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We present a comprehensive analysis of the periodic flares observed in the 6.7 GHz methanol transition in G22.356+0.066, utilizing the Maxwell-Bloch equations (MBEs) as a framework to model these phenomena. By solving the one-dimensional MBEs, we describe the behavior of both the quasi-steady-state maser and transient superradiance regimes. Our findings indicate that the observed periodic flares, with varying timescales across different velocities, are consistent with the characteristics of Dicke's superradiance, triggered by a common radiative pump in regions of varying inverted column densities. This work provides new insights into the physical processes governing variability in maser-hosting regions and underscores the significance of superradiance as a powerful radiation mechanism in astrophysical environments., Comment: 5 pages, 7 figures

We demonstrate that (001) grown Cd3As2 thin films with a superlattice-patterned gate can potentially realize the moir\'e Bernevig-Hughes-Zhang (BHZ) model. Our calculations identify the parameterization region necessary to achieve topological flat mini-bands with a C4z symmetric and a C6z symmetric potential. Additionally, we show that a spin-polarized state can serve as the minimal platform for hosting the moir\'e induced quantum anomalous Hall effect, supported by Hartree Fock interaction kernel analysis and self-consistent mean field calculations., Comment: 10 pages, 10 figures

Achieving precise control of photoinduced molecular charge transfer reactions underpins key emerging technologies. As such, the use of hybrid light-matter molecular exciton-polariton states has been proposed as a scheme to directly modify the efficiency and rate of such reactions. However, the efficacy of polariton-driven photochemistry remains an open question. Here, we demonstrate conditions under which photoinduced polaritonic charge transfer can be achieved and directly visualized using momentum resolved ultrafast spectroscopy. Key conditions for charge transfer are satisfied using Bloch surface wave polaritons, which exhibit favorable dispersion characteristics that permit the selective pumping of hybrid states with long lifetimes (100-400 fs) that permit vibrationally assisted molecular charge transfer. Using this approach, we tune the energetic driving force for charge separation, reducing it by as much as 0.5 eV compared to the bare exciton. These results establish that tunable and efficient polariton-driven molecular charge transfer is indeed possible using carefully considered photonic systems., Comment: 10 pages, 5 figures

Inspired by the entropy-area relation of black hole thermodynamics, we study the thermodynamics of cosmological apparent horizon in a spatially flat Friedmann-Robertson-Walker (FRW) universe in the framework of an Extended Uncertainty Principle (EUP). The adopted EUP naturally admits a minimal measurable momentum (equivalently a maximal measurable length), as an infrared cutoff in the theory. We derive the modified Friedmann equations in this setup and explore some predictions of these equations for the late time universe via distance measures. We show that in this framework it is possible to realize the late time cosmic speed-up and transition to the phantom phase of the equation of state parameter of the effective cosmic fluid without recourse to any dark energy component or modified gravity. Inspection of various distance measures in this framework shows that an EUP with a negative deformation parameter suffices for the interpretation of the late time asymptotically de Sitter universe with standard non-relativistic matter., Comment: 25 pages, 13 figures, Accepted for publication in The Astrophysical Journal

In this paper we construct a dissipative quintessential cosmic inflation. For this purpose, we add a multiplicative dissipative term in the standard quintessence field Lagrangian. We consider the specific form of dissipation as the time integral including the Hubble parameter and an arbitrary function that describes the dissipative properties of the quintessential scalar field. Inflation parameters and observables are calculated under slow-roll approximations and a detailed calculation of the cosmological perturbations is performed in this setup. We consider different forms of potentials and calculate the scalar spectral index and tensor-to-scalar ratio for a constant as well as variable dissipation function. To check the reliability of this model, a numerical analysis on the model parameters space is done in confrontation with recent observational data. By comparing the results with observational joint datasets at 68% and 95% confidence levels, we obtain some constraints on the model parameters space, specially the dissipation factor with e-folds numbers N = 55 and N = 60. As some specific results, we show that the power-law potential with a constant dissipation factor and N = 60 is mildly consistent with observational data in some restricted domains of the model parameter space with very small and negative dissipation factor and a negligible tensor-toscalar ratio. But this case with N = 55 is consistent with observation considerably. For power-law potential and variable dissipation factor as $Q = {\alpha}\phi^n$, the consistency with observation is also considerable with a reliable tensor-to-scalar ratio. The quadratic and quartic potentials with variable dissipation function as $Q = {\alpha}\phi^n$ are consistent with Planck2018 TT, TE, EE+lowE+lensing data at the 68% and 95% levels of confidence for some intervals of the parameter n., Comment: Accepted for publication in Physics of the Dark Universe, 37 pages, 14 figures, 8 tables

