標(biāo)題: Titlebook: Explainable Artificial Intelligence and Process Mining Applications for Healthcare; Third International Jose M. Juarez,Carlos Fernandez-Ll [打印本頁(yè)] 作者: GURU 時(shí)間: 2025-3-21 19:31
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書(shū)目名稱Explainable Artificial Intelligence and Process Mining Applications for Healthcare讀者反饋學(xué)科排名
作者: theta-waves 時(shí)間: 2025-3-21 21:40 作者: 收到 時(shí)間: 2025-3-22 03:01
Cultural Humility, a Path to Equitye rankings can be challenging for users. In this paper, we present a visual analytic tool that combines XAI methods and interactive visualizations to explain ranking systems. Our tool provides users with a better understanding of how these systems work by using customized counterfactual explanations作者: 品牌 時(shí)間: 2025-3-22 06:20
Janna de Gouveia,Liesel Ebers?hnactitioners do not have enough data describing the impact of treatments on the evolution of pathologies, which is necessary to develop a program that can dynamically adapt to changing patient conditions. We have therefore designed an application based on a series of medical consensus rules capable o作者: 圓桶 時(shí)間: 2025-3-22 09:38
Deborah Fitzsimmons,Sally Wheelwrightlevance and usefulness in the field of medicine where human lives are at risk. AI in medicine has the ability to derive meaningful inferences from real world data – an emerging school of thought namely Real World Evidence (RWE) studies – that can assist medical practitioners to improve evidence base作者: CHARM 時(shí)間: 2025-3-22 13:40
Handbook of Quantifiers in Natural Languageents’ behavior. The features in this task are associated with costs. Besides basic (low-cost) information about patients’ phone calls and text messages, we are studying the impact of acoustic features (high-cost) on classifying patients’ states. Unlike in previous papers, now we take the costs into 作者: CHARM 時(shí)間: 2025-3-22 19:23 作者: 多節(jié) 時(shí)間: 2025-3-23 00:37
Cheng-Few Lee,Alice C. Lee,John Leeing the SEER database to model cutaneous malignant melanoma. Additionally, we employ SurvLIMEpy library, a . package designed to provide explainability for survival machine learning models, to analyse feature importance. The results demonstrate that machine learning algorithms outperform the Cox Pro作者: Rebate 時(shí)間: 2025-3-23 03:46
Fundamental Genetic Principles,ion regimens, dietary habits, physical activity, and avoiding flare-ups. Instead of merely positing an “edict,” the AI model can also explain . the recommendation was issued: why one should stay indoors (e.g., increased risk of flare-ups), why further calorie intake should be avoided (e.g., met the 作者: PAC 時(shí)間: 2025-3-23 06:53 作者: Yag-Capsulotomy 時(shí)間: 2025-3-23 12:52
Sergio Chayet,Wallace J. Hopp,Xiaowei Xunical pathways, identifying risk factors on high-frequency pathways may facilitate the efficient optimization of clinical processes. This paper illustrated a data-driven framework that combines local process optimization and conceptual model validation. Frequent clinic pathways and contributing fact作者: septicemia 時(shí)間: 2025-3-23 16:06
Steven R. Applewhite,John M. Gonzalesht glycemic control, both in terms of glycated hemoglobin and the time in range, with different metabolic pathways that contribute to nervous, micro, and macro-vascular complications. As a matter of fact, a few works attempted to obtain phenotyping to describe the evolution of type 2 diabetes patien作者: ornithology 時(shí)間: 2025-3-23 21:35 作者: Commodious 時(shí)間: 2025-3-23 23:12 作者: 高射炮 時(shí)間: 2025-3-24 04:42
978-3-031-54302-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: CIS 時(shí)間: 2025-3-24 07:41 作者: 羞辱 時(shí)間: 2025-3-24 12:52
