Hasil untuk "History of medicine. Medical expeditions"

Arti Virkud, Jessie K. Edwards, Michele Jonsson Funk et al.

Identifying optimal medical treatments to improve survival has long been a critical goal of pharmacoepidemiology. Traditionally, we use an average treatment effect measure to compare outcomes between treatment plans. However, new methods leveraging advantages of machine learning combined with the foundational tenets of causal inference are offering an alternative to the average treatment effect. Here, we use three unique, precision medicine algorithms (random forests, residual weighted learning, efficient augmentation relaxed learning) to identify optimal treatment rules where patients receive the optimal treatment as indicated by their clinical history. First, we present a simple hypothetical example and a real-world application among heart failure patients using Medicare claims data. We next demonstrate how the optimal treatment rule improves the absolute risk in a hypothetical, three-modifier setting. Finally, we identify an optimal treatment rule that optimizes the time to outcome in a real-world heart failure setting. In both examples, we compare the average time to death under the optimized, tailored treatment rule with the average time to death under a universal treatment rule to show the benefit of precision medicine methods. The improvement under the optimal treatment rule in the real-world setting is greatest (additional ~9 days under the tailored rule) for survival time free of heart failure readmission.

Wenxuan Wang, Zizhan Ma, Zheng Wang et al.

Large Language Models (LLMs) are transforming healthcare through the development of LLM-based agents that can understand, reason about, and assist with medical tasks. This survey provides a comprehensive review of LLM-based agents in medicine, examining their architectures, applications, and challenges. We analyze the key components of medical agent systems, including system profiles, clinical planning mechanisms, medical reasoning frameworks, and external capacity enhancement. The survey covers major application scenarios such as clinical decision support, medical documentation, training simulations, and healthcare service optimization. We discuss evaluation frameworks and metrics used to assess these agents' performance in healthcare settings. While LLM-based agents show promise in enhancing healthcare delivery, several challenges remain, including hallucination management, multimodal integration, implementation barriers, and ethical considerations. The survey concludes by highlighting future research directions, including advances in medical reasoning inspired by recent developments in LLM architectures, integration with physical systems, and improvements in training simulations. This work provides researchers and practitioners with a structured overview of the current state and future prospects of LLM-based agents in medicine.

Sobhan Ghezloo, Babak Daneshfard, Ebrahim Khadem

Rui Yang, Yilin Ning, Emilia Keppo et al.

Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of external knowledge. With the rapid advancement of generative AI, RAG can pave the way for connecting this transformative technology with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care.

Xiaofei Chen

This article explores the critical role of statistical analysis in precision medicine. It discusses how personalized healthcare is enhanced by statistical methods that interpret complex, multidimensional datasets, focusing on predictive modeling, machine learning algorithms, and data visualization techniques. The paper addresses challenges in data integration and interpretation, particularly with diverse data sources like electronic health records (EHRs) and genomic data. It also delves into ethical considerations such as patient privacy and data security. In addition, the paper highlights the evolution of statistical analysis in medicine, core statistical methodologies in precision medicine, and future directions in the field, emphasizing the integration of artificial intelligence (AI) and machine learning (ML).

Sophia Yazzourh, Nicolas Savy, Philippe Saint-Pierre et al.

The goal of precision medicine is to provide individualized treatment at each stage of chronic diseases, a concept formalized by Dynamic Treatment Regimes (DTR). These regimes adapt treatment strategies based on decision rules learned from clinical data to enhance therapeutic effectiveness. Reinforcement Learning (RL) algorithms allow to determine these decision rules conditioned by individual patient data and their medical history. The integration of medical expertise into these models makes possible to increase confidence in treatment recommendations and facilitate the adoption of this approach by healthcare professionals and patients. In this work, we examine the mathematical foundations of RL, contextualize its application in the field of DTR, and present an overview of methods to improve its effectiveness by integrating medical expertise.

