標(biāo)題: Titlebook: AI for Health Equity and Fairness; Leveraging AI to Add Arash Shaban-Nejad,Martin Michalowski,Simone Bianc Book 2024 The Editor(s) (if appl [打印本頁] 作者: INEPT 時間: 2025-3-21 18:30
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作者: cartilage 時間: 2025-3-21 20:14
Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories, Warning Scores (EWS) are widely deployed to measure overall health status, and risk of adverse outcomes, in hospital patients. However, current EWS are limited both by their lack of personalisation and use of static observations. We propose a pipeline that groups intensive care unit patients by the作者: 流動性 時間: 2025-3-22 02:01 作者: 售穴 時間: 2025-3-22 06:09 作者: goodwill 時間: 2025-3-22 10:19
,Generation of?Clinical Skin Images with?Pathology with?Scarce Data,ovided to healthcare providers and doctors. Dermatology is among the areas which can benefit from data-driven models, as the first step of identifying skin diseases typically consists of visual inspection (possibly followed by further analyses) and AI approaches are well-suited to classify images—if作者: 無底 時間: 2025-3-22 16:45
,MILFORMER: Weighted Dual Stream Class Centered Random Attention Multiple Instance Learning for?Wholmerged as a pivotal strategy to address the scarcity of localized annotations in WSI analysis. However, in the current landscape of state-of-the-art methods, the instance-level accuracy of these models significantly lags behind that of the bag-level. This article introduces MILFormer, a novel multi-作者: ABOUT 時間: 2025-3-22 19:30 作者: 吹牛者 時間: 2025-3-22 22:28 作者: bibliophile 時間: 2025-3-23 03:49 作者: Osteoarthritis 時間: 2025-3-23 08:34
,DOST—Domain Obedient Self-supervision for?Trustworthy Multi Label Classification with?Noisy Labels,ystems. Deep learning systems rely on enormous amounts of data, often accompanied by annotation errors, and do not natively abide by well-known medical principles. In diagnostic scenarios, lack of adherence to domain constraints make systems unreliable, and this problem is only exacerbated by annota作者: Silent-Ischemia 時間: 2025-3-23 11:17
,Using Large Language Models for?Generating Smart Contracts for?Health Insurance from?Textual Policiet blockchain-based smart contracts as they offer immutability, verifiability, scalability, and a trustless setting: any number of parties can use the smart contracts, and they need not have previously established trust relationships with each other. Our methodology generates outputs at increasing l作者: 說笑 時間: 2025-3-23 16:41 作者: cognizant 時間: 2025-3-23 21:17
Designing Retrieval-Augmented Language Models for Clinical Decision Support,ettings. Retrieval-augmented language models carry potential to relieve the information burden on clinicians in the next generation of clinical decision support systems by unifying non-parametric knowledge representations with parametric reasoning systems. Numerous designs for these semi-parametric 作者: Epithelium 時間: 2025-3-23 22:30 作者: ICLE 時間: 2025-3-24 04:30 作者: 雜役 時間: 2025-3-24 10:30
Knowledge-Grounded Medical Dialogue Generation,has become a critical step to assess treatment eligibility. In order to address the increasing volume?of APOE testing, tools to help patients understand genetic risk factors and their implications are urgently needed. Conversational agents powered by large language models (LLMs) can help triage pati作者: 儲備 時間: 2025-3-24 14:13
,Interpretable Classification of Early Stage Parkinson’s Disease from EEG,ch, representing EEG data as a 15-variate series of bandpower and peak frequency values/coefficients. We hypothesise that this representation captures essential information from the noisy EEG signal, improving disease detection. Statistical features extracted from this representation are input to in作者: Lament 時間: 2025-3-24 16:32 作者: gait-cycle 時間: 2025-3-24 21:42 作者: Permanent 時間: 2025-3-25 00:36 作者: reserve 時間: 2025-3-25 05:45 作者: 歌劇等 時間: 2025-3-25 11:25 作者: Dungeon 時間: 2025-3-25 12:36 作者: FLAX 時間: 2025-3-25 17:32 作者: Occupation 時間: 2025-3-25 21:48
Objektorientierte Programmierung,ble supplements supporting surgical applications and decision-making through computer vision. Particularly the field of image-guided surgery, such as laparoscopic and robotic-assisted surgery, benefits strongly from synthetic image datasets and virtual surgical training methods. Our study presents a作者: 反復(fù)無常 時間: 2025-3-26 02:18