As more clinical workflows continue to be augmented by artificial intelligence (AI), AI literacy among physicians will become a critical requirement for ensuring safe and ethical AI-enabled patient care. Despite the evolving importance of AI in healthcare, the extent to which it has been adopted into traditional and often-overloaded medical curricula is currently unknown. In a scoping review of 1,699 articles published between January 2016 and June 2024, we identified 18 studies which propose guiding frameworks, and 11 studies documenting real-world instruction, centered around the integration of AI into medical education. We found that comprehensive guidelines will require greater clinical relevance and personalization to suit medical student interests and career trajectories. Current efforts highlight discrepancies in the teaching guidelines, emphasizing AI evaluation and ethics over technical topics such as data science and coding. Additionally, we identified several challenges associated with integrating AI training into the medical education program, including a lack of guidelines to define medical students AI literacy, a perceived lack of proven clinical value, and a scarcity of qualified instructors. With this knowledge, we propose an AI literacy framework to define competencies for medical students. To prioritize relevant and personalized AI education, we categorize literacy into four dimensions: Foundational, Practical, Experimental, and Ethical, with tailored learning objectives to the pre-clinical, clinical, and clinical research stages of medical education. This review provides a road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce., Comment: 25 pages, 2 figures, 3 tables

The glycocalyx is a complex layer of carbohydrate and protein molecules that surrounds the cell membrane of many types of mammalian cells. It serves several important functions, including cell adhesion and communication, and maintain cell shape and stability, especially in the case of erythrocytes. Alteration of glycocalyx composition represents a cardiovascular health threatening. For example, in diabetes mellitus glycocalyx of erythrocytes and of endothelial cells is known to be impaired, a potential source of blood occlusion in microcirculation, which may lead to blindness, and renal failure of patients. The impact of glycocalyx impairment on erythrocyte aggregation remains a largely unexplored research area. We conduct here in vitro-experiments in microfluidic devices in order to investigate erythrocytes aggregation incubated with amylase, an enzyme that partially breaks down glycocalyx molecules. It is found that incubation of erythrocytes by amylase leads to a dramatic increase of their aggregation and stability and alters the aggregates morphologies. Confocal microscopy analysis reveals a significant degradation of the glycocalyx layer, correlated with enhanced erythrocytes aggregation. An increased erythrocyte aggregation in vivo should affect oxygen and other metabolites delivery to organs and tissues. This study brings new elements about elucidation of microscopic origins of erythrocyte aggregation and their potential impact on cardiovascular pathologies.

Distributed Deep Neural Network (DNN) training is a technique to reduce the training overhead by distributing the training tasks into multiple accelerators, according to a parallelization strategy. However, high-performance compute and interconnects are needed for maximum speed-up and linear scaling of the system. Wafer-scale systems are a promising technology that allows for tightly integrating high-end accelerators with high-speed wafer-scale interconnects, making it an attractive platform for distributed training. However, the wafer-scale interconnect should offer high performance and flexibility for various parallelization strategies to enable maximum optimizations for compute and memory usage. In this paper, we propose FRED, a wafer-scale interconnect that is tailored for the high-BW requirements of wafer-scale networks and can efficiently execute communication patterns of different parallelization strategies. Furthermore, FRED supports in-switch collective communication execution that reduces the network traffic by approximately 2X. Our results show that FRED can improve the average end-to-end training time of ResNet-152, Transformer-17B, GPT-3, and Transformer-1T by 1.76X, 1.87X, 1.34X, and 1.4X, respectively when compared to a baseline waferscale 2D-Mesh fabric.

