名稱Handbook of Artificial Intelligence in Healthcare網(wǎng)絡(luò)公開度
書目名稱Handbook of Artificial Intelligence in Healthcare網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Handbook of Artificial Intelligence in Healthcare被引頻次
書目名稱Handbook of Artificial Intelligence in Healthcare被引頻次學(xué)科排名
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書目名稱Handbook of Artificial Intelligence in Healthcare年度引用學(xué)科排名
書目名稱Handbook of Artificial Intelligence in Healthcare讀者反饋
書目名稱Handbook of Artificial Intelligence in Healthcare讀者反饋學(xué)科排名
作者: 處理 時間: 2025-3-21 23:36 作者: 萬花筒 時間: 2025-3-22 04:07
https://doi.org/10.1007/978-981-13-1177-2rders that make it difficult the communication with other people. Their natural language presents different degrees of alteration, reaching in some cases the impossibility of speaking. This chapter presents an approach to model the patient’s behavior by processing recordings during the therapy. Vide作者: 過于光澤 時間: 2025-3-22 08:12
https://doi.org/10.1007/978-3-031-61191-9sis is as evident in medicine as it is elsewhere. Numerous artificial intelligence techniques been applied to different medical problems with the aim of automating time-consuming, and often subjective, manual tasks implemented by practitioners in diverse specialties. This chapter focuses on several 作者: Annotate 時間: 2025-3-22 09:29 作者: ADJ 時間: 2025-3-22 15:43
The Audience as Myth and Realitynical background and setting for the opportunistic development of cancer imaging biomarkers from such routine imaging. The chapter is aimed at the clinicians with a data science interest as well as data scientists with a clinical interest, and touches on computational approaches on radiological data作者: Gossamer 時間: 2025-3-22 17:09
https://doi.org/10.1057/9781137478818el of multiple input and multiple output structure to detect LST-type polyp with high accuracy, which is based on U-Net architecture for the segmentation. Not only the original endoscope image but also depth map is also used to the original CNN structure of 2 inputs and 4 outputs. Here, proposed met作者: Agility 時間: 2025-3-23 00:16 作者: 令人作嘔 時間: 2025-3-23 01:59
https://doi.org/10.1007/978-1-349-20540-0g used to detect the presence of subclinical atherosclerosis since it provides a measurement of the Intima Media Thickness (IMT) of the artery and can be used to identify the presence of atherosclerotic plaques. Moreover, it is well known that disruption of an atherosclerotic plaque plays a crucial 作者: Phenothiazines 時間: 2025-3-23 07:53
https://doi.org/10.1057/9780230276499 a major cause of early postoperative recurrence of HCC. Predicting MVI before surgery can help doctors develop treatment plans. However, the diagnosis of MVI depends on postoperative pathological verification, which is difficult to predict before surgery. In recent years, more researchers have used作者: 鼓掌 時間: 2025-3-23 11:48 作者: itinerary 時間: 2025-3-23 15:12
Conclusion: Real Change in the Real Worldent types of data, which reside in complex information networks. Researchers focus on producing usable knowledge by taking advantage of opportunities in various domains (e.g., healthcare, social media, energy etc.). Epidemics and disease outbreaks raised concerns about effective infectious disease m作者: 直覺好 時間: 2025-3-23 19:27
Unattractive Women: Cross-Casting in Comedy,n the field of medical diagnosis. The reason for this is the usual method of how a diagnosis is achieved. Basically, the process of medical diagnosis consists of two important steps. As a first step, a doctor always tries to gather as much relevant information and data as possible about the patient 作者: scotoma 時間: 2025-3-24 00:53
,Taking the Mic: Hip Hop’s Call for Change,le starts from basic methods on experimental design and data acquisition systems for computer-aided depressive severity diagnosis. Next, typical baseline behavioral features such as facial expressions and speech prosody will be introduced. From the experimental results of the baseline systems introd作者: AVOW 時間: 2025-3-24 04:07 作者: 有限 時間: 2025-3-24 08:35
Theatre, Social Media, and Meaning Makingdiseases remain those in which a correct and early diagnosis can not only significantly improve the patient’s quality of life but also impact the effectiveness of the therapy itself. Artificial Intelligence can provide valuable aid to this need through the development of predictive models to support作者: 投射 時間: 2025-3-24 13:35 作者: uveitis 時間: 2025-3-24 15:10
