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.
CRN model estimates treatment effects over time using adversarial balancing.
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. …
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 …
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…
RL algorithms with medical integration improve personalized treatment recommendations.
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…
New method combines multiple datasets to estimate ATE with valid confidence intervals.
Deep Bayesian models estimate causal effects for dynamic treatment regimes over long follow-up times.
This paper tackles data-efficient CEE with scarce labelled data, proposing a method to progressively reduce generalization risk.
A new method flips class values to address class and treatment imbalance in uplift modeling and HTE.
Three approaches learn personalized treatment policies for UTI patients.
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
Machine learning has shown much promise in helping improve the quality of medical, legal, and financial decision-making. In these applications, machine learning models must satisfy two important criteria: (i) they must be causal, since the goal is typically to predict individual treatment effects, and (ii) they must be…
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…
Novel method to quantify aleatoric uncertainty of treatment effects from observational data.
Framework assesses treatment effects by risk groups in observational studies.
CAST models time-varying treatment effects in cancer patients.
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action to take for a target unit after observing its covariates and predicted outcom…
Efficiently find near-optimal medical treatments with less trial and error.
New method estimates treatment effects from high dimensional data.
Proposes DeepSDRF for continuous treatment recommendation from clinical survival data.
Unified framework for counterfactual survival analysis improves treatment effect estimation.
Modeling disease progression using irregular time intervals in EHRs.
With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with var…
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…
This paper investigates robust and efficient DR/RDR estimators for WATEs.
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…
The aim of clinical effectiveness research using repositories of electronic health records is to identify what health interventions 'work best' in real-world settings. Since there are several reasons why the net benefit of intervention may differ across patients, current comparative effectiveness literature focuses on …
Study on how imprecise medical data affects predictions in hyperthyroidism.
Develops a new RL algorithm for medical treatment regimes.
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…
Proposes DSW for unbiased ITE estimation with dynamic confounders.
Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.
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 uses machine learning to analyze patient survey data for Lyme disease.
Smart bin monitors predict medication adherence with high accuracy.
Proposes an interpretable machine learning framework for multi-arm HTE estimation.
New method estimates treatment effects over time with unobserved confounders.
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…
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…
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
Model predicts VKA dosage for Indian patients, aiding in safe medication.
A new DML method for continuous treatments uncovers causal mediation effects.
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…
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…
Bayesian CNN estimates uncertainty in COVID-19 detection.
Novel strategy benchmarks observational studies against randomized trials.
The paper estimates personalized treatment effects in medical settings with competing risks.