標(biāo)題: Titlebook: Artificial Intelligence in Medicine; 21st International C Jose M. Juarez,Mar Marcos,Allan Tucker Conference proceedings 2023 The Editor(s) [打印本頁] 作者: ergonomics 時間: 2025-3-21 19:04
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: 粗糙濫制 時間: 2025-3-21 23:46 作者: 建筑師 時間: 2025-3-22 02:07
Gary R. Hudes MD,Jessie Schol RNsicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.作者: orient 時間: 2025-3-22 06:33
Geistesgeschichtliche Faschismusdiagnosen,nd new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.作者: Ferritin 時間: 2025-3-22 12:24 作者: Bombast 時間: 2025-3-22 13:13 作者: deficiency 時間: 2025-3-22 17:54
Causal Discovery with?Missing Data in?a?Multicentric Clinical Studysicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.作者: MAUVE 時間: 2025-3-22 21:38
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detectionnd new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.作者: indigenous 時間: 2025-3-23 03:49
Computational Evaluation of?Model-Agnostic Explainable AI Using Local Feature Importance in?Healthcaocal feature importances) as features and the output of the prediction problem (labels) again as labels. We evaluate the method based a real-world tabular electronic health records dataset. At the end, we answer the research question: “How can we computationally evaluate XAI Models for a specific prediction model and dataset?”.作者: Focus-Words 時間: 2025-3-23 07:20
Batch Integrated Gradients: Explanations for?Temporal Electronic Health RecordsRecords (EHRs), we see patient records can be stored in temporal sequences. Thus, we demonstrate Batch-Integrated Gradients in producing explanations over a temporal sequence that satisfy proposed properties corresponding to XAI for EHR data.作者: 使絕緣 時間: 2025-3-23 10:21
0302-9743 in Portoroz, Slovenia, in June12–15, 2023..The 23 full papers and 21 short papers presented together with 3?demonstration papers?were selected from 108 submissions.?The papers are grouped in topical sections on: machine learning and deep learning; explainability and transfer learning; natural langu作者: constellation 時間: 2025-3-23 17:27
Proshanto K. Mukherjee,Mark Brownrigges of events. We performed empirical experiments on a cohort of 48. emergency care patients from a large Danish hospital. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional non-sequence-based machine learning approaches.作者: 極大的痛苦 時間: 2025-3-23 21:32
Patient Event Sequences for?Predicting Hospitalization Length of?Stayes of events. We performed empirical experiments on a cohort of 48. emergency care patients from a large Danish hospital. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional non-sequence-based machine learning approaches.作者: 才能 時間: 2025-3-24 00:59
Hospital Length of?Stay Prediction Based on?Multi-modal Data Towards Trustworthy Human-AI Collaboratmaking process. Explaining models built on both: human-annotated and algorithm-extracted radiomics features provides valuable insights for physicians working in a hospital. We believe the presented approach to be general and widely applicable to other time-to-event medical use cases. For reproducibility, we open-source code and the . dataset at ..作者: 繁榮地區(qū) 時間: 2025-3-24 05:04 作者: Injunction 時間: 2025-3-24 09:53
Conference proceedings 2023ions.?The papers are grouped in topical sections on: machine learning and deep learning; explainability and transfer learning; natural language processing; image analysis and signal analysis; data analysis and statistical models; knowledge representation and decision support..作者: overreach 時間: 2025-3-24 13:05
The FasL-Fas System in Disease and Therapy,making process. Explaining models built on both: human-annotated and algorithm-extracted radiomics features provides valuable insights for physicians working in a hospital. We believe the presented approach to be general and widely applicable to other time-to-event medical use cases. For reproducibility, we open-source code and the . dataset at ..作者: 大門在匯總 時間: 2025-3-24 18:54 作者: 果仁 時間: 2025-3-24 22:48
Survival Hierarchical Agglomerative Clustering: A Semi-Supervised Clustering Method Incorporating Sulized therapeutic approaches. To address this issue, clustering algorithms are often employed that identify patient groups with homogeneous characteristics. Clustering algorithms are mainly unsupervised, resulting in clusters that are biologically meaningful, but not necessarily correlated with a cl作者: 防銹 時間: 2025-3-25 00:03 作者: ITCH 時間: 2025-3-25 06:33 作者: Overthrow 時間: 2025-3-25 08:41
Decision Tree Approaches to?Select High Risk Patients for?Lung Cancer Screening Based on?the?UK Primsuch as cough, pain, dyspnoea and anorexia are also present in other diseases. This partly attributes towards the low survival rate. Therefore, it is crucial to screen high risk patients for lung cancer at an early stage through computed tomography (CT) scans. As shown in a previous study, for patie作者: lanugo 時間: 2025-3-25 13:55 作者: 慢跑鞋 時間: 2025-3-25 16:02
