Hasil untuk "Medicine (General)"

Kipp W. Johnson, Jessica Torres Soto, Benjamin S. Glicksberg et al.

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.

C. Deakin, J. Nolan, J. Soar et al.

B. Wilkoff, C. Love, C. Byrd et al.

M. Claudon, C. Dietrich, B. Choi et al.

S. M. K. Rates

J. Mcwhirter, C. Pennington

D. Sackett

Libin Cui, Hyunkyung Jeong, F. Borovečki et al.

H. Colten, B. Altevogt

M. Heel, G. Harauz, E. Orlova et al.

W. Evans

M. Kinoshita, K. Yokote, H. Arai et al.

Toray Industries, Inc., Tokyo, Japan Department of Diabetes, Metabolism and Endocrinology, Chiba University Graduate School of Medicine, Chiba, Japan National Center for Geriatrics and Gerontology, Aichi, Japan Department of Internal Medicine and Cardiology, Gifu Prefectural General Medical Center, Gifu, Japan Division of Diabetes and Metabolism, Department of Internal Medicine, Iwate Medical University, Iwate, Japan Division of Endocrinology and Metabolism, Department of Medicine, Jichi Medical University, Tochigi, Japan Center for Integrated Medical Research, Hiroshima University Hospital, Hiroshima, Japan Egusa Genshi Clinic, Hiroshima, Japan Department of Cardiovascular Medicine, Juntendo University, Tokyo, Japan Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan Biomedical Informatics, Osaka University, Osaka, Japan Division of Cardiology, Department of Medicine, Showa University School of Medicine, Tokyo, Japan Department of Community Health Systems Nursing, Ehime University Graduate School of Medicine, Ehime, Japan Department of Vascular Medicine, Osaka City University Graduate School of Medicine, Osaka, Japan Department of Internal Medicine, Teikyo University School of Medicine, Tokyo, Japan Department of Vascular Surgery, Saitama Medical Center, Saitama, Japan Chief Health Management Department, Mitsui Chemicals Inc., Tokyo, Japan Department of Pediatrics, Showa University School of Medicine, Tokyo, Japan Department of Neurology, Kita-Harima Medical Center, Hyogo, Japan Department of Internal Medicine, Mizonokuchi Hospital, Teikyo University School of Medicine, Kanagawa, Japan Division of Cardiology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan Tsukasa Health Care Hospital, Kagoshima, Japan Faculty of Nutrition, Division of Clinical Nutrition, Kobe Gakuin University, Hyogo, Japan Department of Food and Nutrition, Faculty of Human Sciences and Design, Japan Women’s University, Tokyo, Japan 25 Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Osaka, Japan Department of Medical Statistics, Toho University, Tokyo, Japan Department of Clinical Innovative Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan Department of Laboratory Medicine, Jikei University Kashiwa Hospital, Chiba, Japan Department of Geriatric and General Medicine, Osaka University Graduate School of Medicine, Osaka, Japan Department of Obstetrics and Gynecology, Aichi Medical University, Aichi, Japan 31 Department of Community Medicine, Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Osaka, Japan Rinku General Medical Center, Osaka, Japan

Fei Liu, D. Lagares, K. Choi et al.

D. Santos, D. Giese, S. Brodehl et al.

M. Beach, E. Price, T. Gary et al.

R. Bulger

سمیه علی نسب, نجمه حمید, مالک میرهاشمی

مقاله پژوهشیمقدمه: سرطان پستان، شایع‌ترین سرطان زنان است. درد و خستگی مرتبط سرطان یکی از فراوان‌ترین و ناتوان‌کننده‌ترین پیامدهایی است که کیفیت زندگی این بیماران را تحت تأثیر قرار ‌می‌دهد. از این‌رو هدف این پژوهش، مقایسه‌ی اثربخشی درمان آگاهی و ابراز هیجانی (Emotional Awareness and Expression Therapy) EAET و درمان پذیرش و تعهد (Acceptance and Commitment Therapy) ACT برخستگی مرتبط با سرطان و شدت درد زنان مبتلا به سرطان پستان بود.روش‌ها: پژوهش حاضر از نوع نیمه آزمایشی با طرح پیش‌آزمون- پس‌آزمون با گروه شاهد و پیگیری دو ماهه بود. جامعه‌ی آماری پژوهش شامل زنان مبتلا به سرطان پستان مراجعه‌کننده به مرکز خیریه مهر سهیلا واقع در شهرستان البرز بود. شرکت‌کنندگان با رضایت آگاهانه در پژوهش شرکت کردند. تعداد 75 نفر واجد شرایط با نمونه‌گیری در دسترس انتخاب و به صورت تصادفی در دو گروه آزمایشی درمان آگاهی و ابراز هیجانی (25 نفر) و درمان پذیرش و تعهد (25 نفر) و گروه شاهد (25 نفر) جایگزین شدند. برای گروه‌های آزمایشی به‌طور جداگانه روش درمان آگاهی و ابراز هیجانی و پذیرش و تعهد طی 8 جلسه 90 دقیقه‌ای به صورت درمان گروهی در فاصله‌ی زمانی زمستان 1402 تا بهار 1403 اجرا گردید و داده‌ها از طریق پرسش‌نامه‌های خستگی ناشی از سرطان و مقیاس دیداری درد جمع‌آوری شد. داده‌های حاصل با تحلیل کوواریانس با اندازه‌گیری مکرر تحلیل شد.یافته‌ها: هر دو روش درمانی به طور معنی‌داری بر کاهش درد و خستگی مرتبط با سرطان مؤثر بوده است و درمان آگاهی و ابراز هیجانی، تأثیر بیشتری نسبت به درمان پذیرش و تعهد بر نمره‌ی کلی درد داشت و این اثر تا مرحله‌ی پیگیری ادامه یافت (0/05 > P).نتیجه‌گیری: آموزش آگاهی و ابراز احساسات و درمان پذیرش و تعهد در کاهش درد و خستگی بیماران مبتلا به سرطان پستان مؤثر بود. در بیماران دچار سرطان پستان، به دلیل عدم آگاهی نسبت به ماهیت بیماری و نقش تعیین‌کننده عوامل روان‌شناختی در میزان بهبودی و فرایند درمان، لازم است شرایطی فراهم نمود که احساسات و هیجان‌های آنها به خوبی تنظیم شده و ابراز شوند، لذا توصیه می‌شود که در کنار درمان‌های دارویی، روانشناسان سلامت در مراکز درمانی و کلینیک‌های روانشناختی از درمان مبتنی بر پذیرش و تعهد و به ویژه درمان آگاهی و ابراز هیجانی جهت کمک به بیماران مبتلا به سرطان پستان در پذیرش بیماری و تلاش برای درمان آن و تخلیه و تنظیم هیجانات خود و کاهش درد و خستگی ناشی از بیماری استفاده کنند.