Anthony Hughes, Xingyi Song

Evidence-based medicine is the practice of making medical decisions that adhere to the latest, and best known evidence at that time. Currently, the best evidence is often found in the form of documents, such as randomized control trials, meta-analyses and systematic reviews. This research focuses on aligning medical claims made on social media platforms with this medical evidence. By doing so, individuals without medical expertise can more effectively assess the veracity of such medical claims. We study three core tasks: identifying medical claims, extracting medical vocabulary from these claims, and retrieving evidence relevant to those identified medical claims. We propose a novel system that can generate synthetic medical claims to aid each of these core tasks. We additionally introduce a novel dataset produced by our synthetic generator that, when applied to these tasks, demonstrates not only a more flexible and holistic approach, but also an improvement in all comparable metrics. We make our dataset, the Expansive Medical Claim Corpus (EMCC), available at https://zenodo.org/records/8321460

Yushen Xu, Xiaosong Li, Yuchan Jie et al.

In clinical practice, tri-modal medical image fusion, compared to the existing dual-modal technique, can provide a more comprehensive view of the lesions, aiding physicians in evaluating the disease's shape, location, and biological activity. However, due to the limitations of imaging equipment and considerations for patient safety, the quality of medical images is usually limited, leading to sub-optimal fusion performance, and affecting the depth of image analysis by the physician. Thus, there is an urgent need for a technology that can both enhance image resolution and integrate multi-modal information. Although current image processing methods can effectively address image fusion and super-resolution individually, solving both problems synchronously remains extremely challenging. In this paper, we propose TFS-Diff, a simultaneously realize tri-modal medical image fusion and super-resolution model. Specially, TFS-Diff is based on the diffusion model generation of a random iterative denoising process. We also develop a simple objective function and the proposed fusion super-resolution loss, effectively evaluates the uncertainty in the fusion and ensures the stability of the optimization process. And the channel attention module is proposed to effectively integrate key information from different modalities for clinical diagnosis, avoiding information loss caused by multiple image processing. Extensive experiments on public Harvard datasets show that TFS-Diff significantly surpass the existing state-of-the-art methods in both quantitative and visual evaluations. Code is available at https://github.com/XylonXu01/TFS-Diff.

Florentin Bieder, Julia Wolleb, Alicia Durrer et al.

Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In this work, we present a number of different ways to reduce the resource consumption for 3D diffusion models and apply them to a dataset of 3D images. The main contribution of this paper is the memory-efficient patch-based diffusion model \textit{PatchDDM}, which can be applied to the total volume during inference while the training is performed only on patches. While the proposed diffusion model can be applied to any image generation tasks, we evaluate the method on the tumor segmentation task of the BraTS2020 dataset and demonstrate that we can generate meaningful three-dimensional segmentations.

Mike Sharples

John Clark was inventor of the Eureka machine to generate hexameter Latin verse. He labored for 13 years from 1832 to implement the device that could compose at random over 26 million different lines of well-formed verse. This paper proposes that Clark should be regarded as an early cognitive scientist. Clark described his machine as an illustration of a theory of "kaleidoscopic evolution" whereby the Latin verse is "conceived in the mind of the machine" then mechanically produced and displayed. We describe the background to automated generation of verse, the design and mechanics of Eureka, its reception in London in 1845 and its place in the history of language generation by machine. The article interprets Clark's theory of kaleidoscopic evolution in terms of modern cognitive science. It suggests that Clark has not been given the recognition he deserves as a pioneer of computational creativity.

Srirang Manohar, Ioannis Sechopoulos, Mark A. Anastasio et al.

Phantoms are test objects used for initial testing and optimization of medical imaging techniques, but these rarely capture the complex properties of the tissue. Here we introduce super phantoms, that surpass standard phantoms being able to replicate complex anatomic and functional imaging properties of tissues and organs. These super phantoms can be computer models, inanimate physical objects, or ex-vivo organs. Testing on these super phantoms, will enable iterative improvements well before in-vivo studies, fostering innovation. We illustrate super phantom examples, address development challenges, and envision centralized facilities supporting multiple institutions in applying these models for medical advancements.

