Advances in chronic kidney disease最新文献_第2页

What's Old Is New Again: Harnessing the Power of Original Experiments to Learn Renal Physiology 旧的又是新的:利用原始实验的力量来学习肾脏生理学

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-11-01 DOI: 10.1053/j.ackd.2022.03.006

Melanie P. Hoenig, Stewart H. Lecker, Jeffrey H. William

{"title":"What's Old Is New Again: Harnessing the Power of Original Experiments to Learn Renal Physiology","authors":"Melanie P. Hoenig, Stewart H. Lecker, Jeffrey H. William","doi":"10.1053/j.ackd.2022.03.006","DOIUrl":"10.1053/j.ackd.2022.03.006","url":null,"abstract":"<div><p><span>Although medical schools across the United States have updated their curricula to incorporate active learning techniques, there has been little discussion on the nature of the content presented to students. Here, we share detailed examples of our experience in using original experiments to lay the groundwork for foundational concepts in renal physiology<span> and pathophysiology. We believe that this approach offers distinct advantages over standard case-based teaching by (1) starting with simple concepts, (2) analyzing memorable visuals, (3) increasing graphical literacy, (4) </span></span>translating<span> observations to “rules,” (5) encouraging critical thinking, and (6) providing historical perspective to the study of medicine. Although we developed this content for medical students, we have found that many of these lessons are also appropriate as foundational concepts for residents and fellows and serve as an excellent springboard for increasingly complex discussions of clinical applications of physiology. The use of original experiments for teaching and learning in renal physiology harnesses skills in critical thinking and provides a solid foundation that will help learners with subsequent case-based learning in the preclerkship curriculum and in the clinical arena.</span></p></div>","PeriodicalId":7221,"journal":{"name":"Advances in chronic kidney disease","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10576931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Postgraduate Education and Training for the Nephrology Physician Assistants and Nurse Practitioners 肾内科医师助理和执业护士研究生教育与培训

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-11-01 DOI: 10.1053/j.ackd.2022.03.007

Amy Sears , Jane Davis , Kim Zuber

{"title":"Postgraduate Education and Training for the Nephrology Physician Assistants and Nurse Practitioners","authors":"Amy Sears , Jane Davis , Kim Zuber","doi":"10.1053/j.ackd.2022.03.007","DOIUrl":"10.1053/j.ackd.2022.03.007","url":null,"abstract":"<div><p>There is no consistent educational model to introduce the physician assistant<span> and/or nurse practitioner to nephrology<span>. The job descriptions of the nephrology physician assistant/nurse practitioner may be similar, but the training, state and federal licensing, background, and recertification are different for the 2 professions adding a level of complexity to the training of the physician assistant/nurse practitioner new to nephrology. On-the-job training is the most common modality, but formats, content, mentors, and practices vary from organization to organization and even within organizations. The advantage of on-the-job training is its flexibility while the disadvantage is its nonspecific outcomes. As nephrology practices vary widely and range from single provider private practices to multiprovider academic practices, it is difficult if not impossible to develop a generic orientation model. This article outlines the history and present state of postgraduate educational offerings for the physician assistant/nurse practitioner and provides insight into components of an ideal training program.</span></span></p></div>","PeriodicalId":7221,"journal":{"name":"Advances in chronic kidney disease","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10570129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Future of Artificial Intelligence and Machine Learning in Kidney Health and Disease 人工智能和机器学习在肾脏健康和疾病中的未来

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-09-01 DOI: 10.1053/j.ackd.2022.09.001

Girish N. Nadkarni, Peter Kotanko

引用次数: 0

Practical Implementation and Challenges of Artificial Intelligence-Driven Electronic Health Record Evaluation: Protected Health Information 人工智能驱动的电子健康记录评估的实际实施与挑战:受保护的健康信息

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-09-01 DOI: 10.1053/j.ackd.2022.05.003

Adam P. Tashman PhD

引用次数: 0

Natural Language Processing in Nephrology 肾脏病学中的自然语言处理

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-09-01 DOI: 10.1053/j.ackd.2022.07.001

Tielman T. Van Vleck , Douglas Farrell , Lili Chan

{"title":"Natural Language Processing in Nephrology","authors":"Tielman T. Van Vleck , Douglas Farrell , Lili Chan","doi":"10.1053/j.ackd.2022.07.001","DOIUrl":"10.1053/j.ackd.2022.07.001","url":null,"abstract":"<div><p><span>Unstructured data in the electronic health records contain essential patient information. Natural language processing (NLP), teaching a computer to read, allows us to tap into these data without needing the time and effort of manual chart abstraction. The core first step for all NLP algorithms is preprocessing the text to identify the core words that differentiate the text while filtering out the noise. Traditional NLP uses a rule-based approach, applying grammatical rules to infer meaning from the text. Newer NLP approaches use machine learning/deep learning which can infer meaning without explicitly being programmed. NLP use in </span>nephrology<span><span> research has focused on identifying distinct disease processes, such as CKD, and extraction of patient-oriented outcomes such as symptoms with high sensitivity. NLP can identify patient features from clinical text associated with </span>acute kidney injury<span> and progression of CKD. Lastly, inclusion of features extracted using NLP improved the performance of risk-prediction models compared to models that only use structured data. Implementation of NLP algorithms has been slow, partially hindered by the lack of external validation of NLP algorithms. However, NLP allows for extraction of key patient characteristics from free text, an infrequently used resource in nephrology.</span></span></p></div>","PeriodicalId":7221,"journal":{"name":"Advances in chronic kidney disease","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10136386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Artificial Intelligence Systems in CKD: Where Do We Stand and What Will the Future Bring? CKD中的人工智能系统:我们在哪里，未来会带来什么?