Chao‐Wen Chen, Yung‐Sung Yeh, Ta‐Chien Chan et al.

Abstract Introduction The acute care system faced significant challenges in managing healthcare emergencies due to a lack of coordination between emergency services and logistical support. This disorganization undermined collaboration and response efficiency. Methods Taiwan's Presidential Hackathon introduced an innovative approach to improving the trauma system by integrating digital pipeline science through public–private partnerships (PPPs). This initiative specifically addressed inefficiencies and complexities in the acute care ecosystem, brought to light by the catastrophic 2014 gas explosion in Kaohsiung City. Results The hackathon led to the development of a unified digital platform for emergency data management. This platform significantly enhanced communication, data sharing, and coordination across healthcare sectors, culminating in the implementation of a digital pre‐hospital emergency care system across multiple administrative regions. Conclusion Our experience demonstrated the effectiveness of leveraging digital technologies, PPPs, and the hackathon model to revolutionize emergency healthcare management and response systems through cross‐sector collaboration.

Hongqin Chen, Jian Meng, Qiao Li et al.

Abstract Background The heterogeneity of patient-reported outcomes (PROs) and patient-reported outcome measures (PROMs) in published clinical studies on vaginal relaxation syndrome (VRS) hinders cross-study comparisons and integration of evidence-based findings, impeding the development of robust clinical evidence. Objective To comprehensively investigate the current use of PROs and PROMs in VRS research, compile a comprehensive catalog, and provide guidance for selecting outcome measures and tools VRS patients. Methods This study systematically searched clinical studies on VRS treatment published up to December 2024 in PUBMED, EMBASE, Web of Science, and Cochrane databases, focusing primarily on pelvic floor muscle training, physical energy therapies, and surgical interventions. PROs and PROMs were extracted, organized into a structured catalog, and categorized by thematic domains. The COSMIN checklist was applied to assess the measurement properties of commonly used PROMs. Results A total of 69 studies were included, comprising 14 randomized controlled trials (1193 patients) and 55 observational studies (3327 patients), totaling 4520 participants. These studies reported 68 PROs and 57 PROMs. The most commonly used PROMs were the Female Sexual Function Index (FSFI, 47.83%), Vaginal Laxity Questionnaire (VLQ), Visual Analog Scale (VAS), Pelvic Organ Prolapse/Urinary Incontinence Sexual Questionnaire (PISQ-12), and Sexual Satisfaction Questionnaire (SSQ). Notably, 42 PROMs (73.68%) appeared only once. Conclusions PROs for surgical and non-surgical VRS treatments are similar, but non-surgical interventions include additional outcomes, such as overall efficacy and patient’s vaginal tightness satisfaction. The high proportion of unvalidated PROMs (81.09%) underscores the need for standardized, disease-specific measures. Future Delphi surveys and expert consensus are anticipated to facilitate the development of a comprehensive core outcome set (COS) and core outcome measurement set (COMS) for VRS.

Anran Li, Lingfei Qian, Mengmeng Du et al.

Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to enhance domain-specific accuracy and safety. However, a key open question remains: to what extent do LLMs memorize medical training data. Memorization can be beneficial when it enables LLMs to retain valuable medical knowledge during domain adaptation. Yet, it also raises concerns. LLMs may inadvertently reproduce sensitive clinical content (e.g., patient-specific details), and excessive memorization may reduce model generalizability, increasing risks of misdiagnosis and making unwarranted recommendations. These risks are further amplified by the generative nature of LLMs, which can not only surface memorized content but also produce overconfident, misleading outputs that may hinder clinical adoption. In this work, we present a study on memorization of LLMs in medicine, assessing its prevalence (how frequently it occurs), characteristics (what is memorized), volume (how much content is memorized), and potential downstream impacts (how memorization may affect medical applications). We systematically analyze common adaptation scenarios: (1) continued pretraining on medical corpora, (2) fine-tuning on standard medical benchmarks, and (3) fine-tuning on real-world clinical data, including over 13,000 unique inpatient records from Yale New Haven Health System. The results demonstrate that memorization is prevalent across all adaptation scenarios and significantly higher than that reported in the general domain. Moreover, memorization has distinct characteristics during continued pre-training and fine-tuning, and it is persistent: up to 87% of content memorized during continued pre-training remains after fine-tuning on new medical tasks.