By considering the Friedmann equations emerging from the entropy-area law of black hole thermodynamics in the context of the generalized uncertainty principle, we study tachyon inflation in the early universe. The presence of a minimal length modifies the Friedmann equations and hence the slow-roll and perturbation parameters in the tachyon model. These modifications, though small, affect the viability of the tachyon inflation in confrontation with observational data. We compare the numerical results of the model with Planck2018 TT, TE, EE +lowE+lensing+BAO+BK14(18) data and Planck2018 TT, TE,EE +lowE+lensing+BK14(18) +BAO+LIGO $\&$ Virgo2016 data at $68\%$ and $95\%$ CL. We show that while the tachyon inflation with power-law, inverse power-law and inverse exponential potentials is not observationally viable in comparison with the $1\sigma$ and $2\sigma$ confidence levels of the new joint data, in the presence of the minimal length the model becomes observationally viable., Comment: Accepted for publication in Physics of the Dark Universe,29 pages, 12 figures, 10 tables

We present significant improvements to our previous work on noise reduction in {\sl Herschel} observation maps by defining sparse filtering tools capable of handling, in a unified formalism, a significantly improved noise reduction as well as a deconvolution in order to reduce effects introduced by the limited instrumental response (beam). We implement greater flexibility by allowing a wider choice of parsimonious priors in the noise-reduction process. More precisely, we introduce a sparse filtering and deconvolution approach approach of type $l^2$-$l^p$, with $p > 0$ variable and apply it to a larger set of molecular clouds using {\sl Herschel} 250 $\mu $m data in order to demonstrate their wide range of application. In the {\sl Herschel} data, we are able to use this approach to highlight extremely fine filamentary structures and obtain singularity spectra that tend to show a significantly less $\log$-normal behavior and a filamentary nature in the less dense regions. We also use high-resolution adaptive magneto-hydrodynamic simulation data to assess the quality of deconvolution in such a simulated beaming framework., Comment: 24 pages, 32 figures, official acceptance date on 16/05/2024 (Astronomy & Astrophysics)

In today's forex market traders increasingly turn to algorithmic trading, leveraging computers to seek more profits. Deep learning techniques as cutting-edge advancements in machine learning, capable of identifying patterns in financial data. Traders utilize these patterns to execute more effective trades, adhering to algorithmic trading rules. Deep reinforcement learning methods (DRL), by directly executing trades based on identified patterns and assessing their profitability, offer advantages over traditional DL approaches. This research pioneers the application of a multi-agent (MA) RL framework with the state-of-the-art Asynchronous Advantage Actor-Critic (A3C) algorithm. The proposed method employs parallel learning across multiple asynchronous workers, each specialized in trading across multiple currency pairs to explore the potential for nuanced strategies tailored to different market conditions and currency pairs. Two different A3C with lock and without lock MA model was proposed and trained on single currency and multi-currency. The results indicate that both model outperform on Proximal Policy Optimization model. A3C with lock outperforms other in single currency training scenario and A3C without Lock outperforms other in multi-currency scenario. The findings demonstrate that this approach facilitates broader and faster exploration of different currency pairs, significantly enhancing trading returns. Additionally, the agent can learn a more profitable trading strategy in a shorter time.

Infrared emission features toward interstellar gas of the IC 348 star cluster in Perseus have been recently proposed to originate from the amino acid tryptophan. The assignment was based on laboratory infrared spectra of tryptophan pressed into pellets, a method which is known to cause large frequency shifts compared to the gas phase. We assess the validity of the assignment based on the original Spitzer data as well as new data from JWST. In addition, we report new spectra of tryptophan condensed in para-hydrogen matrices to compare with the observed spectra. The JWST MIRI data do not show evidence for tryptophan, despite deeper integration toward IC 348. In addition, we show that several of the lines attributed to tryptophan are likely due to instrumental artifacts. This, combined with the new laboratory data, allows us to conclude that there is no compelling evidence for the tryptophan assignment.