Cristina Sans-Ponseti

¿En qué contexto llegaron los desarrollos tecnocientíficos que hicieron posibles las transfusiones sanguíneas durante la primera mitad del siglo XX? ¿En qué trabajaban los médicos transfusores barceloneses antes de que la hemoterapia se profesionalizara? En este artículo intentamos resolver estas preguntas para el caso concreto del Instituto Central de Análisis Clínicos, Bacteriológicos y Químicos de Barcelona (1909-1939), dirigido por el doctor Josep Antoni Grifols Roig (1885-1976). Sus primeras patentes del test de Wassermann para la detección de la sífilis, o la técnica del aglutino-diagnóstico, nos indican cómo la sangre era concebida, en primer lugar, como una tecnología propia del laboratorio. Y para la obtención de muestras óptimas, fue necesario que Grifols desarrollara sus propias tecnologías médicas. Todo esto le reportó, además de un prestigio como médico analista, el conocimiento necesario para adaptar rápidamente su actividad a la medicina transfusional. Durante la década de los veinte, patentó el primer sistema de transfusión indirecta de España, que junto a sus sueros control, empezaron a convertir la sangre en un auténtico medicamento, utilizado para sanar. Las fuentes utilizadas son principalmente las contenidas en el Archivo Histórico Grifols, así como las actas de los primeros congresos de médicos en lengua catalana.

Галина Сиваченко, Антоніна Аністратенко

У статті розглядається склад кроскультурних процесів, пов’язаних із утвердженням «французького» роману В. Винниченка на тлі роману Просвітництва (Вольтер, Дідро, Руссо). Мета статті – визначити провідні естетичні компоненти та засоби формування філософських парадигм. Новизна й актуальність дослідження полягає в тому, що у центрі уваги вперше зосереджено генезис та особливості філософсько-архітектоніки «французьких» романів Володимира Винниченка «Проказа» та «Вічний імператив». Матеріали та методи. У статті використано унікальні джерела, зокрема щоденники В. Винниченка та тексти згаданих романів. Основним методом, використаним у статті, є порівняльний, а також підпорядкований принципам анахронічного функціонального аналізу, зіставлення творчих прийомів романної прози українського модерніста Винниченка та французьких митців Просвітництва. Висновки. Представлено концепцію філософськості цих модерністських творів, яку Винниченко виразно позначає жанровим експериментом, укоріненим у філософській прозі Просвітництва (Вольтер, Дідро, Руссо). Йдеться про особливості вияву філософії в різних формах: філософська повість, філософський діалог, філософський роман, а також про специфіку реалізації філософської сутності в романі-діалозі, романі-полеміці, романі-лапології. Особливу увагу приділено такому жанровому експерименту Вольтера і Винниченка, як філософська повість з такими поетичними ознаками, як алегорія, притча, умовність, екзотика. Критична оптика дослідження поєднує в собі історико-філософську специфіку Просвітництва, на якій ґрунтуються романи В. Винниченка, національну самосвідомість письменника та його біографічну індивідуальність.

Janaina Salvador Cardoso

Resumo Dos letrados que pensaram a economia portuguesa no século XVII, talvez Duarte Ribeiro de Macedo seja o mais conhecido. Formado em direito e em filosofia, Macedo escreveu diferentes discursos sobre a introdução de manufaturas e o maior aproveitamento dos recursos naturais encontrados nas colônias portuguesas. Em seu “Discurso sobre os gêneros para o comércio, que há no Maranhão e Pará” (1633), apresentou 37 gêneros de elevado potencial econômico e que estariam disponíveis no Maranhão e no Pará. Este trabalho se dedica à transcrição desse manuscrito inédito e à apresentação das propostas de Macedo acerca das potencialidades econômicas maranhenses.

Seungmann PARK

This article examines the technological competition between laparoscopic sterilization and mini-laparotomy from the 1960s to the 1980s in South Korea and analyzes the motives of obstetricians and gynecologists for participating in the Family Planning Program. Obstetricians and gynecologists were key actors in implementing the Program in the front line, but there is not enough research on why they became involved in the Program. Preceding studies describe the doctors as those who internalized historicism combined with population problems and devoted themselves to the cause of the state. However, it is difficult to find concerns about the nation’s future in the oral statements or memoirs of those who participated in the Program. This research focuses on the fact that laparoscopic sterilization, a complex and expensive technology, proliferated, rather than simple and inexpensive mini-laparotomy in South Korea, a low-income country where the Family Planning Program was implemented. This study also argues that behind this reversal of appropriateness lay the desire for advanced technology of elite obstetricians and gynecologists that cannot be reduced to the cause of the state.

