We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control. Empowerment, being the channel capacity between actions and states, maximises the influence of an agent on its near future. It has been sho…
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48 results for “patient empowerment”
Study on women entrepreneurs' access to finance in France.
problem Inequalities in accessing external finance for women entrepreneurs in France.
method Quantitative approach using data from a representative sample of women entrepreneurs.
result Founder status affects access to external finance; increases success in fundraising but reduces bank finance.
Universal AI seeks high-optionality states through empowerment and curiosity.
problem Understanding and optimizing AI behavior in uncertain environments.
method Unified framework combining AIXI and variational empowerment, showing how universal AI agents balance goal-directed behavior with uncertainty reduction curiosity.
result Self-AIXI asymptotically converges to AIXI performance and exhibits power-seeking behavior due to intrinsic motivations.
Exploration is a difficult challenge in reinforcement learning and is of prime importance in sparse reward environments. However, many of the state of the art deep reinforcement learning algorithms, that rely on epsilon-greedy, fail on these environments. In such cases, empowerment can serve as an intrinsic reward sign…
New method calculates empowerment from visual data, solving complex reinforcement learning problems.
problem Calculating empowerment in unknown dynamics from visual observation is challenging.
method Developed a novel approach using stochastic dynamic models in latent space and the Water-Filling algorithm.
result Efficiently computed empowerment in unknown dynamics from visual observation only.
Empowerment is an information-theoretic method that can be used to intrinsically motivate learning agents. It attempts to maximize an agent's control over the environment by encouraging visiting states with a large number of reachable next states. Empowered learning has been shown to lead to complex behaviors, without …
Efficient Empowermentstat.ML
Empowerment quantifies the influence an agent has on its environment. This is formally achieved by the maximum of the expected KL-divergence between the distribution of the successor state conditioned on a specific action and a distribution where the actions are marginalised out. This is a natural candidate for an intr…
We consider a problem of learning the reward and policy from expert examples under unknown dynamics. Our proposed method builds on the framework of generative adversarial networks and introduces the empowerment-regularized maximum-entropy inverse reinforcement learning to learn near-optimal rewards and policies. Empowe…
Automated model assesses online health info quality using machine learning.
problem Low quality health information on the internet poses risks to patients.
method Used machine learning models, specifically hierarchical encoder attention-based neural networks (HEA) with BERT and BioBERT embeddings.
result HEA models outperform traditional models in evaluating health info quality.
EDL discovers state-covering skills without relying on task rewards.
problem Discovering skills in reinforcement learning without a task-oriented reward function.
method EDL optimizes information-theoretic objective using different machinery to address coverage problem.
result EDL discovers state-covering skills more effectively than existing methods.
The mutual information is a core statistical quantity that has applications in all areas of machine learning, whether this is in training of density models over multiple data modalities, in maximising the efficiency of noisy transmission channels, or when learning behaviour policies for exploration by artificial agents…
New approach categorizes objective functions for embodied agents.
problem Understanding how objectives relate to each other and discovering new objectives.
method Introducing Action Perception Divergence (APD) to categorize objective functions.
result Introduces a spectrum of objectives from narrow to general, explaining various unsupervised objectives.
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability. We utilize the variational intrinsic control framework (Gregor et.al., 2016) which maximizes empowerment -- the ability to reliably reach a diverse set of states and show how to ide…
CityTFT models urban building energy using a data-driven approach.
problem Current UBEM methods are time-consuming and based on physics.
method CityTFT uses a TFT framework with an augmented loss function.
result CityTFT predicts energy demands with high accuracy.
Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments. The information-theoretic principle of empowerment formalizes an unsupervised exploration objective through an agent trying to maximize its influence on the future states of its environme…
Revisits VIC method to correct intrinsic reward bias in stochastic environments.
problem Intrinsic reward bias in VIC leading to suboptimal solutions.
method Proposes two methods based on transitional probability model and Gaussian mixture model to correct bias.
result Achieves maximal empowerment through corrected intrinsic reward.
Improved forecasting of suicide attempts using LSGPs for patients with little data.
problem Challenges in predicting suicide attempts due to their rarity and patient heterogeneity.
method Introduced Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity.
result LSGPs outperform baseline models, even without kernel-design, and offer new insights into patient similarity.
Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.
problem Improving seizure detection accuracy for wearable devices.
method Transfer learning with tensor kernel machine using canonical polyadic decomposition.
result Patient fine-tuned model achieves high performance with smaller model size.
Personalized models explain TB treatment outcomes considering patient context.
problem Heterogeneity in TB treatment outcomes due to co-morbidities.
method Multi-task learning approach encoding patient context into personalized models.
result Identifies anemia, age of onset, and HIV as influential for treatment efficacy.
Simulates patient pathways to detect delayed rare disease diagnoses.
problem Delayed rare disease diagnoses in France, causing health system and patient harm.
method Probabilistic modelling of patient pathways to create an alert system.
result Alert system detects and refers wandering patients to CRMRs.
Model predicts wound and episode-level readmission risk and time to re-admit.
problem Identify patients at high risk of re-admission to prevent wound recurrences and reduce healthcare costs.
method Data-driven analysis of wound care and episode-level patient data.
result Model achieves high recall and precision for predicting re-admission risk and time.
Study evaluates digital transformation impact on financial performance using LLMs.
problem Measuring and understanding the impact of digital transformation on financial performance.
method Constructed DT indicators from company reports; analyzed effects of different digital technologies.
result Digital transformation improves financial performance, but varies by technology.
