標(biāo)題: Titlebook: Artificial Intelligence in Medicine; 22nd International C Joseph Finkelstein,Robert Moskovitch,Enea Parimbel Conference proceedings 2024 Th [打印本頁] 作者: hearken 時間: 2025-3-21 16:38
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: 裝飾 時間: 2025-3-21 23:26 作者: 六邊形 時間: 2025-3-22 03:41
Ashley N. Matskevich,Matcheri S. Keshavanealthcare practitioners deeper insights into the factors affecting COVID-19 patient readmission risks. These findings can potentially empower clinicians to refine interventions and care strategies, mitigating adverse outcomes and advancing healthcare delivery for individuals affected by COVID-19.作者: 開玩笑 時間: 2025-3-22 06:57 作者: opprobrious 時間: 2025-3-22 09:54 作者: 惡意 時間: 2025-3-22 16:30
Evaluating the?TMR Model for?Multimorbidity Decision Support Using a?Community-of-Practice Based Metfew that supports automated detection of adverse interactions. However, it falls short on temporal reasoning and reasoning about drug dosage. Our study also represents the first independent validation of the evaluation methodology published in [.].作者: 壯觀的游行 時間: 2025-3-22 19:25
Identifying Factors Associated with COVID-19 All-Cause 90-Day Readmission: Machine Learning Approachealthcare practitioners deeper insights into the factors affecting COVID-19 patient readmission risks. These findings can potentially empower clinicians to refine interventions and care strategies, mitigating adverse outcomes and advancing healthcare delivery for individuals affected by COVID-19.作者: 易受騙 時間: 2025-3-23 00:39
Minimizing Survey Questions for?PTSD Prediction Following Acute Traumas a feature selection problem and consider 4 different feature selection approaches. We demonstrate that it is possible to achieve up to . accuracy for predicting the 3-month PTSD diagnosis from 10 survey questions using a mean decrease in impurity-based feature selector followed by a gradient boosting classifier.作者: nautical 時間: 2025-3-23 03:19
0302-9743 n Medicine, AIME 2024, held in Salt Lake City, UT, USA, during July 9-12, 2024...The 54 full papers and 22 short papers presented in the book were carefully reviewed and selected from 335 submissions...The papers are grouped in the following topical sections:..Part I: Predictive modelling and diseas作者: 機(jī)警 時間: 2025-3-23 05:51
Huijun Li,Daniel I. Shapiro,Larry J. Seidmandisease mechanisms, root causes and future disease progression at a patient cohort level thereby enabling early interventions for complex diseases and promoting an evidence based precision medicine approach for healthcare providers.作者: 使成核 時間: 2025-3-23 12:52
Monica Thielking,Mark D. Terjeseniction. Employing a multivariate time series, predictions are made with 30-minute and 60-minute horizons. The proposed model is comparable with state-of-the-art models on the OhioT1DM dataset, encompassing eight weeks of data from 12 distinct patients.作者: Ostrich 時間: 2025-3-23 14:57
Mining Disease Progression Patterns for?Advanced Disease Surveillancedisease mechanisms, root causes and future disease progression at a patient cohort level thereby enabling early interventions for complex diseases and promoting an evidence based precision medicine approach for healthcare providers.作者: Soliloquy 時間: 2025-3-23 18:11 作者: 不舒服 時間: 2025-3-24 02:11
Boosting Multitask Decomposition: Directness, Sequentiality, Subsampling, Cross-Gradientslity of using partial data, and (4) the applicability of gradient-based cross-training task affinities in auxiliary task selection. We apply the methods to a drug-target interaction prediction problem.作者: 討人喜歡 時間: 2025-3-24 02:25 作者: Missile 時間: 2025-3-24 10:21 作者: 召集 時間: 2025-3-24 13:46 作者: 繁榮地區(qū) 時間: 2025-3-24 15:31
0302-9743 e risk prediction; natural language processing; bioinformatics and omics; and wearable devices, sensors, and robotics...Part II: Medical imaging analysis; data integration and multimodal analysis; and explainable AI..978-3-031-66537-0978-3-031-66538-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Host142 時間: 2025-3-24 19:23 作者: 空氣傳播 時間: 2025-3-25 00:53
Natural History of Atopic Eczemality of using partial data, and (4) the applicability of gradient-based cross-training task affinities in auxiliary task selection. We apply the methods to a drug-target interaction prediction problem.作者: bioavailability 時間: 2025-3-25 05:40
Daniel I. Shapiro,Huijun Li,Larry J. Seidmanors of childhood obesity, taking into account the relationship among the data gathered in different visits. The experiments carried out on the data collected from 386 children from Girona and Figueres (Spain) demonstrate the relevance of discriminant frequent patterns for childhood overweight prediction.作者: Occupation 時間: 2025-3-25 07:55 作者: tattle 時間: 2025-3-25 15:33