Novel Approach for?Phenotyping Based on?Diverse Top-K Subgroup Lists clinicians to understand it. In this paper, we approach this problem by defining the technical task of mining diverse top-k phenotypes and proposing an algorithm called DSLM to solve it. The phenotypes obtained are evaluated according to their quality and predictive capacity in a bacterial infectio作者: 自戀 時間: 2025-3-25 22:34
Patient Event Sequences for?Predicting Hospitalization Length of?Stayations. This paper proposes a novel transformer-based model, termed Medic-BERT (M-BERT), for predicting LOS by modeling patient information as sequences of events. We performed empirical experiments on a cohort of 48. emergency care patients from a large Danish hospital. Experimental results show th作者: gerrymander 時間: 2025-3-26 02:02
Autoencoder-Based Prediction of ICU Clinical Codesthem in the EHR is tedious, and some clinical codes may be overlooked. Given an incomplete list of clinical codes, we investigate the performance of ML methods on predicting the complete ones, and assess the added predictive value of including other clinical patient data in this task. We used the MI作者: 礦石 時間: 2025-3-26 05:20
Hospital Length of?Stay Prediction Based on?Multi-modal Data Towards Trustworthy Human-AI Collaborat textual radiology reports annotated by humans. Although black-box models predict better on average than interpretable ones, like Cox proportional hazards, they are not inherently understandable. To overcome this trust issue, we introduce time-dependent model explanations into the human-AI decision 作者: Observe 時間: 2025-3-26 12:30 作者: Debate 時間: 2025-3-26 15:42
Federated Learning to?Improve Counterfactual Explanations for?Sepsis Treatment Predictionble artificial intelligence (XAI) improving the interpretability of these models. In turn, this fosters the adoption by medical personnel and improves trustworthiness of CDSSs. Among others, counterfactual explanations prove to be one such XAI technique particularly suitable for the healthcare domai作者: 蠟燭 時間: 2025-3-26 18:33
Explainable AI for?Medical Event Prediction for?Heart Failure Patientsedictions neither understandable nor interpretable by humans. This limitation is especially significant when they contradict clinicians’ expectations based on medical knowledge. This can lead to a lack of trust in the model. In this work, we propose a pipeline to explain AI models. We used a previou作者: hermetic 時間: 2025-3-27 00:03
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detectionrowsiness before any impairment occurs, a promising strategy is using Machine Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work presents multiple experiments with different HRV time windows and ML models, a feature impact analysis using Shapley Additive Explanations (SHAP), an作者: Champion 時間: 2025-3-27 04:13
Computational Evaluation of?Model-Agnostic Explainable AI Using Local Feature Importance in?Healthcaased medical practice. In the XAI field, effective evaluation methods are still being developed. The straightforward way is to evaluate via user feedback. However, this needs big efforts (applying on high number of users and test cases) and can still include various biases inside. A computational ev作者: OPINE 時間: 2025-3-27 05:41
Batch Integrated Gradients: Explanations for?Temporal Electronic Health Recordscerned with analysing temporal data. Namely, we must consider a sequence of instances that occur in time, and explain why the prediction transitions from one time point to the next. Currently, XAI techniques do not leverage the temporal nature of data and instead treat each instance independently. T作者: fatty-acids 時間: 2025-3-27 13:22
Improving Stroke Trace Classification Explainability Through Counterexamples are typically not explainable, an issue which is particularly relevant in medicine..In our recent work we tackled this problem, by proposing ., a novel tool able to highlight what trace activities are particularly significant for the classification task. A trace saliency map is built by generating 作者: Pedagogy 時間: 2025-3-27 16:29 作者: Ebct207 時間: 2025-3-27 19:22
Artificial Intelligence in Medicine978-3-031-34344-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 協(xié)奏曲 時間: 2025-3-27 22:31
Amita Patnaik MD,Eric K. Rowinsky MD clinicians to understand it. In this paper, we approach this problem by defining the technical task of mining diverse top-k phenotypes and proposing an algorithm called DSLM to solve it. The phenotypes obtained are evaluated according to their quality and predictive capacity in a bacterial infection problem.作者: acheon 時間: 2025-3-28 04:38
A Return to Halves in the Twentieth Century,lized therapeutic approaches. To address this issue, clustering algorithms are often employed that identify patient groups with homogeneous characteristics. Clustering algorithms are mainly unsupervised, resulting in clusters that are biologically meaningful, but not necessarily correlated with a cl作者: 炸壞 時間: 2025-3-28 06:58 作者: –FER 時間: 2025-3-28 14:07
