A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
RL platform enhances user journeys in healthcare apps.
Study improves conformal prediction for EEG classification in healthcare, enhancing coverage.
Distributed representations have been used to support downstream tasks in healthcare recently. Healthcare data (e.g., electronic health records) contain multiple modalities of data from heterogeneous sources that can provide complementary information, alongside an added dimension to learning personalized patient repres…
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between visits. Although those wor…
There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow healthcare experts to make reasonable and data-driven decisions to provide…
We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (…
New algorithm for personalized healthcare with privacy guarantees.
Study proposes BFEL framework for privacy-preserving FL in personalized healthcare.
PNNs improve personalized healthcare policies using mixed integer programming.
Proposes a method for generating prediction intervals in dose-response models using conformal prediction.
DeepCoDA provides personalized interpretability for complex health data.
LogGENE uses log-cosh loss for deep learning in gene expression datasets, improving accuracy and interpretability.
Bayesian Supervised Causal Clustering identifies patient subgroups for personalized decision-making.
Study user engagement in mobile health apps for health workers in resource-poor settings.
LMM predicts healthcare costs and risks with improved accuracy.
Study assesses whether RL algorithm personalizes treatment sequences.
A recommendation framework helps users choose healthcare interventions.
Paper addresses data heterogeneity in federated learning for CoxPH models in healthcare.
Neural TPPs improve EHR modelling efficiency.
LUQ-Learning adapts Q-learning for healthcare decisions considering patient preferences.
Unified survey of treatment effect heterogeneity and uplift modeling methods.
A new method models continuous-time counterfactual outcomes using neural controlled differential equations.
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's health gradually getting worse. While recent works involve raw electronic health re…
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit. While the sensitivity of these domains compels us to eval…
Study improves mortality prediction in hospital patients using comprehensive feature engineering.
Social Reinforcement Learning methods, which model agents in large networks, are useful for fake news mitigation, personalized teaching/healthcare, and viral marketing, but it is challenging to incorporate inter-agent dependencies into the models effectively due to network size and sparse interaction data. Previous soc…
RL algorithms with medical integration improve personalized treatment recommendations.
In this paper, we introduce a novel task for machine learning in healthcare, namely personalized modeling of the female hormonal cycle. The motivation for this work is to model the hormonal cycle and predict its phases in time, both for healthy individuals and for those with disorders of the reproductive system. Becaus…
PhysioMTL learns personalized HRV rhythms from wearable data, improving prediction and counterfactual analysis.
Paper proposes federated offline RL for personalized medicine.
We propose a flexible method for estimating value functions in reinforcement learning without parametric assumptions.
A new method synthesizes expressions from characteristics using GAN for healthcare.
The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits, and clinical history. This data is then used by statistical models to predict personalized treatments. However, due to pri…
In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (…
Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several a…
Algorithm generates private continuous-time data for sensitive domains.
LI-ITR combines flexible ML with interpretable approximations for personalized treatment rules.
Method constructs prediction intervals for time-varying individual treatment effects.
IntelligentPooling improves treatment decisions in mHealth.
We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models…
Due to escalating healthcare costs, accurately predicting which patients will incur high costs is an important task for payers and providers of healthcare. High-cost claimants (HiCCs) are patients who have annual costs above $\$250,000$ and who represent just 0.16% of the insured population but currently account for 9%…
A variety of methods existing for generating synthetic electronic health records (EHRs), but they are not capable of generating unstructured text, like emergency department (ED) chief complaints, history of present illness or progress notes. Here, we use the encoder-decoder model, a deep learning algorithm that feature…
New method improves human mesh recovery for obese people.
The goal of personalized decision making is to map a unit's characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pat…
Automated prediction of valence, one key feature of a person's emotional state, from individuals' personal narratives may provide crucial information for mental healthcare (e.g. early diagnosis of mental diseases, supervision of disease course, etc.). In the Interspeech 2018 ComParE Self-Assessed Affect challenge, the …
HOLMES improves real-time model serving for ICU patients, balancing accuracy and speed.
Always-on sensing of mobile device user's contextual information is critical to many intelligent use cases nowadays such as healthcare, drive assistance, voice UI. State-of-the-art approaches for predicting user context have proved the value to leverage multiple sensing modalities for better accuracy. However, those co…
Type 2 diabetes mellitus (T2DM) is a chronic disease that often results in multiple complications. Risk prediction and profiling of T2DM complications is critical for healthcare professionals to design personalized treatment plans for patients in diabetes care for improved outcomes. In this paper, we study the risk of …