Grundkurs Programmieren mit Visual Basicovided to healthcare providers and doctors. Dermatology is among the areas which can benefit from data-driven models, as the first step of identifying skin diseases typically consists of visual inspection (possibly followed by further analyses) and AI approaches are well-suited to classify images—if作者: discord 時間: 2025-3-26 08:22
Varianten von Alternative und Schleife,merged as a pivotal strategy to address the scarcity of localized annotations in WSI analysis. However, in the current landscape of state-of-the-art methods, the instance-level accuracy of these models significantly lags behind that of the bag-level. This article introduces MILFormer, a novel multi-作者: Original 時間: 2025-3-26 09:51 作者: 失誤 時間: 2025-3-26 14:52
Objektorientierte Programmierung,ge. Furthermore, data needed to assist with addressing this problem is frequently sparse in features. To address this data sparsity challenge, we use a masked Transformer-encoder to learn the relationship among all input features, dynamically masking input features that are not provided for each giv作者: 發(fā)微光 時間: 2025-3-26 20:30 作者: DEAWL 時間: 2025-3-26 23:17 作者: vascular 時間: 2025-3-27 04:39
Ereignisorientierte Programmierung,et blockchain-based smart contracts as they offer immutability, verifiability, scalability, and a trustless setting: any number of parties can use the smart contracts, and they need not have previously established trust relationships with each other. Our methodology generates outputs at increasing l作者: 相同 時間: 2025-3-27 07:52
Grundkurs Relationale Datenbankend other healthcare professionals to get clearance in advance from a health plan before performing a particular procedure on a patient in order to be eligible for payment coverage. For health insurance companies, approving PA requests for patients in the medical domain is a time-consuming and challen作者: 散布 時間: 2025-3-27 09:44
Grundkurs Relationale Datenbankenettings. Retrieval-augmented language models carry potential to relieve the information burden on clinicians in the next generation of clinical decision support systems by unifying non-parametric knowledge representations with parametric reasoning systems. Numerous designs for these semi-parametric 作者: 肌肉 時間: 2025-3-27 15:47
https://doi.org/10.1007/978-3-8348-9076-4ment and outcome. However, such data is difficult to apply to artificial intelligence (AI) systems due to its heterogeneity, sparsity and combinatorial complexity. This text proposes a novel pipeline to address such pitfalls by utilizing the structure of Systematized Nomenclature of Medicine Clinica作者: GEST 時間: 2025-3-27 20:38
Grundkurs Relationale Datenbankenowever, the exchange of sensitive patient data, such as chest X-rays, poses inherent privacy risks when shared with third parties. Addressing this concern, we propose MedBlindTuner, a privacy-preserving framework leveraging fully homomorphic encryption (FHE) and a data-efficient image transformer (D作者: ureter 時間: 2025-3-28 00:04
https://doi.org/10.1007/978-3-8348-9076-4has become a critical step to assess treatment eligibility. In order to address the increasing volume?of APOE testing, tools to help patients understand genetic risk factors and their implications are urgently needed. Conversational agents powered by large language models (LLMs) can help triage pati作者: Jubilation 時間: 2025-3-28 04:43
https://doi.org/10.1007/978-3-8348-9076-4ch, representing EEG data as a 15-variate series of bandpower and peak frequency values/coefficients. We hypothesise that this representation captures essential information from the noisy EEG signal, improving disease detection. Statistical features extracted from this representation are input to in作者: 強制性 時間: 2025-3-28 08:47
https://doi.org/10.1007/978-3-8348-9076-4I-powered generation of medical image reports. However, some limitations remain. For example, generated reports may be lengthy without highlighting anomalies as desired; some minor features might be neglected, which fails in fine-grained labeling.To tackle the aforementioned challenges, this paper p作者: 尊嚴(yán) 時間: 2025-3-28 11:41
Grundkurs Relationale Datenbankenoncerns and misinformation might inform the healthcare space by helping public health efforts strategically allocate resources or information campaigns. We explore the task of detecting vaccine concerns in online discourse using large language models (LLMs) in a zero-shot setting without the need fo作者: ascetic 時間: 2025-3-28 17:48
Arash Shaban-Nejad,Martin Michalowski,Simone BiancHighlights the latest achievements in the use of AI in improving healthy equity.Includes revised versions of selected papers presented at the 2024 AAAI Workshop on Health Intelligence.Interconnects th作者: Medicare 時間: 2025-3-28 20:33 作者: NOTCH 時間: 2025-3-29 00:07