Currently, approximately 30% of epileptic patients treated with antiepileptic drugs (AEDs) remain resistant to treatment (known as refractory patients). This project seeks to understand the underlying similarities in refractory patients vs. other epileptic patients, identify features contributing to drug resistance acr…
Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood f…
The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers includes that of enrolling patients into care programs and keeping them sufficiently engaged in the program, so that patients can attain various goals. The attainmen…
System detects multiple patients' behaviors in real-time using mmWave radar and CNN.
problem Real-time patient behavior monitoring in hospitals.
method Used mmWave radar for tracking and collecting Doppler patterns. Created a three-layer CNN model for behavior classification.
result System achieved very good inference accuracy in predicting patient behaviors in real-time.
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scaleq-bio.QM
Deep learning model creates patient representations for scalable EHR-based stratification.
problem Challenges in summarizing and representing patient data from EHRs prevent scalable stratification analysis.
method Unsupervised framework based on deep learning (ConvAE) using word embeddings, CNNs, and autoencoders.
result ConvAE significantly outperformed baselines in clustering diverse patient cohorts, identifying clinically relevant subtypes.
Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis diagnostic criteria fail to account for significant underlying heterogeneity, both between patients as well as over time in a single patient. W…
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Expertscs.LG
We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims t…
Longitudinal patient data has the potential to improve clinical risk stratification models for disease. However, chronic diseases that progress slowly over time are often heterogeneous in their clinical presentation. Patients may progress through disease stages at varying rates. This leads to pathophysiological misalig…
Study automates detection of visitation disruptions in ICU patients.
problem Difficulty in detecting frequent visitation disruptions in ICU patients.
method Used DensePose R-CNN model to count people in video frames, analyzed disruptions and patient outcomes.
result Automated method detects visitation disruptions, impacts on pain and length of stay examined.
Bayesian methods improve group testing for identifying infected patients.
problem Identifying infected patients from group testing results with false positives.
method Bayesian inference and belief propagation algorithm, combined with expectation-maximization method.
result True-positive rate improved by considering credible intervals.
In Vitro Fertilization (IVF) Cumulative Pregnancy Rate Prediction from Basic Patient Characteristicsq-bio.QM
Paper predicts IVF pregnancy rates from basic patient info.
problem Predicting IVF pregnancy rates from patient characteristics.
method Clustering patients into groups, then SVM models for each group.
result Support vector machine models achieve best overall performance.
Model identifies key problems in HIV patients' records.
problem Complex and time-consuming task of identifying patient problems from electronic health records.
method Unsupervised phenotyping approach that jointly learns phenotypes from structured and unstructured data.
result Learned phenotypes and their relatedness are clinically valid and surpass existing methods.
Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity resea…
Study develops electronic phenotypes of ICU patient acuity.
problem Limited time for patient acuity assessments and imprecise clinical trajectory prediction.
method Developed electronic phenotypes using automated variable retrieval in electronic health records.
result Identified three phenotypes: persistently stable, persistently unstable, and transitioning from unstable to stable.
Deep learning clusters patient time-series data for better prognosis.
problem Clustering time-series data for patient phenotyping and prognosis.
method Deep predictive clustering with novel loss functions for future outcome distribution.
result Model achieves superior clustering performance and identifies meaningful patient subgroups.
Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric framework that has proven useful in jointly modeling the physiological states of patients in medical time series data. However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological state remains chall…
Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that both the number of cancer patients admitted to the ICU and overall survival have increased since the millennium change. MIMIC-III, a freely accessible critical care data…
Machine learning detects NASH patients from medical claims data.
problem Detecting undiagnosed NASH patients for screening and management.
method Gradient-boosted decision trees trained on administrative medical claims data.
result Model precision for NASH detection is significantly higher than NASH incidence.
Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare. However, the common relations among patients (e.g., share the same doctor) were rarely considered. In this paper, we represent patients and clinicians relati…
The paper tracks patient recovery using graphs of joint movement data.
problem Tracking individual patient recovery trajectories in physical therapy.
method Bayesian learning of Random Geometric Graphs from joint movement data.
result Optimal exercise routines can be recommended based on patient recovery data.
Modelling of Sickle Cell Anemia Patients Response to Hydroxyurea using Artificial Neural Networksq-bio.QM
Model predicts HU response for sickle cell patients.
problem Predicting which sickle cell patients will respond to Hydroxyurea.
method Developed Deep Artificial Neural Network models.
result 92.6% accuracy in predicting HbF response.
Enhances understanding of patient healthcare journeys using self-attention.
problem Capturing hidden dependencies in multi-level patient journey data.
method Proposes a multi-level self-attention network (MusaNet) for encoding patient journeys.
result MusaNet produces higher-quality representations than state-of-the-art methods.
Paper proposes a new method for more accurate group testing of infected patients.
problem Identifying infected patients efficiently with reduced tests and corrected errors.
method Adaptive design of pools based on Bayesian posterior prediction using belief propagation algorithm.
result The proposed method results in more accurate identification of infected patients.
New model identifies patient-specific disease root causes.
problem Identifying root causes of complex diseases varying between patients.
method Generalized Root Causal Inference (GRCI) algorithm for heteroscedastic noise model.
result GRCI accurately extracts patient-specific root causes.
Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis. To improve those patients' outcome, rare disease detection is an important task for identifying patients with rare conditions based on longitudinal medical claims. In this paper, we present a deep learning m…
Deep learning model predicts severe COVID-19 outcomes.
problem Predicting severe COVID-19 outcomes in ED patients.
method Deep feature fusion model using EHR data and CXR images.
result CO-RISK score achieved AUC of 0.95 and 0.92 for 24 and 72 hours predictions, superior to human performance.