標(biāo)題: Titlebook: Artificial Intelligence in Medicine; 19th International C Allan Tucker,Pedro Henriques Abreu,David Ria?o Conference proceedings 2021 The Ed [打印本頁] 作者: Myelopathy 時(shí)間: 2025-3-21 17:58
書目名稱Artificial Intelligence in Medicine影響因子(影響力)
書目名稱Artificial Intelligence in Medicine影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence in Medicine網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence in Medicine網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence in Medicine被引頻次
書目名稱Artificial Intelligence in Medicine被引頻次學(xué)科排名
書目名稱Artificial Intelligence in Medicine年度引用
書目名稱Artificial Intelligence in Medicine年度引用學(xué)科排名
書目名稱Artificial Intelligence in Medicine讀者反饋
書目名稱Artificial Intelligence in Medicine讀者反饋學(xué)科排名
作者: 錯(cuò)誤 時(shí)間: 2025-3-21 22:56 作者: Irrigate 時(shí)間: 2025-3-22 03:06 作者: 首創(chuàng)精神 時(shí)間: 2025-3-22 08:14
Bin Shen,Xuemei Ding,Yanyan Wang,Shuyun Renon, a classifier based on a lexicon and a BERT-base neural network achieved a 0.838 F1-score, a score similar to the score achieved by the best classifier on this task during the #SMM4H’20 competition, but it processed the corpus 28 times faster - a positive result, since processing speed is still a作者: MAIZE 時(shí)間: 2025-3-22 09:01 作者: radiograph 時(shí)間: 2025-3-22 15:32 作者: 跳動 時(shí)間: 2025-3-22 17:26 作者: Junction 時(shí)間: 2025-3-22 23:23 作者: 蚊帳 時(shí)間: 2025-3-23 02:30 作者: Irrepressible 時(shí)間: 2025-3-23 07:55
A Topological Data Analysis Mapper of the Ovarian Folliculogenesis Based on MALDI Mass Spectrometry and the surrounding follicle cells..Here, we tested the use of advanced clustering and visual analytics approaches on MALDI-MSI data for the . identification of the protein signature of growing follicles, from the pre-antral T4 to the pre-ovulatory T8. Specifically, we first analyzed follicles MALDI作者: Ventilator 時(shí)間: 2025-3-23 12:39
Primary Care Datasets for Early Lung Cancer Detection: An AI Led Approacharch focuses initially on lung cancer but can be extended to other types of cancer. Additional challenges are present in this type of data due to the irregular and infrequent nature of doing pathology tests, which are also considered in designing the AI solution. Our findings demonstrate that hemato作者: ABHOR 時(shí)間: 2025-3-23 15:50
Addressing Extreme Imbalance for Detecting Medications Mentioned in Twitter User Timelineson, a classifier based on a lexicon and a BERT-base neural network achieved a 0.838 F1-score, a score similar to the score achieved by the best classifier on this task during the #SMM4H’20 competition, but it processed the corpus 28 times faster - a positive result, since processing speed is still a作者: 仇恨 時(shí)間: 2025-3-23 18:47 作者: thwart 時(shí)間: 2025-3-23 22:38 作者: Crepitus 時(shí)間: 2025-3-24 06:12 作者: Glossy 時(shí)間: 2025-3-24 09:12
Analysis of Health Screening Records Using Interpretations of Predictive Modelsd that the model makes good predictions using a number of attributes conventionally known to be related to diabetes, but also those not commonly used in the diagnosis of diabetes. A sensitivity analysis showed that the predictions’ changes were mostly consistent with our intuition on how daily behav作者: Pepsin 時(shí)間: 2025-3-24 11:42 作者: phlegm 時(shí)間: 2025-3-24 18:44
A Petri Dish for Histopathology Image Analysiss the properties of biopsy or resected specimens traditionally manually examined under a microscope by pathologists. However, challenges such as limited data, costly annotation, and processing high-resolution and variable-size images make it difficult to quickly iterate over model designs..Throughou作者: 半身雕像 時(shí)間: 2025-3-24 22:03
fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiserith any widely used imaging modality, there is a need to ensure the quality of the same, with missing values being highly frequent due to the presence of artifacts or sub-optimal imaging resolutions. Our work focus on missing values imputation on multivariate signal data. To do so, a new imputation 作者: Offbeat 時(shí)間: 2025-3-25 01:33
