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 algorithms with medical integration improve personalized treatment recommendations.
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. …
Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.
CRN model estimates treatment effects over time using adversarial balancing.
Machine-assisted treatment recommendations hold a promise to reduce physician time and decision errors. We formulate the task as a sequence-to-sequence prediction model that takes the entire time-ordered medical history as input, and predicts a sequence of future clinical procedures and medications. It is built on the …
Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continuous state-space mode…
Efficiently find near-optimal medical treatments with less trial and error.
Modeling disease progression using irregular time intervals in EHRs.
When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials. Stanford Health Care alone has millions of electronic medical records (EMRs) that are only just recently being leveraged to i…
Develops a new RL algorithm for medical treatment regimes.
Proposes DeepSDRF for continuous treatment recommendation from clinical survival data.
Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or pri…
Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations …
New method estimates treatment effects from high dimensional data.
Three approaches learn personalized treatment policies for UTI patients.
New method for finding optimal treatment regimes in medical settings with time-varying unobserved factors.
Novel method to quantify aleatoric uncertainty of treatment effects from observational data.
This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexit…
CAST models time-varying treatment effects in cancer patients.
Deep Bayesian models estimate causal effects for dynamic treatment regimes over long follow-up times.
This paper addresses privacy in federated learning for medical imaging by estimating model uncertainty.
It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. More…
Adherence can be defined as "the extent to which patients take their medications as prescribed by their healthcare providers"[Osterberg and Blaschke, 2005]. World Health Organization's reports point out that, in developed countries, only about 50% of patients with chronic diseases correctly follow their treatments. Thi…
Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated…
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
This paper investigates robust and efficient DR/RDR estimators for WATEs.
Given two possible treatments, there may exist subgroups who benefit greater from one treatment than the other. This problem is relevant to the field of marketing, where treatments may correspond to different ways of selling a product. It is similarly relevant to the field of public policy, where treatments may corresp…
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, pr…
Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many vis…
The paper proposes an interpretable off-policy learning algorithm for medical treatments.
Smart bin monitors predict medication adherence with high accuracy.
This paper tackles data-efficient CEE with scarce labelled data, proposing a method to progressively reduce generalization risk.
Developed a new algorithm to improve dynamic treatment regimens.
New method combines multiple datasets to estimate ATE with valid confidence intervals.
Method learns optimal treatment sequences from observational data.
Study on how imprecise medical data affects predictions in hyperthyroidism.
New method estimates treatment effects over time with unobserved confounders.
Optimal treatment regimes (OTR) are individualised treatment assignment strategies that identify a medical treatment as optimal given all background information available on the individual. We discuss Bayes optimal treatment regimes estimated using a loss function defined on the bivariate distribution of dichotomous po…
Today, treatment effect estimation at the individual level is a vital problem in many areas of science and business. For example, in marketing, estimates of the treatment effect are used to select the most efficient promo-mechanics; in medicine, individual treatment effects are used to determine the optimal dose of med…
Bayesian CNN estimates uncertainty in COVID-19 detection.
Opioid overdose rates have reached an epidemic level and state-level policy innovations have followed suit in an effort to prevent overdose deaths. State-level drug law is a set of policies that may reinforce or undermine each other, and analysts have a limited set of tools for handling the policy collinearity using st…
A new DML method for continuous treatments uncovers causal mediation effects.
A treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals. Patient responses are dichotomous and can be predicted through an unknown relationship that depends on the patient information …
The paper estimates personalized treatment effects in medical settings with competing risks.
New method uses probabilistic independence to discover disease signatures from medical records.
Novel strategy benchmarks observational studies against randomized trials.
We study the problem of learning to choose from m discrete treatment options (e.g., news item or medical drug) the one with best causal effect for a particular instance (e.g., user or patient) where the training data consists of passive observations of covariates, treatment, and the outcome of the treatment. The standa…
Framework improves health by planning actionable treatment processes.