Hasil untuk "Medical emergencies. Critical care. Intensive care. First aid"

Di Zhang, Zhangpeng Gong, Xiaobo Pang et al.

Foundation models have recently achieved impressive success in computational pathology, demonstrating strong generalization across diverse histopathology tasks. However, existing models overlook the heterogeneous and non-uniform organization of pathological regions of interest (ROIs) because they rely on natural image backbones not tailored for tissue morphology. Consequently, they often fail to capture the coherent tissue architecture beyond isolated patches, limiting interpretability and clinical relevance. To address these challenges, we present Cross-modal Adaptive Region Encoder (CARE), a foundation model for pathology that automatically partitions WSIs into several morphologically relevant regions. Specifically, CARE employs a two-stage pretraining strategy: (1) a self-supervised unimodal pretraining stage that learns morphological representations from 34,277 whole-slide images (WSIs) without segmentation annotations, and (2) a cross-modal alignment stage that leverages RNA and protein profiles to refine the construction and representation of adaptive regions. This molecular guidance enables CARE to identify biologically relevant patterns and generate irregular yet coherent tissue regions, selecting the most representative area as ROI. CARE supports a broad range of pathology-related tasks, using either the ROI feature or the slide-level feature obtained by aggregating adaptive regions. Based on only one-tenth of the pretraining data typically used by mainstream foundation models, CARE achieves superior average performance across 33 downstream benchmarks, including morphological classification, molecular prediction, and survival analysis, and outperforms other foundation model baselines overall.

Zury S. Rosas-Vaquero, Iván llescas-Martínez, Marco Antonio-Martínez et al.

Neil Natarajan, Sruthi Viswanathan, Xavier Roberts-Gaal et al.

Static solutions don't serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore how generative AI has the potential to address current capacity constraints in neuropsychology, potentially enabling more personalized and longitudinal care pathways. In particular, AI can efficiently conduct frequent, low-level experience sampling from patients and facilitate diagnostic reconciliation across care pathways. We envision a future where mental health care benefits from continuous, rich, and patient-centered data sampling to dynamically adapt to individual patient needs and evolving conditions, thereby improving both accessibility and efficacy of treatment. We further propose the use of mental health digital twins (MHDTs) - continuously updated computational models that capture individual symptom dynamics and trajectories - as a transformative framework for personalized mental health care. We ground this framework in empirical evidence and map out the research agenda required to refine and operationalize it.

Jing Fu Lin, Shu Ping Chuang, Jo Yung Wei Wu

Background: The rising prevalence of overweight in Taiwan has raised concerns about its psychological impact, particularly depression. Research suggests that perceived overweight, body image dissatisfaction, and weight-based rejection sensitivity may mediate this relationship. However, limited studies have explored these mechanisms in Taiwanese populations. Aim: To examine whether perceived overweight, body-image dissatisfaction, and weight-based rejection sensitivity mediate the association between body mass index (BMI) and depressive symptoms in Taiwanese adults. Methods: In this cross-sectional survey, 607 participants (291 university students, 316 community residents) completed measures of BMI, perceived overweight, body-image dissatisfaction, weight-based rejection sensitivity, and depressive symptoms. Parallel and serial mediation analyses with 5000-sample bootstrap were conducted, controlling for gender, age, and earliest overweight age. Results: BMI was indirectly associated with depressive symptoms through perceived weight (β = 0.12, P = 0.022). Perceived weight further predicted depression through body image dissatisfaction (β = 0.22, P < 0.001) and rejection sensitivity (β = 0.12, P < 0.001). BMI also contributed to greater body image dissatisfaction (β = 0.08, P < 0.001) and rejection sensitivity (β = 0.07, P < 0.001) through heavier perceived weight, leading to more depressive symptoms. Conclusion: Depressive symptoms associated with higher BMI are largely driven by individuals’ perception of being overweight and the resulting body dissatisfaction and anticipated weight stigma, rather than weight itself. Interventions should, therefore, target weight perception, promote positive body image, and reduce weight-related stigma to mitigate depression risk.

Najah Queenland, Matthew D. Holmes, Paxton Prather et al.