In recent years, there has been a significant increase in applications of multimodal signal processing and analysis, largely driven by the increased availability of multimodal datasets and the rapid progress in multimodal learning systems. Well-known examples include autonomous vehicles, audiovisual generative systems, vision-language systems, and so on. Such systems integrate multiple signal modalities: text, speech, images, video, LiDAR, etc., to perform various tasks. A key issue for understanding such systems is the relationship between various modalities and how it impacts task performance. In this paper, we employ the concept of mutual information (MI) to gain insight into this issue. Taking advantage of the recent progress in entropy modeling and estimation, we develop a system called InfoMeter to estimate MI between modalities in a multimodal learning system. We then apply InfoMeter to analyze a multimodal 3D object detection system over a large-scale dataset for autonomous driving. Our experiments on this system suggest that a lower MI between modalities is beneficial for detection accuracy. This new insight may facilitate improvements in the development of future multimodal learning systems., Comment: 6 pages, 7 figures, IEEE MIPR 2024

Given the sheer volume of surgical procedures and the significant rate of postoperative fatalities, assessing and managing surgical complications has become a critical public health concern. Existing artificial intelligence (AI) tools for risk surveillance and diagnosis often lack adequate interpretability, fairness, and reproducibility. To address this, we proposed an Explainable AI (XAI) framework designed to answer five critical questions: why, why not, how, what if, and what else, with the goal of enhancing the explainability and transparency of AI models. We incorporated various techniques such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), counterfactual explanations, model cards, an interactive feature manipulation interface, and the identification of similar patients to address these questions. We showcased an XAI interface prototype that adheres to this framework for predicting major postoperative complications. This initial implementation has provided valuable insights into the vast explanatory potential of our XAI framework and represents an initial step towards its clinical adoption., Comment: 32 pages, 7 figures, 4 supplement figures and 1 supplement table

In this study, we introduce different chiro-optical structures composed of single, dimer, or trimer arrangements of plasmonic nanoantennae that can actively modulate the chiral transmission of light through the application of an external magnetic field. Specifically, we explore monomers, dimers, and trimers of trapezoidal nanoantennae, as well as trimer configurations of triangular nanoantennae, using Au nanoparticles as the plasmonic elements. To enable active magnetic control over the chiro-optical response in these chiral plasmonic systems, a single Ni nanoparticle is incorporated as the active magneto-plasmonic element. Our findings confirm the chirality of all proposed structures and demonstrate the feasibility of magnetically manipulating the chiro-optical response within these configurations.

Modern electronic health records (EHRs) hold immense promise in tracking personalized patient health trajectories through sequential deep learning, owing to their extensive breadth, scale, and temporal granularity. Nonetheless, how to effectively leverage multiple modalities from EHRs poses significant challenges, given its complex characteristics such as high dimensionality, multimodality, sparsity, varied recording frequencies, and temporal irregularities. To this end, this paper introduces a novel multimodal contrastive learning framework, specifically focusing on medical time series and clinical notes. To tackle the challenge of sparsity and irregular time intervals in medical time series, the framework integrates temporal cross-attention transformers with a dynamic embedding and tokenization scheme for learning multimodal feature representations. To harness the interconnected relationships between medical time series and clinical notes, the framework equips a global contrastive loss, aligning a patient's multimodal feature representations with the corresponding discharge summaries. Since discharge summaries uniquely pertain to individual patients and represent a holistic view of the patient's hospital stay, machine learning models are led to learn discriminative multimodal features via global contrasting. Extensive experiments with a real-world EHR dataset demonstrated that our framework outperformed state-of-the-art approaches on the exemplar task of predicting the occurrence of nine postoperative complications for more than 120,000 major inpatient surgeries using multimodal data from UF health system split among three hospitals (UF Health Gainesville, UF Health Jacksonville, and UF Health Jacksonville-North)., Comment: 12 pages, 3 figures. arXiv admin note: text overlap with arXiv:2403.04012