Conference proceedings 2024 all aspects of eXplainable Artificial Intelligence (XAI) in the medical and healthcare field. For PM4H 5 papers have been accepted from 17 submissions. They deal with data-driven process analysis techniques in healthcare.?.作者: Mindfulness 時(shí)間: 2025-3-24 18:41 作者: Individual 時(shí)間: 2025-3-24 22:33 作者: Limpid 時(shí)間: 2025-3-25 00:37
Sergio Chayet,Wallace J. Hopp,Xiaowei Xuonent analysis (PCA) was used to identify latent factors. LPM was used to identify structural relationships in the high-frequent process pathways. PLS-SEM was adopted to evaluate the magnitude of relations. Through this framework, the study identified one frequent clinic pathway and six contributing factors for severe PI patients.作者: Invigorate 時(shí)間: 2025-3-25 05:25
A Data-Driven Framework for Improving Clinical Managements of Severe Paralytic Ileus in ICU: From Paonent analysis (PCA) was used to identify latent factors. LPM was used to identify structural relationships in the high-frequent process pathways. PLS-SEM was adopted to evaluate the magnitude of relations. Through this framework, the study identified one frequent clinic pathway and six contributing factors for severe PI patients.作者: MAG 時(shí)間: 2025-3-25 07:40
Interpreting Machine Learning Models for?Survival Analysis: A Study of?Cutaneous Melanoma Using the?y for survival machine learning models, to analyse feature importance. The results demonstrate that machine learning algorithms outperform the Cox Proportional Hazards Model. Our work underscores the importance of explainability methods for interpreting black-box models and provides insights into important features related to melanoma prognosis.作者: 哀求 時(shí)間: 2025-3-25 15:16
Janna de Gouveia,Liesel Ebers?hnr time, allows us to better understand how exercises are generated and then to analyze the pathways in order to monitor their effectiveness..The application, resulting from this work, is available as a WebApp.作者: Osteons 時(shí)間: 2025-3-25 19:29
Quantifiers in Kenyah Uma Baha,different expert models agreed that bigger subsets of unobserved features tend to be more relevant, the expert models are divided by whether the columnarity of an interneuron is irrelevant and in general the probability of a new observation changing the classification of its scenario is relatively low.作者: 取消 時(shí)間: 2025-3-25 21:09 作者: GRATE 時(shí)間: 2025-3-26 00:26
Explainable Artificial Intelligence in Response to the Failures of Musculoskeletal Disorder Rehabilir time, allows us to better understand how exercises are generated and then to analyze the pathways in order to monitor their effectiveness..The application, resulting from this work, is available as a WebApp.作者: 放牧 時(shí)間: 2025-3-26 05:34 作者: AWRY 時(shí)間: 2025-3-26 08:35
PMApp: An Interactive Process Mining Toolkit for?Building Healthcare Dashboardsrs. PMApp’s innovative approach enhances information comprehension at different levels, making it user-friendly for healthcare professionals. The toolkit has been successfully tested with over one million patients across more than 10 European hospitals, addressing diverse healthcare scenarios in Portugal, Spain, Sweden, and The Netherlands.作者: ABOUT 時(shí)間: 2025-3-26 15:22
1865-0929 re 2023, and the First International Workshop on Process Mining Applications for Healthcare, PM4H 2023, which took place in conjunction with AIME 2023 in?Portoroz, Slovenia, on June 15, 2023..The 7 full papers included from XAI-Healthcare were carefully reviewed and selected from 11 submissions. The作者: HUSH 時(shí)間: 2025-3-26 17:31 作者: BOLT 時(shí)間: 2025-3-26 22:05 作者: Adherent 時(shí)間: 2025-3-27 04:46 作者: 仲裁者 時(shí)間: 2025-3-27 07:03 作者: 公理 時(shí)間: 2025-3-27 10:29
Feature Selection in?Bipolar Disorder Episode Classification Using Cost-Constrained Methodsents’ behavior. The features in this task are associated with costs. Besides basic (low-cost) information about patients’ phone calls and text messages, we are studying the impact of acoustic features (high-cost) on classifying patients’ states. Unlike in previous papers, now we take the costs into 作者: 調(diào)情 時(shí)間: 2025-3-27 14:56 作者: 災(zāi)禍 時(shí)間: 2025-3-27 18:59