Applying Gaussian Mixture Model for Clustering Analysis of Emergency Room Patients Based on Intubatir clustering based on intubation status. Out of 137,722 cases spanning January 1, 2017, to September 30, 2023, 1.14% underwent intubation. The study included the following variables: continuous variables such as WBC (White Blood Cell count), Hb (Hemoglobin), Hct (Hematocrit), MCV (Mean Corpuscular V作者: Slit-Lamp 時間: 2025-3-25 19:31 作者: glamor 時間: 2025-3-25 20:01
Boosting Multitask Decomposition: Directness, Sequentiality, Subsampling, Cross-Gradients largely empirical and computationally demanding process. To reduce the computational cost while maintaining statistical rigor, we investigate (1) the concept of direct transfer effect between tasks, (2) the use of sequential learning to minimize the number of test-train data splits, (3) the possibi作者: MEEK 時間: 2025-3-26 03:59
Diagnostic Modeling to Identify Unrecognized Inpatient Hypercapnia Using Health Record Data standard diagnostic test, but it is painful and not routine. When clinicians fail to make the diagnosis, it is often because an arterial blood gas was not obtained. This ‘partial verification’ of CO. levels presents a challenge for machine learning algorithms. We assessed the accuracy of two machin作者: 鞠躬 時間: 2025-3-26 06:09
Enhancing Hypotension Prediction in?Real-Time Patient Monitoring Through Deep Learning: A Novel Applmental in this field, are hampered by their dependence on structured historical data and manual feature extraction techniques. These methods often fall short of recognizing the intricate patterns present in physiological signals. Addressing this limitation, our study introduces an innovative applica作者: 不可接觸 時間: 2025-3-26 09:55 作者: 軍火 時間: 2025-3-26 15:39
Frequent Patterns of?Childhood Overweight from?Longitudinal Data on?Parental and?Early-Life of?Infanomising solutions thanks to the use of Artificial Intelligence applied to data from cohorts of children. Previous studies have analyzed the data without taking into account the relationship of data regarding when they are collected. In this work, frequent pattern mining is used to find the risk fact作者: 飾帶 時間: 2025-3-26 20:33
Fuzzy Neural Network Model Based on?Uni-Nullneuron in?Extracting Knowledge About Risk Factors of?Matchildren. This paper introduces an interpretable fuzzy neural network model that uses artificial intelligence (AI) techniques for early risk detection in pregnancy. The model, which integrates a fuzzy inference system and a defuzzification process across three layers, provides deep insights by formu作者: 寄生蟲 時間: 2025-3-26 22:14 作者: Noisome 時間: 2025-3-27 03:15 作者: ostrish 時間: 2025-3-27 08:32 作者: BLINK 時間: 2025-3-27 13:09
Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Chental effects on patients’ physical and psychological wellbeing. This study aims to apply machine learning (ML) models to patient-reported, clinical, and demographic data to predict changes in physical well-being, social functioning, role functioning, usual activities, and mobility at 6, 12 and 18?w作者: 劇本 時間: 2025-3-27 14:49
Predicting Blood Glucose Levels with?LMU Recurrent Neural Networks: A Novel Computational Modelctions or insulin pumps. Consumer devices can forecast blood glucose levels by leveraging data from blood glucose sensors and other sources. Such predictions are valuable for informing patients about their blood glucose trajectory and supporting various downstream applications. Numerous machine-lear作者: cathartic 時間: 2025-3-27 18:38
Prediction Modelling and Data Quality Assessment for Nursing Scale in a Big Hospital: A Proposal to dentification of patients at risk of prolonged hospitalization or difficult discharge, and enables an estimation of care complexity during hospitalization. Given the high predictive value of these scales, it is important that the measurements are reported precisely. In this paper, we provide a gener作者: Grievance 時間: 2025-3-28 00:14 作者: Apraxia 時間: 2025-3-28 05:13 作者: 違抗 時間: 2025-3-28 10:17
Reinforcement Learning with Balanced Clinical Reward for Sepsis Treatmenttional treatment approaches, primarily reliant on clinicians’ judgment and standard guidelines, frequently fail to deliver personalized care. Moreover, clinical decisions may vary considerably among healthcare providers managing identical patient cases. In this study, we propose an innovative method作者: 離開 時間: 2025-3-28 14:16 作者: 約會 時間: 2025-3-28 15:39 作者: prodrome 時間: 2025-3-28 21:24 作者: Memorial 時間: 2025-3-28 23:20