Background: The accurate prediction of postoperative complication risk using Electronic Health Records (EHR) and artificial intelligence shows great potential. Training a robust artificial intelligence model typically requires large-scale and diverse datasets. In reality, collecting medical data often encounters challenges surrounding privacy protection. Methods: This retrospective cohort study includes adult patients who were admitted to UFH Gainesville (GNV) (n = 79,850) and Jacksonville (JAX) (n = 28,636) for any type of inpatient surgical procedure. Using perioperative and intraoperative features, we developed federated learning models to predict nine major postoperative complications (i.e., prolonged intensive care unit stay and mechanical ventilation). We compared federated learning models with local learning models trained on a single site and central learning models trained on pooled dataset from two centers. Results: Our federated learning models achieved the area under the receiver operating characteristics curve (AUROC) values ranged from 0.81 for wound complications to 0.92 for prolonged ICU stay at UFH GNV center. At UFH JAX center, these values ranged from 0.73-0.74 for wound complications to 0.92-0.93 for hospital mortality. Federated learning models achieved comparable AUROC performance to central learning models, except for prolonged ICU stay, where the performance of federated learning models was slightly higher than central learning models at UFH GNV center, but slightly lower at UFH JAX center. In addition, our federated learning model obtained comparable performance to the best local learning model at each center, demonstrating strong generalizability. Conclusion: Federated learning is shown to be a useful tool to train robust and generalizable models from large scale data across multiple institutions where data protection barriers are high., Comment: 57 pages. 2 figures, 3 tables, 2 supplemental figures, 8 supplemental tables

Due to the concerns of the society about the increase of antibiotic resistant infections, many studies and research have been done on nanoparticles and applications of nano-biotechnology. Zirconium Oxide ($\text{ZrO}_{2}$) in which called zirconia, is a white oxide of zirconium metal that its diameter is 20 nm. The colloidal size of these particles is often smaller than bacterial and eukaryotic cells. The main intention of this paper is to investigate the effect of different doses of $\text{ZrO}_{2}$ NPs on the sequences changes for the $\textit{Escherichia coli}$ ($\textit{E. coli}$) genome. At the first step, $\textit{E. coli}$ was cultured in eosin methylene blue agar and brain heart broth (BHB) mediums, respectively. Then, bacteria were treated with $\text{ZrO}_{2}$ NPs at concentrations of 100, 250, and 350 $\mu$g/ml. After treatment, the growth of bacteria was evaluated by utilizing spectrophotometry at 600 nm after incubation times including 2, 4, 6, 8, and 24 hours at 37 $^{\circ}$C. At the second step, the extraction of DNA was performed by using control and treated samples. Then, the changes in the sequence of bacterial genome were investigated using RAPD markers. Finally, NTSYS-PC platform was employed in order to analyze of the results extracted by electrophoresis of products on agarose gel. In this paper, it was observed that $\text{ZrO}_{2}$ NPs can inhibit the growth of bacteria at concentrations of 250 and 350 $\mu$g/ml after 8 hours of treatment. It was also found that the $\text{ZrO}_{2}$ NPs at different concentrations have not changed the genome sequence of $\textit{E. coli}$. Furthermore, it was concluded that the $\text{ZrO}_{2}$ NPs with the concentration of 350 $\mu$g/ml had the highest inhibitory properties without significant changing in the genomic sequence of $\textit{E. coli}$., Comment: 18 Pages, 6 Figures, 6 Tables

Background: Acute kidney injury (AKI) is a clinical syndrome affecting almost one fifth of hospitalized patients, as well as more than half of the patients who are admitted to the intensive care unit (ICU). Stratifying AKI patients into groups based on severity and duration would facilitate more targeted efforts for treating AKI. Methods: In a retrospective, multicenter and longitudinal cohort study of 935,679 patients who were admitted between 2012 and 2020 to health centers included in OneFlorida+ Network, we analyzed the impact of AKI trajectories which are rapidly reversed AKI, persistent AKI with renal recovery, and persistent AKI without renal recovery on patients' clinical outcomes, including hospital, 30-day, 1-year, and 3-year mortality, kidney replacement therapy, new chronic kidney disease (CKD) within 90 days or 1-year of discharge, CKD progression within 1-year of discharge, resource utilization, hospital disposition, and major complications during hospitalization. As analytical approaches, Kaplan-Meier estimators and survival curves, Cox proportional-hazards regression model, logistic regression model, Kruskal-Wallis test, analysis of variance, chi-square, Fisher's exact test were used. Results: Among 2,187,254 encounters, 14% had AKI, of which 63%, 21%, and 16% had Stage 1, 2, and 3, respectively, as the worst AKI stage. Fraction of patients with persistent AKI was 31%. Patients with AKI had worse clinical outcomes and increased resource utilization compared to patients without the condition. One-year mortality was 5 times greater for patients with persistent AKI compared to those without AKI. Conclusions: Persistent AKI was associated with prolonged hospitalization, increased ICU admission and mortality compared to the other groups. This may emphasize the critical need for devising strategies targeting effective management of AKI and prevention of persisting AKI., Comment: 61 pages, 2 tables, 3 figures, 13 supplemental tables, 3 supplemental figures