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-09-01 DOI: 10.1053/j.ackd.2022.06.004

Arjun Ananda Padmanabhan, Emily A. Balczewski, Karandeep Singh

引用次数: 0

Recent Advances and Future Perspectives in the Use of Machine Learning and Mathematical Models in Nephrology 机器学习和数学模型在肾脏病学应用的最新进展和未来展望

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-09-01 DOI: 10.1053/j.ackd.2022.07.002

Paulo Paneque Galuzio, Alhaji Cherif

{"title":"Recent Advances and Future Perspectives in the Use of Machine Learning and Mathematical Models in Nephrology","authors":"Paulo Paneque Galuzio, Alhaji Cherif","doi":"10.1053/j.ackd.2022.07.002","DOIUrl":"10.1053/j.ackd.2022.07.002","url":null,"abstract":"<div><p><span><span>We reviewed some of the latest advancements in the use of mathematical models in nephrology<span>. We looked over 2 distinct categories of mathematical models that are widely used in biological research and pointed out some of their strengths and weaknesses when applied to health care<span>, especially in the context of nephrology. A mechanistic dynamical system allows the representation of causal relations among the system variables but with a more complex and longer development/implementation phase. Artificial intelligence/machine learning provides predictive tools that allow identifying correlative patterns in large data sets, but they are usually harder-to-interpret black boxes. Chronic kidney disease (CKD), a major worldwide </span></span></span>health problem<span>, generates copious quantities of data that can be leveraged by choice of the appropriate model; also, there is a large number of dialysis parameters that need to be determined at every treatment session that can benefit from predictive mechanistic models. Following important steps in the use of mathematical methods in </span></span>medical science might be in the intersection of seemingly antagonistic frameworks, by leveraging the strength of each to provide better care.</p></div>","PeriodicalId":7221,"journal":{"name":"Advances in chronic kidney disease","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9122284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can Artificial Intelligence Assist in Delivering Continuous Renal Replacement Therapy? 人工智能能否辅助持续肾替代治疗?

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-09-01 DOI: 10.1053/j.ackd.2022.08.001

Nada Hammouda , Javier A. Neyra

{"title":"Can Artificial Intelligence Assist in Delivering Continuous Renal Replacement Therapy?","authors":"Nada Hammouda , Javier A. Neyra","doi":"10.1053/j.ackd.2022.08.001","DOIUrl":"10.1053/j.ackd.2022.08.001","url":null,"abstract":"<div><p><span><span><span>Continuous renal replacement therapy (CRRT) is widely utilized to support critically ill patients with </span>acute kidney injury. Artificial intelligence (AI) has the potential to enhance CRRT delivery, but evidence is limited. We reviewed existing literature on the utilization of AI in CRRT with the objective of identifying current gaps in evidence and research considerations. We conducted a scoping review focusing on the development or use of AI-based tools </span>in patients<span> receiving CRRT. Ten papers were identified; 6 of 10 (60%) published in 2021, and 6 of 10 (60%) focused on machine learning models to augment CRRT delivery. All innovations were in the design/early validation phase of development. Primary research interests focused on early indicators of CRRT need, prognostication of mortality and kidney recovery, and identification of risk factors for mortality. Secondary research priorities included dynamic CRRT monitoring, predicting CRRT-related complications, and automated data pooling for point-of-care analysis. Literature gaps included prospective validation and implementation, biases ascertainment, and evaluation of AI-generated </span></span>health care disparities. Research on AI applications to enhance CRRT delivery has grown exponentially in the last years, but the field remains premature. There is a need to evaluate how these applications could enhance bedside decision-making capacity and assist structure and processes of CRRT delivery.</p></div>","PeriodicalId":7221,"journal":{"name":"Advances in chronic kidney disease","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10131397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Artificial Intelligence in Acute Kidney Injury Prediction 人工智能在急性肾损伤预测中的应用

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-09-01 DOI: 10.1053/j.ackd.2022.07.009

Tushar Bajaj, Jay L. Koyner

引用次数: 2

Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit 重症监护病房急性肾损伤预测的机器学习

IF 2.9 3区医学

Advances in chronic kidney disease Pub Date : 2022-09-01 DOI: 10.1053/j.ackd.2022.06.005

Eric R. Gottlieb , Mathew Samuel , Joseph V. Bonventre , Leo A. Celi , Heather Mattie

引用次数: 5