Advances in Artificial Intelligence for the Identification of Epileptiform Discharges dynamics to be initiated in various regions of the brain. In order, to define the different ictal states and thus to evaluate the overall course of the patient, expert clinicians rely on Electroencephalography (EEG) denoting the differentiated events based on their experience and perception. As suc作者: 放縱 時間: 2025-3-24 20:34 作者: endarterectomy 時間: 2025-3-25 02:55
Autistic Verbal Behavior Language Parameterizationrders that make it difficult the communication with other people. Their natural language presents different degrees of alteration, reaching in some cases the impossibility of speaking. This chapter presents an approach to model the patient’s behavior by processing recordings during the therapy. Vide作者: voluble 時間: 2025-3-25 07:03
Case Studies to Demonstrate Real-World Applications in Ophthalmic Image Analysissis is as evident in medicine as it is elsewhere. Numerous artificial intelligence techniques been applied to different medical problems with the aim of automating time-consuming, and often subjective, manual tasks implemented by practitioners in diverse specialties. This chapter focuses on several 作者: BRAND 時間: 2025-3-25 11:32
Segmentation of Petri Plate Images for Automatic Reporting of Urine Culture Testson of advanced image processing techniques, artificial intelligence tools, fuzzy logic, genetic algorithms, and Bayesian modeling. In particular, the development of intelligent tools for the automatic reporting of medical analyses (screening systems) has attracted increasing research interest, due t作者: 郊外 時間: 2025-3-25 12:50
Repurposing Routine Imaging for Cancer Biomarker Discovery Using Machine Learningnical background and setting for the opportunistic development of cancer imaging biomarkers from such routine imaging. The chapter is aimed at the clinicians with a data science interest as well as data scientists with a clinical interest, and touches on computational approaches on radiological data作者: exostosis 時間: 2025-3-25 18:58 作者: Pulmonary-Veins 時間: 2025-3-25 20:17
Artificial Intelligence and Deep Learning, Important Tools in Assisting Gastroenterologiststhis problem. Early detection of pre-cancerous signs considerably increases the survival rate. Inter and intra-observer variability might be stated in lesions’ diagnosis due to the patient’s personal particularities, depending on colon cleansing degree or influenced by the expert’s training, state o作者: 鎮(zhèn)痛劑 時間: 2025-3-26 01:39
Last Advances on Automatic Carotid Artery Analysis in Ultrasound Images: Towards Deep Learningg used to detect the presence of subclinical atherosclerosis since it provides a measurement of the Intima Media Thickness (IMT) of the artery and can be used to identify the presence of atherosclerotic plaques. Moreover, it is well known that disruption of an atherosclerotic plaque plays a crucial 作者: reptile 時間: 2025-3-26 08:15 作者: infringe 時間: 2025-3-26 08:35 作者: considerable 時間: 2025-3-26 13:06
Mining Data to Deal with Epidemics: Case Studies to Demonstrate Real World AI Applicationsent types of data, which reside in complex information networks. Researchers focus on producing usable knowledge by taking advantage of opportunities in various domains (e.g., healthcare, social media, energy etc.). Epidemics and disease outbreaks raised concerns about effective infectious disease m作者: Deadpan 時間: 2025-3-26 17:48
A Powerful Holonic and Multi-Agent-Based Front-End for Medical Diagnostics Systemsn the field of medical diagnosis. The reason for this is the usual method of how a diagnosis is achieved. Basically, the process of medical diagnosis consists of two important steps. As a first step, a doctor always tries to gather as much relevant information and data as possible about the patient 作者: 昏暗 時間: 2025-3-26 21:21 作者: 北極熊 時間: 2025-3-27 01:11 作者: Hyperlipidemia 時間: 2025-3-27 08:49 作者: 樣式 時間: 2025-3-27 11:56
New Insights on Implementing and Evaluating Artificial Intelligence in Cardiovascular Careve to other industries, the adoption of artificial intelligence in healthcare has progressed slowly. This chapter is focused on describing the unique challenges faced by personalized care delivery using multi-domain data patient health information. It discusses validated solutions for data managemen作者: 網(wǎng)絡(luò)添麻煩 時間: 2025-3-27 15:54 作者: exclamation 時間: 2025-3-27 19:39 作者: entice 時間: 2025-3-27 23:10 作者: fender 時間: 2025-3-28 02:34 作者: MITE 時間: 2025-3-28 07:29 作者: recession 時間: 2025-3-28 12:36 作者: JADED 時間: 2025-3-28 14:52
The Audience as Myth and Realitynicians with a data science interest as well as data scientists with a clinical interest, and touches on computational approaches on radiological data to solve clinical problems. The chapter outlines the technical considerations of imaging, where it occurs in the cancer pathway, and challenges to overcome in order to develop new radiomic features.作者: A精確的 時間: 2025-3-28 20:51