Assessing acute brain dysfunction (ABD), including delirium and coma in the intensive care unit (ICU), is a critical challenge due to its prevalence and severe implications for patient outcomes. Current diagnostic methods rely on infrequent clinical observations, which can only determine a patient's ABD status after onset. Our research attempts to solve these problems by harnessing Electronic Health Records (EHR) data to develop automated methods for ABD prediction for patients in the ICU. Existing models solely predict a single state (e.g., either delirium or coma), require at least 24 hours of observation data to make predictions, do not dynamically predict fluctuating ABD conditions during ICU stay (typically a one-time prediction), and use small sample size, proprietary single-hospital datasets. Our research fills these gaps in the existing literature by dynamically predicting delirium, coma, and mortality for 12-hour intervals throughout an ICU stay and validating on two public datasets. Our research also introduces the concept of dynamically predicting critical transitions from non-ABD to ABD and between different ABD states in real time, which could be clinically more informative for the hospital staff. We compared the predictive performance of two state-of-the-art neural network models, the MAMBA selective state space model and the Longformer Transformer model. Using the MAMBA model, we achieved a mean area under the receiving operator characteristic curve (AUROC) of 0.95 on outcome prediction of ABD for 12-hour intervals. The model achieves a mean AUROC of 0.79 when predicting transitions between ABD states. Our study uses a curated dataset from the University of Florida Health Shands Hospital for internal validation and two publicly available datasets, MIMIC-IV and eICU, for external validation, demonstrating robustness across ICU stays from 203 hospitals and 140,945 patients., Comment: 22 pages, 8 figures, To be published

Despite the importance of closely monitoring patients in the Intensive Care Unit (ICU), many aspects are still assessed in a limited manner due to the time constraints imposed on healthcare providers. For example, although excessive visitations during rest hours can potentially exacerbate the risk of circadian rhythm disruption and delirium, it is not captured in the ICU. Likewise, while mobility can be an important indicator of recovery or deterioration in ICU patients, it is only captured sporadically or not captured at all. In the past few years, the computer vision field has found application in many domains by reducing the human burden. Using computer vision systems in the ICU can also potentially enable non-existing assessments or enhance the frequency and accuracy of existing assessments while reducing the staff workload. In this study, we leverage a state-of-the-art noninvasive computer vision system based on depth imaging to characterize ICU visitations and patients' mobility. We then examine the relationship between visitation and several patient outcomes, such as pain, acuity, and delirium. We found an association between deteriorating patient acuity and the incidence of delirium with increased visitations. In contrast, self-reported pain, reported using the Defense and Veteran Pain Rating Scale (DVPRS), was correlated with decreased visitations. Our findings highlight the feasibility and potential of using noninvasive autonomous systems to monitor ICU patients.

The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health trajectories using sequential deep learning. However, learning useful representations of EHR data is challenging due to its high dimensionality, sparsity, multimodality, irregular and variable-specific recording frequency, and timestamp duplication when multiple measurements are recorded simultaneously. Although recent efforts to fuse structured EHR and unstructured clinical notes suggest the potential for more accurate prediction of clinical outcomes, less focus has been placed on EHR embedding approaches that directly address temporal EHR challenges by learning time-aware representations from multimodal patient time series. In this paper, we introduce a dynamic embedding and tokenization framework for precise representation of multimodal clinical time series that combines novel methods for encoding time and sequential position with temporal cross-attention. Our embedding and tokenization framework, when integrated into a multitask transformer classifier with sliding window attention, outperformed baseline approaches on the exemplar task of predicting the occurrence of nine postoperative complications of more than 120,000 major inpatient surgeries using multimodal data from three hospitals and two academic health centers in the United States., Comment: ICLR 2024 Workshop on Learning From Time Series for Health. 10 pages, 3 figures