Bayesian Deep Active Learning for Medical Image Analysisequate performance. However, such labelled images are costly to acquire in time, labour, and human expertise. We propose a novel practical Bayesian Active Learning approach using Dropweights and overall bias-corrected uncertainty measure to suggest which unlabelled image to annotate. Experiments wer作者: floaters 時(shí)間: 2025-3-25 07:12 作者: resuscitation 時(shí)間: 2025-3-25 11:28 作者: 克制 時(shí)間: 2025-3-25 11:51 作者: Lipoma 時(shí)間: 2025-3-25 16:11
Catching Patient’s Attention at the Right Time to Help Them Undergo Behavioural Change: Stress Classalth behavioral change interventions and supporting patients compliance. Focusing on managing stress via deep breathing intervention, we hypothesise that the patients are more likely to perform suggested breathing exercises when they need calming down. To prompt them at the right time, we developed 作者: Libido 時(shí)間: 2025-3-25 20:39 作者: 山間窄路 時(shí)間: 2025-3-26 01:00 作者: ACE-inhibitor 時(shí)間: 2025-3-26 04:44
ICU Days-to-Discharge Analysis with Machine Learning Technologynts is essential to that management. Previous studies showed a low predictive capability of internists and ML-generated models. Therefore, more elaborated combinations of ML technologies are required. Here, we present four approaches to the analysis of the DTDs of ICU patients from different perspec作者: 聰明 時(shí)間: 2025-3-26 11:43 作者: 強(qiáng)有力 時(shí)間: 2025-3-26 12:50
Semantic Web Framework to Computerize Staged Reflex Testing Protocols to Mitigate Underutilization o) to render a timely diagnosis—often delaying appropriate treatment for years. In contemporary clinical laboratories, laboratory interventions can appropriately add-on extra tests to help confirm or rule out complex disorders. For these protocols to be clinically valid and economically efficient, th作者: jarring 時(shí)間: 2025-3-26 18:15 作者: SYN 時(shí)間: 2025-3-26 23:26 作者: 出汗 時(shí)間: 2025-3-27 01:48
Seasonality in Infection Predictions Using Interpretable Models for High Dimensional Imbalanced Dataeasonality in clinical prediction models, including a new proposal based on sliding windows. Class imbalance, high dimensionality and interpretable models are also considered since they are common traits of clinical datasets..We tested these approaches with four datasets: two created synthetically a作者: 疾馳 時(shí)間: 2025-3-27 09:19 作者: Aerate 時(shí)間: 2025-3-27 11:29 作者: output 時(shí)間: 2025-3-27 14:30
https://doi.org/10.1007/978-3-030-55218-3]. Finally, a recurrent layer is applied to tune the signal, such that it captures its true patterns. Both operations yield an improved robustness against state-of-the-art alternatives. The code is made available on ..作者: deficiency 時(shí)間: 2025-3-27 19:04 作者: nascent 時(shí)間: 2025-3-28 00:33
The Naked Truth of Antifashion Philosophyt at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.作者: GORGE 時(shí)間: 2025-3-28 04:19 作者: 牌帶來 時(shí)間: 2025-3-28 08:30 作者: 樹上結(jié)蜜糖 時(shí)間: 2025-3-28 11:32
fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser]. Finally, a recurrent layer is applied to tune the signal, such that it captures its true patterns. Both operations yield an improved robustness against state-of-the-art alternatives. The code is made available on ..作者: tinnitus 時(shí)間: 2025-3-28 15:45 作者: LATHE 時(shí)間: 2025-3-28 22:46 作者: buoyant 時(shí)間: 2025-3-28 22:54
Sum-Product Networks for Early Outbreak Detection of Emerging Diseasesversely to the conventional use of SPNs, we present a new approach to detect anomalies by evaluating .-values on the learned model. Our experiments on synthetic and real data with synthetic outbreaks show that SPNs are able to improve upon state-of-the-art techniques for detecting outbreaks of emerging diseases.作者: 束縛 時(shí)間: 2025-3-29 04:25 作者: ALLAY 時(shí)間: 2025-3-29 10:19 作者: 冷峻 時(shí)間: 2025-3-29 13:19 作者: TRAWL 時(shí)間: 2025-3-29 17:40 作者: iodides 時(shí)間: 2025-3-29 22:20 作者: ear-canal 時(shí)間: 2025-3-30 00:15
0302-9743 as a virtual event, in June 2021...The 28 full papers presented together with 30 short papers were selected from 138 submissions. The papers are grouped in topical sections on image analysis; predictive modelling; temporal data analysis; unsupervised learning; planning and decision support; deep le作者: 咽下 時(shí)間: 2025-3-30 04:36 作者: LAY 時(shí)間: 2025-3-30 10:58
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162492.jpg作者: 鞭子 時(shí)間: 2025-3-30 14:27 作者: 攀登 時(shí)間: 2025-3-30 18:59