Clinical Symptoms of Atopic Eczemacterial susceptibility to antibiotics is crucial, but it often takes several days. On the other hand, in medical decision support systems, such as the one proposed in this contribution, it is crucial to assess the uncertainty of the model when a decision is provided. In this work, we propose a model作者: A保存的 時間: 2025-3-29 04:18 作者: Counteract 時間: 2025-3-29 10:23
Natural History of Atopic Eczema standard diagnostic test, but it is painful and not routine. When clinicians fail to make the diagnosis, it is often because an arterial blood gas was not obtained. This ‘partial verification’ of CO. levels presents a challenge for machine learning algorithms. We assessed the accuracy of two machin作者: Autobiography 時間: 2025-3-29 13:06
Atopy: Condition, Disease, or Syndrome?mental in this field, are hampered by their dependence on structured historical data and manual feature extraction techniques. These methods often fall short of recognizing the intricate patterns present in physiological signals. Addressing this limitation, our study introduces an innovative applica作者: 叢林 時間: 2025-3-29 17:54
https://doi.org/10.1007/978-3-030-17336-4 of methods for detecting such guideline interactions have been developed, based on computer interpretable representations of guidelines. A recently published paper by Van Woensel et al. [.] compared a number of methods for detecting and resolving interactions between multiple guidelines. The curren作者: Antimicrobial 時間: 2025-3-29 21:55 作者: CAB 時間: 2025-3-30 00:07
Ashley N. Matskevich,Matcheri S. Keshavanchildren. This paper introduces an interpretable fuzzy neural network model that uses artificial intelligence (AI) techniques for early risk detection in pregnancy. The model, which integrates a fuzzy inference system and a defuzzification process across three layers, provides deep insights by formu作者: 自傳 時間: 2025-3-30 07:44 作者: 亂砍 時間: 2025-3-30 10:01
Huijun Li,Daniel I. Shapiro,Larry J. Seidmanstarting from the current health conditions, for individual patients or populations. Using a pattern mining algorithm, we extract disease trajectory patterns from temporally modeled encounters of 17 million patients in the medical knowledge graph and develop a disease surveillance system on 477,933 作者: 使痛苦 時間: 2025-3-30 13:25
Markerless Tracking for Augmented Realitypairments in both social and occupational functioning. There has been great interest in utilizing machine learning approaches to predict the development of PTSD in trauma patients from clinician assessment or survey-based psychological assessments. However, these assessments require a large number o作者: Ptosis 時間: 2025-3-30 17:09
Hong Hua,Leonard D. Brown,Rui Zhangental effects on patients’ physical and psychological wellbeing. This study aims to apply machine learning (ML) models to patient-reported, clinical, and demographic data to predict changes in physical well-being, social functioning, role functioning, usual activities, and mobility at 6, 12 and 18?w作者: Ptosis 時間: 2025-3-30 21:52 作者: 代理人 時間: 2025-3-31 00:51
ASD and Unlawful Behaviour: Background,dentification of patients at risk of prolonged hospitalization or difficult discharge, and enables an estimation of care complexity during hospitalization. Given the high predictive value of these scales, it is important that the measurements are reported precisely. In this paper, we provide a gener作者: Allergic 時間: 2025-3-31 06:39 作者: HUMID 時間: 2025-3-31 11:41
Autism and Child Psychopathology Seriescally done manually. The optimization process is further hindered by complex mathematical aspects, involving non-convex multi-objective inverse problems with a vast solution space. Expert bias introduces variability in clinical practice, as the preferences of radiation oncologists and medical physic作者: 不能根除 時間: 2025-3-31 15:03
https://doi.org/10.1007/978-3-319-06796-4tional treatment approaches, primarily reliant on clinicians’ judgment and standard guidelines, frequently fail to deliver personalized care. Moreover, clinical decisions may vary considerably among healthcare providers managing identical patient cases. In this study, we propose an innovative method作者: oblique 時間: 2025-3-31 19:04
Connor Morrow Kerns Ph.D.,Philip C. Kendall outcomes. However, CVA patients’ data is vertically distributed across hospitals and rehabilitation clinics. Centralizing distributed medical data in a central repository leads to difficulty concerning data privacy and data ownership. Vertical federated learning has been introduced as a solution, b作者: 特征 時間: 2025-3-31 23:00 作者: 積習(xí)已深 時間: 2025-4-1 05:39 作者: FLOUR 時間: 2025-4-1 06:36
Artificial Intelligence in Medicine978-3-031-66538-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: agonist 時間: 2025-4-1 13:14 作者: reperfusion 時間: 2025-4-1 15:57