Interpreting Machine Learning Models for?Survival Analysis: A Study of?Cutaneous Melanoma Using the?ing the SEER database to model cutaneous malignant melanoma. Additionally, we employ SurvLIMEpy library, a . package designed to provide explainability for survival machine learning models, to analyse feature importance. The results demonstrate that machine learning algorithms outperform the Cox Pro作者: Tidious 時(shí)間: 2025-3-27 23:08
Explanations of?Symbolic Reasoning to?Effect Patient Persuasion and?Educationion regimens, dietary habits, physical activity, and avoiding flare-ups. Instead of merely positing an “edict,” the AI model can also explain . the recommendation was issued: why one should stay indoors (e.g., increased risk of flare-ups), why further calorie intake should be avoided (e.g., met the 作者: syring 時(shí)間: 2025-3-28 02:40
PMApp: An Interactive Process Mining Toolkit for?Building Healthcare Dashboardsthe healthcare sector lacks a comprehensive solution addressing flexibility, connectivity, and usability. This paper presents the PMApp, the Interactive Process Mining Toolkit, a specialized tool for healthcare professionals. PMApp is designed to adapt to diverse scenarios, seamlessly integrate with作者: expeditious 時(shí)間: 2025-3-28 09:00
A Data-Driven Framework for Improving Clinical Managements of Severe Paralytic Ileus in ICU: From Panical pathways, identifying risk factors on high-frequency pathways may facilitate the efficient optimization of clinical processes. This paper illustrated a data-driven framework that combines local process optimization and conceptual model validation. Frequent clinic pathways and contributing fact作者: adumbrate 時(shí)間: 2025-3-28 12:57
Phenotypes vs Processes: Understanding the?Progression of?Complications in?Type 2 Diabetes. A?Case Sht glycemic control, both in terms of glycated hemoglobin and the time in range, with different metabolic pathways that contribute to nervous, micro, and macro-vascular complications. As a matter of fact, a few works attempted to obtain phenotyping to describe the evolution of type 2 diabetes patien作者: Nucleate 時(shí)間: 2025-3-28 15:23 作者: Instantaneous 時(shí)間: 2025-3-28 20:01
Understanding Prostate Cancer Care Process Using Process Mining: A Case Studydiagnosed with prostate cancer go through established procedures, and the decisions made about the treatments are vital due to cancer’s unfavorable essence evolution. In this context, prostate-specific antigen tests are helpful in stratifying surveillance and subsequent risk and are monitored for re作者: 小教堂 時(shí)間: 2025-3-29 01:49 作者: membrane 時(shí)間: 2025-3-29 06:13
Jose M. Juarez,Carlos Fernandez-Llatas,Alfredo Vel作者: 搖曳的微光 時(shí)間: 2025-3-29 08:44 作者: 記憶法 時(shí)間: 2025-3-29 12:20
An Explainable AI Framework for?Treatment Failure Model for?Oncology Patientsvides explanations like feature analysis, counterfactual, and top risk factors that contribute to a treatment failure. As a result, the framework adds an explainability layer between treatment failure predictive model and oncologists, thereby enabling evidence based assistance to oncologists in desi作者: Infect 時(shí)間: 2025-3-29 17:06 作者: conformity 時(shí)間: 2025-3-29 19:55
Explanations of?Symbolic Reasoning to?Effect Patient Persuasion and?Educationnations for AI in Notation3) to explain symbolic reasoning inferences in a trace-based, contrastive, and counterfactual way. We applied this framework to explain recommendations for Chronic Obstructive Pulmonary Disease (COPD) patients to avoid flare-ups. For evaluation, we propose a questionnaire t作者: 他很靈活 時(shí)間: 2025-3-30 02:57