Benjamin A. Cordier, Nicolas P. D. Sawaya, Gian G. Guerreschi et al.

Quantum computing holds significant potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning approaches for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is typically contingent on a reduction in the consumption of a computational resource, such as time, space, or data. Here, we distill the concept of a quantum advantage into a simple framework that we hope will aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to i) assess the potential of quantum advantages in specific application areas and ii) identify gaps that may be addressed with novel quantum approaches. Bearing in mind the rapid pace of change in the fields of quantum computing and classical algorithms, we aim to provide an extensive survey of applications in biology and medicine that may lead to practical quantum advantages.

Nasim Sadat Mosavi, Manuel Filipe Santos

Research is ongoing all over the world for identifying the barriers and finding effective solutions to accelerate the projection of Precision Medicine (PM) in the healthcare industry. Yet there has not been a valid and practical model to tackle the several challenges that have slowed down the widespread of this clinical practice. This study aimed to highlight the major limitations and considerations for implementing Precision Medicine. The two theories Diffusion of Innovation and Socio-Technical are employed to discuss the success indicators of PM adoption. Throughout the theoretical assessment, two key theoretical gaps are identified and related findings are discussed.

Ivanna STRUK

The research is devoted to the interaction of verbal and non-verbal means of communication in the dramatic text. In the scientific research the expansion of the semantic space of terms is taken into account. It helped to understand the theory and enabled to use the idea of communication. Taking into account the specificity of dramatic text (DT) – it’s clearly distinguished author's speech and speech of characters. DT is defined as a two-level entity, which is the unity of verbal (character speech) and non-verbal commu- nication (author’s communication); a complex semiotic unit, the po- lyphony of which forms author's speech. The novelty of scientific research is that the notion of implication for the first time were intro- duced to the terminology apparatus of the DT, which is understood by the minimal structural and semantic unity of verbal and non-verbal components (remarks and replicas) that are viewed as single visual, cognitive and communicative, provide a comprehensive pragmatic influence on the addressee. The relevance of scientific research is determined by the need to analyze extralinguistic implicatures with remarks on the desig- nation of silence and their linguistic means in the dramatic texts of Bukovinian writers (Yu. Fedkovich, S. Vorobkevich, S. Yarychevsky, I. Sinyuk). Communicatively meaningful silence has a certain emotion- al color and is a means of communication that gives a variety of com- municative meanings of a semantically-pragmatic nature. The following methods and techniques of linguistic analy- sis are used to achieve the goal and task: the system-functional analy- sis, the method of discursive analysis, the contextual-interpretive meth- od, the method of stylistic analysis, the conversion analysis. Conclusions. Silence is a full-fledged extralinguistic compo- nent of the implicature in dramatic text. Communicatively meaningful silence has a certain emotional color and is a means of communication that conditions a variety of communicative meanings of a semantically- pragmatic nature. The prospect of the study involves a systematic anal- ysis of the implicatures, taking into account the universal, ethnospecific and individual markers of the dramatic text of the Bukovinian writers.

Pablo Toribio

Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge. Weakly-supervised learning can train models by relying on weaker forms of annotation, such as scribbles. Here, we learn to segment using scribble annotations in an adversarial game. With unpaired segmentation masks, we train a multi-scale GAN to generate realistic segmentation masks at multiple resolutions, while we use scribbles to learn their correct position in the image. Central to the model's success is a novel attention gating mechanism, which we condition with adversarial signals to act as a shape prior, resulting in better object localization at multiple scales. Subject to adversarial conditioning, the segmentor learns attention maps that are semantic, suppress the noisy activations outside the objects, and reduce the vanishing gradient problem in the deeper layers of the segmentor. We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks. We also demonstrate extensions in a variety of settings: semi-supervised learning; combining multiple scribble sources (a crowdsourcing scenario) and multi-task learning (combining scribble and mask supervision). We release expert-made scribble annotations for the ACDC dataset, and the code used for the experiments, at https://vios-s.github.io/multiscale-adversarial-attention-gates