,Taking the Mic: Hip Hop’s Call for Change,ine behavioral features such as facial expressions and speech prosody will be introduced. From the experimental results of the baseline systems introduced in this chapter, readers can not only compare between the performance of different baseline features but also have a general understanding of computer-aided depressive severity diagnosis.作者: 屈尊 時間: 2025-3-29 01:11
https://doi.org/10.1057/9781137367884challenges faced by personalized care delivery using multi-domain data patient health information. It discusses validated solutions for data management and Machine Learning approaches for combining the value of these complementary yet disparate data resources for patient-specific risk prediction modelling.作者: flammable 時間: 2025-3-29 04:04 作者: obnoxious 時間: 2025-3-29 08:53 作者: 卵石 時間: 2025-3-29 11:35
https://doi.org/10.1057/9780230276499 magnetic resonance imaging data to predict MVI of HCC. At present, our fusion prediction model achieves 72.60% accuracy and 0.7607 area under the curve (AUC). In this chapter, we first introduce fundamentals of radiomics and then we present our MVI prediction method using radiomics.作者: gonioscopy 時間: 2025-3-29 17:31
Automatic Detection of LST-Type Polyp by CNN Using Depth Mape projection. Higher accuracy of 85% was obtained for the detection of LST-type polyp by the proposed method. It is shown that the multiple input-output structure of U-Net model gives the higher performance of segmentation problem using both of original endoscope image and depth map.作者: commonsense 時間: 2025-3-29 20:57 作者: antiandrogen 時間: 2025-3-30 03:13
Radiomics and Its Application in Predicting Microvascular Invasion of Hepatocellular Carcinoma magnetic resonance imaging data to predict MVI of HCC. At present, our fusion prediction model achieves 72.60% accuracy and 0.7607 area under the curve (AUC). In this chapter, we first introduce fundamentals of radiomics and then we present our MVI prediction method using radiomics.作者: LATHE 時間: 2025-3-30 07:46 作者: ALT 時間: 2025-3-30 08:12
https://doi.org/10.1057/9780230114029ore complex ones, which combine recurrent networks with convolutional models. Experimental results have shown the feasibility of the approach, and the superiority of composite architectures, which have led to higher accuracy values.作者: Collar 時間: 2025-3-30 15:19
Artificial Intelligence in Remote Photoplethysmography: Remote Heart Rate Estimation from Video Imagn these methods are discussed for solving movement artifacts and illumination changes. Deep learning methods are then reviewed, and a general overview of the datasets available for remote photoplethysmography learning is furnished.作者: 玩忽職守 時間: 2025-3-30 19:27 作者: APO 時間: 2025-3-31 00:17
https://doi.org/10.1007/978-981-13-1177-2of stereotyped responses collected in a systematic way, labeled as patterns. Those movements and sounds, represents how patterns in audio and video relate to stimuli from the environment. Findings allow to discriminate when and how there is a reaction, an autistic verbal behavior.作者: UNT 時間: 2025-3-31 02:02
Autistic Verbal Behavior Language Parameterizationof stereotyped responses collected in a systematic way, labeled as patterns. Those movements and sounds, represents how patterns in audio and video relate to stimuli from the environment. Findings allow to discriminate when and how there is a reaction, an autistic verbal behavior.作者: 草本植物 時間: 2025-3-31 06:57
Book 2022 methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context.?.The advent of digital and computing technologies has created a surge in the development of AI methodologies and their 作者: 慢跑 時間: 2025-3-31 11:00 作者: 大包裹 時間: 2025-3-31 16:44
Computer-Aided Detection of Depressive Severity Using Multimodal Behavioral Dataine behavioral features such as facial expressions and speech prosody will be introduced. From the experimental results of the baseline systems introduced in this chapter, readers can not only compare between the performance of different baseline features but also have a general understanding of computer-aided depressive severity diagnosis.作者: ear-canal 時間: 2025-3-31 18:51
New Insights on Implementing and Evaluating Artificial Intelligence in Cardiovascular Carechallenges faced by personalized care delivery using multi-domain data patient health information. It discusses validated solutions for data management and Machine Learning approaches for combining the value of these complementary yet disparate data resources for patient-specific risk prediction modelling.