The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between participants and senior researchers on timely and relevant topics for the \ac{ML4H} community. Encouraged by the successful virtual roundtables in the previous year, we organized eleven in-person roundtables and four virtual roundtables at ML4H 2022. The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables. Each roundtable session included invited senior chairs (with substantial experience in the field), junior chairs (responsible for facilitating the discussion), and attendees from diverse backgrounds with interest in the session's topic. Herein we detail the organization process and compile takeaways from these roundtable discussions, including recent advances, applications, and open challenges for each topic. We conclude with a summary and lessons learned across all roundtables. This document serves as a comprehensive review paper, summarizing the recent advancements in machine learning for healthcare as contributed by foremost researchers in the field., Comment: ML4H 2023, Research Roundtables

Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney damage. Methods: This retrospective cohort study includes adult patients admitted to a non-intensive care unit at the University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of Florida Health (UFH) (n = 127,202). We developed and compared deep learning and conventional machine learning models to predict progression to Stage 2 or higher AKI within the next 48 hours. We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model). We internally and externally validated the models on each site and performed subgroup analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3% (n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under the receiver operating curve values (AUROC) for the UFH test cohort ranged between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80, 0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06]) for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen remained the top three features with the highest influence across the models and health centers. Conclusion: Locally developed models displayed marginally reduced discrimination when tested on another institution, while the top set of influencing features remained the same across the models and sites.

Event prediction aims to forecast the time and type of a future event based on a historical event sequence. Despite its significance, several challenges exist, including the irregularity of time intervals between consecutive events, the existence of cycles, periodicity, and multi-scale event interactions, as well as the high computational costs for long event sequences. Existing neural temporal point processes (TPPs) methods do not capture the multi-scale nature of event interactions, which is common in many real-world applications such as clinical event data. To address these issues, we propose the cross-temporal-scale transformer (XTSFormer), designed specifically for irregularly timed event data. Our model comprises two vital components: a novel Feature-based Cycle-aware Time Positional Encoding (FCPE) that adeptly captures the cyclical nature of time, and a hierarchical multi-scale temporal attention mechanism. These scales are determined by a bottom-up clustering algorithm. Extensive experiments on several real-world datasets show that our XTSFormer outperforms several baseline methods in prediction performance.

We study the intermediate tachyon inflation in an anisotropic background. By using the Friedmann equations obtained in the anisotropic geometry, we obtain the slow-roll parameters in the tachyon model. The presence of the anisotropic effects in the slow-roll parameters changes the perturbation parameters in our setup which may change its observational viability. To check this, we perform a numerical analysis and test the results with Planck2018 TT, TE, EE +lowE+lensing+BK14(18)+BAO data. We show that the intermediate anisotropic inflation in some ranges of the anisotropic and intermediate parameters is observationally viable. We also show that the equilateral amplitude of the non-gaussianity in our model is of the order of $10^{-2}-10^{-1}$. By studying the reheating process in our setup, we find that it is possible to have instantaneous reheating in this model. We also find that the temperature during the reheating in our setup is consistent with Big-Bang nucleosynthesis., Comment: 17 pages, 8 figures, 4 tables

We consider an intermediate Dirac-Born-Infeld (DBI) inflationary model in the presence of a minimal measurable length in the theory. We show that, the presence of a minimal measurable length modifies the definitions of the scalar and tensor spectral indices and also other inflation observables. This is due to modification of the momentum and corresponding wave number of the perturbations in the presence of a minimal length. By using the deformed definition of the scalar and tensor spectral indices, we perform numerical analysis on the intermediate DBI inflation model to find some constraints on the deformation parameter. In this regard, we compare our numerical results with both Planck2018 TT, TE, EE +lowE +lensing +BAO+ BK14 and Planck2018 TT, TE,EE +lowE+lensing+BK14 +BAO+LIGO $\&$ Virgo2016 data at the $68\%$ CL and $95\%$ CL. Our numerical study shows that the intermediate DBI inflation model in the presence of a minimal measurable length is observationally viable if the upper bound on the deformation parameter to be considered of the order of $10^{48}$ at $68\%$ CL and $10^{49}$ at $95\%$ CL. This is consistent with the results of other approaches to constrain such a quantity., Comment: 7 pages, 3 figures, 1 table