Farming in Norfolk Around 1800, prevent disease worsening, but selecting the right treatment is difficult due to the heterogeneity. To alleviate this decision-making process, predictions of the long-term prognosis of the individual patient are of interest (especially at diagnosis, when not much is known yet). However, most progno作者: 船員 時間: 2025-3-28 17:52
,Climate and the?Grain Price, 1264–1431,such as cough, pain, dyspnoea and anorexia are also present in other diseases. This partly attributes towards the low survival rate. Therefore, it is crucial to screen high risk patients for lung cancer at an early stage through computed tomography (CT) scans. As shown in a previous study, for patie作者: 模仿 時間: 2025-3-28 22:43
Gary R. Hudes MD,Jessie Schol RNhe associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on e作者: Anhydrous 時間: 2025-3-28 23:20
Amita Patnaik MD,Eric K. Rowinsky MD clinicians to understand it. In this paper, we approach this problem by defining the technical task of mining diverse top-k phenotypes and proposing an algorithm called DSLM to solve it. The phenotypes obtained are evaluated according to their quality and predictive capacity in a bacterial infectio作者: FLAIL 時間: 2025-3-29 03:27 作者: CESS 時間: 2025-3-29 10:58 作者: eczema 時間: 2025-3-29 13:11
The FasL-Fas System in Disease and Therapy, textual radiology reports annotated by humans. Although black-box models predict better on average than interpretable ones, like Cox proportional hazards, they are not inherently understandable. To overcome this trust issue, we introduce time-dependent model explanations into the human-AI decision 作者: 牽連 時間: 2025-3-29 16:06
https://doi.org/10.1007/978-3-663-00140-9e associated Explainable Artificial Intelligence (XAI) approaches, which should help to provide insight into the often opaque methods and thus gain trust of users and patients as well as facilitate interdisciplinary work. Using the differentiation of white blood cells with the aid of a high throughp作者: albuminuria 時間: 2025-3-29 23:42 作者: 強制令 時間: 2025-3-30 00:32 作者: 用肘 時間: 2025-3-30 08:05 作者: magenta 時間: 2025-3-30 10:30
Sozialpsychologische Faschismuskonzeptionen,ased medical practice. In the XAI field, effective evaluation methods are still being developed. The straightforward way is to evaluate via user feedback. However, this needs big efforts (applying on high number of users and test cases) and can still include various biases inside. A computational ev作者: 搜尋 時間: 2025-3-30 16:26
Geistesgeschichtliche Faschismusdiagnosen,cerned with analysing temporal data. Namely, we must consider a sequence of instances that occur in time, and explain why the prediction transitions from one time point to the next. Currently, XAI techniques do not leverage the temporal nature of data and instead treat each instance independently. T作者: indigenous 時間: 2025-3-30 17:15 作者: Glucose 時間: 2025-3-30 21:42
Sozialpsychologische Faschismuskonzeptionen,in need of additional support. Performant readmission models based on deep learning approaches require large, high-quality training datasets to perform optimally. Utilizing EHR data from a source hospital system to enhance prediction on a target hospital using traditional approaches might bias the d作者: molest 時間: 2025-3-31 01:22 作者: 愛得痛了 時間: 2025-3-31 08:55
https://doi.org/10.1007/978-3-031-34344-5artificial intelligence; bioinformatics; computer networks; computer systems; computer vision; data minin作者: Efflorescent 時間: 2025-3-31 10:38
978-3-031-34343-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 在駕駛 時間: 2025-3-31 14:46 作者: 休息 時間: 2025-3-31 20:46
A Return to Halves in the Twentieth Century,HAC outperformed several existing semi-supervised clustering algorithms. The algorithm was also evaluated on a critical care database of atrial fibrillation management, where it identified clusters that could readily be mapped to existing knowledge of treatment effects such as contraindications and 作者: 領(lǐng)帶 時間: 2025-3-31 21:49
Elizabeth Griffiths,Mark Overtonat are available in our oncology EMR. After following a novel three axes - performance, complexity, and explainability - design exploration framework, boosted random forests are selected because they provide a baseline accuracy of 80% and an F1 score of 75%, with reduced model complexity, thus makin作者: Ingrained 時間: 2025-4-1 05:33
Farming in Norfolk Around 1800,ral issues in observational datasets, such as sporadic measurements at irregular time-intervals, widely varying lengths of follow-up, and unequal number of measurements even for the same follow-up. We evaluated our approach on real-world clinical data from an observational single-center cohort of mu作者: SLING 時間: 2025-4-1 06:28
,Climate and the?Grain Price, 1264–1431,945 general practices across the UK. Two tree-based models (decision trees and random forest) are developed and implemented. The performance of the two models is compared with a logistic regression model in terms of accuracy, Area Under the Receiver Operating Characteristic curve (AUROC), sensitivit