<b>Background:</b> Gastrointestinal bleeding (GIB) is a frequent emergency department (ED) presentation with rare but life-threatening causes, including arterio-enteric fistulas (AEF), which account for less than 1% of GIB cases. Ilio-enteric fistulas are even more rare but have similar morbidity and mortality. <b>Methods:</b> This case report describes a 51-year-old male with a history of type 2 diabetes mellitus, diabetic retinopathy, and pancreas–kidney transplantation who presented to the ED with a massive hemorrhage from an ilio-enteric fistula. Despite initial stability, the patient became hypotensive and deteriorated to pulseless electrical activity (PEA) arrest. Despite multiple arrests, he survived and was discharged to a rehabilitation facility. <b>Results:</b> AEFs, particularly iliac-enteric fistulas, are diagnostically challenging and often present with nonspecific symptoms. Diagnostic imaging, especially CT angiography, is crucial, although initial non-contrast CT may miss the diagnosis. Early consultation with vascular surgery is essential for managing these patients. <b>Conclusions:</b> This case underscores the need to consider AEF in the differential diagnosis of GIB, particularly in post-transplant patients, and highlights the importance of prompt intervention.

Yong Ma, Oda Elise Nordberg, Jessica Hubbers et al.

Dementia has serious consequences for the daily life of the person affected due to the decline in the their cognitive, behavioral and functional abilities. Caring for people living with dementia can be challenging and distressing. Innovative solutions are becoming essential to enrich the lives of those impacted and alleviate caregiver burdens. This scoping review, spanning literature from 2010 to July 2023 in the field of Human-Computer Interaction (HCI), offers a comprehensive look at how interactive technology contributes to dementia care. Emphasizing technology's role in addressing the unique needs of people with dementia (PwD) and their caregivers, this review encompasses assistive devices, mobile applications, sensors, and GPS tracking. Delving into challenges encountered in clinical and home-care settings, it succinctly outlines the influence of cutting-edge technologies, such as wearables, virtual reality, robots, and artificial intelligence, in supporting individuals with dementia and their caregivers. We categorize current dementia-related technologies into six groups based on their intended use and function: 1) daily life monitoring, 2) daily life support, 3) social interaction and communication, 4) well-being enhancement, 5) cognitive support, and 6) caregiver support.

Fan Bai, Yuxin Du, Tiejun Huang et al.

Medical image analysis is essential to clinical diagnosis and treatment, which is increasingly supported by multi-modal large language models (MLLMs). However, previous research has primarily focused on 2D medical images, leaving 3D images under-explored, despite their richer spatial information. This paper aims to advance 3D medical image analysis with MLLMs. To this end, we present a large-scale 3D multi-modal medical dataset, M3D-Data, comprising 120K image-text pairs and 662K instruction-response pairs specifically tailored for various 3D medical tasks, such as image-text retrieval, report generation, visual question answering, positioning, and segmentation. Additionally, we propose M3D-LaMed, a versatile multi-modal large language model for 3D medical image analysis. Furthermore, we introduce a new 3D multi-modal medical benchmark, M3D-Bench, which facilitates automatic evaluation across eight tasks. Through comprehensive evaluation, our method proves to be a robust model for 3D medical image analysis, outperforming existing solutions. All code, data, and models are publicly available at: https://github.com/BAAI-DCAI/M3D.

Anil Palepu, Vikram Dhillon, Polly Niravath et al.

Large language models (LLMs) have shown remarkable progress in encoding clinical knowledge and responding to complex medical queries with appropriate clinical reasoning. However, their applicability in subspecialist or complex medical settings remains underexplored. In this work, we probe the performance of AMIE, a research conversational diagnostic AI system, in the subspecialist domain of breast oncology care without specific fine-tuning to this challenging domain. To perform this evaluation, we curated a set of 50 synthetic breast cancer vignettes representing a range of treatment-naive and treatment-refractory cases and mirroring the key information available to a multidisciplinary tumor board for decision-making (openly released with this work). We developed a detailed clinical rubric for evaluating management plans, including axes such as the quality of case summarization, safety of the proposed care plan, and recommendations for chemotherapy, radiotherapy, surgery and hormonal therapy. To improve performance, we enhanced AMIE with the inference-time ability to perform web search retrieval to gather relevant and up-to-date clinical knowledge and refine its responses with a multi-stage self-critique pipeline. We compare response quality of AMIE with internal medicine trainees, oncology fellows, and general oncology attendings under both automated and specialist clinician evaluations. In our evaluations, AMIE outperformed trainees and fellows demonstrating the potential of the system in this challenging and important domain. We further demonstrate through qualitative examples, how systems such as AMIE might facilitate conversational interactions to assist clinicians in their decision making. However, AMIE's performance was overall inferior to attending oncologists suggesting that further research is needed prior to consideration of prospective uses.

