Objective: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. Methods: We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. Results: The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). Conclusions and Significance: These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.

Introduction: Developing reliable medication dosing guidelines is challenging because individual dose–response relationships are mitigated by both static (e. g., demographic) and dynamic factors (e.g., kidney function). In recent years, several data-driven medication dosing models have been proposed for sedatives, but these approaches have been limited in their ability to assess interindividual differences and compute individualized doses.Objective: The primary objective of this study is to develop an individualized framework for sedative–hypnotics dosing.Method: Using publicly available data (1,757 patients) from the MIMIC IV intensive care unit database, we developed a sedation management agent using deep reinforcement learning. More specifically, we modeled the sedative dosing problem as a Markov Decision Process and developed an RL agent based on a deep deterministic policy gradient approach with a prioritized experience replay buffer to find the optimal policy. We assessed our method's ability to jointly learn an optimal personalized policy for propofol and fentanyl, which are among commonly prescribed sedative–hypnotics for intensive care unit sedation. We compared our model's medication performance against the recorded behavior of clinicians on unseen data.Results: Experimental results demonstrate that our proposed model would assist clinicians in making the right decision based on patients' evolving clinical phenotype. The RL agent was 8% better at managing sedation and 26% better at managing mean arterial compared to the clinicians' policy; a two-sample t-test validated that these performance improvements were statistically significant (p < 0.05).Conclusion: The results validate that our model had better performance in maintaining control variables within their target range, thereby jointly maintaining patients' health conditions and managing their sedation.

Credit risk analysis is a process that involves a wide range of complex cognitive abilities. Automating the credit risk analysis process using Large Language Models can bring transformative changes to the finance industry, but not without appropriate measures to ensure trustworthy responses. In this work, we propose a novel prompt-engineering method that enhances the ability of Large Language Models to generate reliable credit risk reports - Labeled Guide Prompting (LGP). LGP consists of: (1) providing annotated few-shot examples to the LLM that denote sets of tokens in an exemplary prompt that are of greater importance when generating sets of tokens in the exemplary response and (2) providing text in the prompt that describes the direction, pathways and interactions between variables from a Bayesian network used for credit risk assessment, thus promoting abductive reasoning. Using data from 100 credit applications, we demonstrate that LGP enables LLMs to generate credit risk reports that are preferred by human credit analysts (in 60-90% of cases) over alternative credit risk reports created by their peers in a blind review. Additionally, we found a statistically significant improvement (p-value < 10− 10) in the insightfulness of the responses generated using LGP when compared to identical prompts without LGP components. We conclude that Labeled Guide Prompting can enhance LLM performance in complex problem-solving tasks, achieving a level of competency comparable to or exceeding human experts.

In this paper, we present MambaNet: a hybrid neural network for predicting the outcomes of Basketball games. Contrary to other studies, which focus primarily on season games, this study investigates playoff games. MambaNet is a hybrid neural network architecture that processes a time series of teams’ and players’ game statistics and generates the probability of a team winning or losing an NBA playoff match. In our approach, we utilize Feature Imitating Networks to provide latent signal-processing feature representations of game statistics to further process with convolutional, recurrent, and dense neural layers. Three experiments using five different datasets are conducted to evaluate the performance and generalizability of our architecture against a wide range of previous studies. Our final method successfully predicted the AUC from 0.72 to 0.82, beating the best-performing baseline models by a considerable margin.

Background and Objectives: Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury following cardiac arrest. We aimed to delineate the evolution of coma neurophysiology features ensembles associated with recovery from coma after cardiac arrest. Methods: Adult subjects in acute coma following cardiac arrest were included in a retrospective database involving seven hospitals. The combination of three quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon’s entropy [En]) was used to define five distinct neurophysiology states: Epileptiform High Entropy (EHE: SpF ≥4/minute and En ≥5); Epileptiform and Low Entropy (ELE: SpF ≥4/minute and <5 En); Non-Epileptiform High Entropy (NEHE: SpF <4/minute and ≥5 En); Non-Epileptiform Low Entropy (NELE: SpF<4/minute and <5 En), and Burst Suppression (BSup≥50% and SpF <4/minute). State transitions were measured at consecutive 6h blocks between 6-84h after return of spontaneous circulation. Good neurological outcome was defined as best Cerebral Performance Category (CPC) 1-2 at 3-6 months. Results: One-thousand thirty-eight subjects were included (50,224 hours of EEG), and 373 (36%) had good outcome. Subjects with EHE state had a 29% rate of good outcome while those with ELE had 11%. Transitions out of an EHE or BSup to a NEHE were associated with good outcome (45% and 20%, respectively). No subjects with ELE state lasting >15h had good recovery. Discussion: Transition to high entropy states is associated with increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.

Objective To determine cost-effectiveness parameters for EEG monitoring in cardiac arrest prognostication. Methods We conducted a cost-effectiveness analysis to estimate the cost per quality-adjusted life-year (QALY) gained by adding continuous EEG monitoring to standard cardiac arrest prognostication using the American Academy of Neurology Practice Parameter (AANPP) decision algorithm: neurologic examination, somatosensory evoked potentials, and neuron-specific enolase. We explored lifetime cost-effectiveness in a closed system that incorporates revenue back into the medical system (return) from payers who survive a cardiac arrest with good outcome and contribute to the health system during the remaining years of life. Good outcome was defined as a Cerebral Performance Category (CPC) score of 1–2 and poor outcome as CPC of 3–5. Results An improvement in specificity for poor outcome prediction of 4.2% would be sufficient to make continuous EEG monitoring cost-effective (baseline AANPP specificity = 83.9%). In sensitivity analysis, the effect of increased sensitivity on the cost-effectiveness of EEG depends on the utility (u) assigned to a poor outcome. For patients who regard surviving with a poor outcome (CPC 3–4) worse than death (u = −0.34), an increased sensitivity for poor outcome prediction of 13.8% would make AANPP + EEG monitoring cost-effective (baseline AANPP sensitivity = 76.3%). In the closed system, an improvement in sensitivity of 1.8% together with an improvement in specificity of 3% was sufficient to make AANPP + EEG monitoring cost-effective, assuming lifetime return of 50% (USD $70,687). Conclusion Incorporating continuous EEG monitoring into cardiac arrest prognostication is cost-effective if relatively small improvements in sensitivity and specificity are achieved.