The field of healthcare has increasingly turned its focus towards Large Language Models (LLMs) due to their remarkable performance. However, their performance in actual clinical applications has been underexplored. Traditional evaluations based on question-answering tasks don't fully capture the nuanced contexts. This gap highlights the need for more in-depth and practical assessments of LLMs in real-world healthcare settings. Objective: We sought to evaluate the performance of LLMs in the complex clinical context of adult critical care medicine using systematic and comprehensible analytic methods, including clinician annotation and adjudication. Methods: We investigated the performance of three general LLMs in understanding and processing real-world clinical notes. Concepts from 150 clinical notes were identified by MetaMap and then labeled by 9 clinicians. Each LLM's proficiency was evaluated by identifying the temporality and negation of these concepts using different prompts for an in-depth analysis. Results: GPT-4 showed overall superior performance compared to other LLMs. In contrast, both GPT-3.5 and text-davinci-003 exhibit enhanced performance when the appropriate prompting strategies are employed. The GPT family models have demonstrated considerable efficiency, evidenced by their cost-effectiveness and time-saving capabilities. Conclusion: A comprehensive qualitative performance evaluation framework for LLMs is developed and operationalized. This framework goes beyond singular performance aspects. With expert annotations, this methodology not only validates LLMs' capabilities in processing complex medical data but also establishes a benchmark for future LLM evaluations across specialized domains.

Acuity assessments are vital in critical care settings to provide timely interventions and fair resource allocation. Traditional acuity scores rely on manual assessments and documentation of physiological states, which can be time-consuming, intermittent, and difficult to use for healthcare providers. Furthermore, such scores do not incorporate granular information such as patients' mobility level, which can indicate recovery or deterioration in the ICU. We hypothesized that existing acuity scores could be potentially improved by employing Artificial Intelligence (AI) techniques in conjunction with Electronic Health Records (EHR) and wearable sensor data. In this study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for developing an AI-driven acuity assessment score. Accelerometry data were collected from 86 patients wearing accelerometers on their wrists in an academic hospital setting. The data was analyzed using five deep neural network models: VGG, ResNet, MobileNet, SqueezeNet, and a custom Transformer network. These models outperformed a rule-based clinical score (SOFA= Sequential Organ Failure Assessment) used as a baseline, particularly regarding the precision, sensitivity, and F1 score. The results showed that while a model relying solely on accelerometer data achieved limited performance (AUC 0.50, Precision 0.61, and F1-score 0.68), including demographic information with the accelerometer data led to a notable enhancement in performance (AUC 0.69, Precision 0.75, and F1-score 0.67). This work shows that the combination of mobility and patient information can successfully differentiate between stable and unstable states in critically ill patients.

The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable. Early detection of deteriorating conditions can result in providing timely interventions and improved survival rates. In this study, we propose APRICOT-M (Acuity Prediction in Intensive Care Unit-Mamba), a 150k-parameter state space-based neural network to predict acuity state, transitions, and the need for life-sustaining therapies in real-time in ICU patients. The model uses data obtained in the prior four hours in the ICU and patient information obtained at admission to predict the acuity outcomes in the next four hours. We validated APRICOT-M externally on data from hospitals not used in development (75,668 patients from 147 hospitals), temporally on data from a period not used in development (12,927 patients from one hospital from 2018-2019), and prospectively on data collected in real-time (215 patients from one hospital from 2021-2023) using three large datasets: the University of Florida Health (UFH) dataset, the electronic ICU Collaborative Research Database (eICU), and the Medical Information Mart for Intensive Care (MIMIC)-IV. The area under the receiver operating characteristic curve (AUROC) of APRICOT-M for mortality (external 0.94-0.95, temporal 0.97-0.98, prospective 0.96-1.00) and acuity (external 0.95-0.95, temporal 0.97-0.97, prospective 0.96-0.96) shows comparable results to state-of-the-art models. Furthermore, APRICOT-M can predict transitions to instability (external 0.81-0.82, temporal 0.77-0.78, prospective 0.68-0.75) and need for life-sustaining therapies, including mechanical ventilation (external 0.82-0.83, temporal 0.87-0.88, prospective 0.67-0.76), and vasopressors (external 0.81-0.82, temporal 0.73-0.75, prospective 0.66-0.74). This tool allows for real-time acuity monitoring in critically ill patients and can help clinicians make timely interventions.