Noman Ali, Alan Tan, Jordan Chenkin

Abstract Background In the Emergency Department (ED), patients may present with various injuries that damage muscles, tendons, ligaments, and bony structures. Fractures, joint dislocations, strains, and sprains are prevalent among them. However, distal biceps tendon ruptures are uncommon. Case Report Here, we report a case of a young man presented to the ED with a complaint of left arm pain following a martial arts activity. The diagnosis of distal biceps tendon rupture was made using a point-of-care ultrasound (PoCUS), and an early referral to the orthopedic service was provided. Conclusion This case highlights the utility of point-of-care ultrasound in assessing musculoskeletal injuries in the ED. Early incorporation of PoCUS into routine clinical practice can potentially improve the overall care of musculoskeletal injuries.

Hannah Schuster, Axel Polleres, Johannes Wachs

This research studies how populations depend on road networks for access to health care during crises or natural disasters. So far, most researchers rather studied the accessibility of the whole network or the cost of network disruptions in general, rather than as a function of the accessibility of specific priority destinations like hospitals. Even short delays in accessing healthcare can have significant adverse consequences. We carry out a comprehensive stress test of the entire Austrian road network from this perspective. We simplify the whole network into one consisting of what we call accessibility corridors, deleting single corridors to evaluate the change in accessibility of populations to healthcare. The data created by our stress test was used to generate an importance ranking of the corridors. The findings suggest that certain road segments and corridors are orders of magnitude more important in terms of access to hospitals than the typical one. Our method also highlights vulnerable municipalities and hospitals who may experience demand surges as populations are cut off from their usual nearest hospitals. Even though the skewed importance of some corridors highlights vulnerabilities, they provide policymakers with a clear agenda.

Oisin Fitzgerald, Oscar Perez-Concha, Blanca Gallego-Luxan et al.

Irregularly measured time series are common in many of the applied settings in which time series modelling is a key statistical tool, including medicine. This provides challenges in model choice, often necessitating imputation or similar strategies. Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations through incorporating continuous evolution of the hidden states between observations. This is achieved using a neural ordinary differential equation (ODE) or neural flow layer. In this manuscript, we give an overview of these models, including the varying architectures that have been proposed to account for issues such as ongoing medical interventions. Further, we demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting using electronic medical record and simulated data. The experiments confirm that addition of a neural ODE or neural flow layer generally improves the performance of autoregressive recurrent neural networks in the irregular measurement setting. However, several CTRNN architecture are outperformed by an autoregressive gradient boosted tree model (Catboost), with only a long short-term memory (LSTM) and neural ODE based architecture (ODE-LSTM) achieving comparable performance on probabilistic forecasting metrics such as the continuous ranked probability score (ODE-LSTM: 0.118$\pm$0.001; Catboost: 0.118$\pm$0.001), ignorance score (0.152$\pm$0.008; 0.149$\pm$0.002) and interval score (175$\pm$1; 176$\pm$1).

Kenta Takatsu, Alexander W. Levis, Edward Kennedy et al.

Comparative effectiveness research frequently employs the instrumental variable design since randomized trials can be infeasible for many reasons. In this study, we investigate and compare treatments for emergency cholecystitis -- inflammation of the gallbladder. A standard treatment for cholecystitis is surgical removal of the gallbladder, while alternative non-surgical treatments include managed care and pharmaceutical options. As randomized trials are judged to violate the principle of equipoise, we consider an instrument for operative care: the surgeon's tendency to operate. Standard instrumental variable estimation methods, however, often rely on parametric models that are prone to bias from model misspecification. We outline instrumental variable estimation methods based on the doubly robust machine learning framework. These methods enable us to employ various machine learning techniques for nuisance parameter estimation and deliver consistent estimates and fast rates of convergence for valid inference. We use these methods to estimate the primary target causal estimand in an IV design. Additionally, we expand these methods to develop estimators for heterogeneous causal effects, profiling principal strata, and a sensitivity analyses for a key instrumental variable assumption. We conduct a simulation study to demonstrate scenarios where more flexible estimation methods outperform standard methods. Our findings indicate that operative care is generally more effective for cholecystitis patients, although the benefits of surgery can be less pronounced for key patient subgroups.

Michael G. Adam, Sebastian Eger, Martin Piccolrovazzi et al.

As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We further provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot.

Aakanksha Naik, Bailey Kuehl, Erin Bransom et al.

Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for limited aspects of this problem, failing to capture the real-world complexity and nuance required. Focusing on biomedicine, this work presents CARE -- a new IE dataset for the task of extracting clinical findings. We develop a new annotation schema capturing fine-grained findings as n-ary relations between entities and attributes, which unifies phenomena challenging for current IE systems such as discontinuous entity spans, nested relations, variable arity n-ary relations and numeric results in a single schema. We collect extensive annotations for 700 abstracts from two sources: clinical trials and case reports. We also demonstrate the generalizability of our schema to the computer science and materials science domains. We benchmark state-of-the-art IE systems on CARE, showing that even models such as GPT4 struggle. We release our resources to advance research on extracting and aggregating literature findings.

Lea Steffen, Martin Schulze, Christian Eichmann et al.

Care robotics as a research field has developed a lot in recent years, driven by the rapidly increasing need for it. However, these technologies are mostly limited to a very concrete and usually relatively simple use case. The bimanual robot House of Living Labs intelligent Escort (HoLLiE) includes an omnidirectional mobile platform. This paper presents how HoLLiE is adapted, by flexible software and hardware modules, for different care applications. The design goal of HoLLiE was to be human-like but abstract enough to ensure a high level of acceptance, which is very advantageous for its use in hospitals. After a short retrospect of previous generations of HoLLiE, it is highlighted how the current version is equipped with a variety of additional sensors and actuators to allow a wide range of possible applications. Then, the software stack of HoLLiE is depicted, with the focus on navigation and force sensitive intention recognition.

Raghuraman M. Sethuraman, Shanthi Ponnusamy

Masahiro Ojima, Kenichiro Ishida, Yusuke Katayama et al.

Aim The nationwide impact of the coronavirus disease (COVID‐19) pandemic on major trauma in Japan is unknown. The nationwide registry‐based data of the Japanese Trauma Data Bank were analyzed to elucidate the impact of COVID‐19 on the epidemiology, treatment, and outcomes of major trauma patients. Methods Among patients transported directly from the injury site by ambulance with an Injury Severity Score of ≥16, we compared patients managed from April to December in 2019 to those managed from April to December in 2020. Results In total, 9792 patients were included in this study (2019, n = 5194; 2020, n = 4598). There were no significant differences in age or sex, but there were significant differences between 2019 and 2020 in the rates of “self‐injury (suicide)”, “motor vehicle accident”, “fall from height”, “fall down”, and “fall to the ground”, which are factors associated with patient age. Injury severity in 2019 and 2020 did not differ to a statistically significant extent, but the rate of major spinal injury increased. The time of prehospital care significantly increased in 2020 compared to 2019. There was no noticeable change in hospital treatment or in‐hospital mortality between 2019 and 2020. Conclusion This study suggests that the COVID‐19 pandemic might have altered the injuries of major trauma; however, medical services for major trauma were well supplied in Japan in 2020.

Sasikumar Mahalingam, Gunaseelan Rajendran, Saravanan Muthusamy et al.

Paula Maddigan, Teo Susnjak

Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to inadequate staffing levels. These delays have been linked with adverse clinical outcomes. Previous research into forecasting demand this domain has mostly used a collection of statistical techniques, with machine learning approaches only now beginning to emerge in recent literature. The forecasting problem for this domain is difficult and has also been complicated by the COVID-19 pandemic which has introduced an additional complexity to this estimation due to typical demand patterns being disrupted. This study explores the ability of machine learning methods to generate accurate patient presentations at two large urgent care clinics located in Auckland, New Zealand. A number of machine learning algorithms were explored in order to determine the most effective technique for this problem domain, with the task of making forecasts of daily patient demand three months in advance. The study also performed an in-depth analysis into the model behaviour in respect to the exploration of which features are most effective at predicting demand and which features are capable of adaptation to the volatility caused by the COVID-19 pandemic lockdowns. The results showed that ensemble-based methods delivered the most accurate and consistent solutions on average, generating improvements in the range of 23%-27% over the existing in-house methods for estimating the daily demand.

Bengisu Cagiltay, Joseph Michaelis, Sarah Sebo et al.

Research in child-robot interactions suggests that engaging in "care-taking" of a social robot, such as tucking the robot in at night, can strengthen relationships formed between children and robots. In this work, we aim to better understand and explore the design space of caretaking activities with 10 children, aged 8--12 from eight families, involving an exploratory design session followed by a preliminary feasibility testing of robot caretaking activities. The design sessions provided insight into children's current caretaking tasks, how they would take care of a social robot, and how these new caretaking activities could be integrated into their daily routines. The feasibility study tested two different types of robot caretaking tasks, which we call connection and utility, and measured their short term effects on children's perceptions of and closeness to the social robot. We discuss the themes and present interaction design guidelines of robot caretaking activities for children.