AI for Health Equity and Fairness978-3-031-63592-2Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: 是限制 時間: 2025-3-29 06:19 作者: 不可侵犯 時間: 2025-3-29 07:29 作者: induct 時間: 2025-3-29 12:34
,Navigating the?Synthetic Realm: Harnessing Diffusion-Based Models for?Laparoscopic Text-to-Image GeA validation study with a human assessment survey underlines the realistic nature of our synthetic data, as medical personnel detects actual images in a pool with generated images causing a false-positive rate of 66%. In addition, the investigation of a state-of-the-art machine learning model to rec作者: 懸掛 時間: 2025-3-29 16:15 作者: white-matter 時間: 2025-3-29 19:47
,Using Large Language Models for?Generating Smart Contracts for?Health Insurance from?Textual Policisess the LLM output, we propose ., ., ., ., and . as metrics. Our evaluation employs three health insurance policies (.) with increasing difficulty from Medicare’s official booklet. Our evaluation uses GPT-3.5 Turbo, GPT-3.5 Turbo 16K, GPT-4, GPT-4 Turbo and CodeLLaMA. Our findings confirm that LLMs作者: 遺產(chǎn) 時間: 2025-3-30 00:42
Can GPT Improve the State of Prior Authorization Via Guideline Based Automated Question Answering?,s introduce our own novel prompting technique. Moreover, we report qualitative assessment by humans on the natural language generation outputs from our approach. Results show that our method achieves superior performance with the mean weighted F1 score of 0.61 as compared to its standard counterpart作者: COLON 時間: 2025-3-30 04:53
Knowledge-Grounded Medical Dialogue Generation,n effectiveness. First,?we build a knowledge bank of recorded patient-provider genetic counseling sessions and leverage an open-source LLM to extract?and summarize relevant information. We leverage this knowledge bank?to develop a retrieval-augmented system for answering patient questions. We find t作者: 冒煙 時間: 2025-3-30 11:30 作者: Prologue 時間: 2025-3-30 15:27 作者: 艱苦地移動 時間: 2025-3-30 19:21
,Hierarchical Multi-label Classification of?Online Vaccine Concerns,entions a vaccine concern or not, works the best. Our results indicate that GPT-4 can strongly outperform crowdworker accuracy when compared to ground truth annotations provided by experts on the recently introduced VaxConcerns dataset, achieving an overall F1 score of ..作者: 情感脆弱 時間: 2025-3-30 21:19 作者: 咽下 時間: 2025-3-31 03:33 作者: Cryptic 時間: 2025-3-31 05:17 作者: dithiolethione 時間: 2025-3-31 12:44
Ereignisorientierte Programmierung,but also improves learning performance in key metrics and minimizes the effect of annotation noise. This novel approach uses domain guidance to detect offending annotations and deter rule-violating predictions in a self-supervised manner, thus making it more “data efficient” and domain compliant.作者: Fluctuate 時間: 2025-3-31 15:43
Ereignisorientierte Programmierung,sess the LLM output, we propose ., ., ., ., and . as metrics. Our evaluation employs three health insurance policies (.) with increasing difficulty from Medicare’s official booklet. Our evaluation uses GPT-3.5 Turbo, GPT-3.5 Turbo 16K, GPT-4, GPT-4 Turbo and CodeLLaMA. Our findings confirm that LLMs作者: 歌唱隊 時間: 2025-3-31 18:13 作者: BILL 時間: 2025-4-1 01:25
https://doi.org/10.1007/978-3-8348-9076-4n effectiveness. First,?we build a knowledge bank of recorded patient-provider genetic counseling sessions and leverage an open-source LLM to extract?and summarize relevant information. We leverage this knowledge bank?to develop a retrieval-augmented system for answering patient questions. We find t作者: indenture 時間: 2025-4-1 03:33 作者: VEN 時間: 2025-4-1 09:50
https://doi.org/10.1007/978-3-8348-9076-4t more sensitive multi-label classification. Extensive experiments over two known public datasets, the IU X-ray dataset and the PEIR Gross dataset, have demonstrated the effectiveness of the presented SVAML framework.作者: Harness 時間: 2025-4-1 14:14
Grundkurs Relationale Datenbankenentions a vaccine concern or not, works the best. Our results indicate that GPT-4 can strongly outperform crowdworker accuracy when compared to ground truth annotations provided by experts on the recently introduced VaxConcerns dataset, achieving an overall F1 score of ..作者: 沒有貧窮 時間: 2025-4-1 16:05 作者: 否決 時間: 2025-4-1 19:10 作者: 檢查 時間: 2025-4-2 02:10
Book 2024pers presented at the 2024 Health Intelligence workshop, co-located with the Thirty-Eight Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides
