Md Osman Gani

Riddhiman Adib, Md Mobasshir Arshed Naved, Chih-Hao Fang et al.

Structural causal models (SCMs) provide a principled approach to identifying causation from observational and experimental data in disciplines ranging from economics to medicine. However, SCMs, which is typically represented as graphical models, cannot rely only on data, rather require support of domain knowledge. A key challenge in this context is the absence of a methodological framework for encoding priors (background knowledge) into causal models in a systematic manner. We propose an abstraction called causal knowledge hierarchy (CKH) for encoding priors into causal models. Our approach is based on the foundation of "levels of evidence" in medicine, with a focus on confidence in causal information. Using CKH, we present a methodological framework for encoding causal priors from various information sources and combining them to derive an SCM. We evaluate our approach on a simulated dataset and demonstrate overall performance compared to the ground truth causal model with sensitivity analysis.

Riddhiman Adib, Md Osman Gani, Sheikh Iqbal Ahamed et al.

Delirium occurs in about 80% cases in the Intensive Care Unit (ICU) and is associated with a longer hospital stay, increased mortality and other related issues. Delirium does not have any biomarker-based diagnosis and is commonly treated with antipsychotic drugs (APD). However, multiple studies have shown controversy over the efficacy or safety of APD in treating delirium. Since randomized controlled trials (RCT) are costly and time-expensive, we aim to approach the research question of the efficacy of APD in the treatment of delirium using retrospective cohort analysis. We plan to use the Causal inference framework to look for the underlying causal structure model, leveraging the availability of large observational data on ICU patients. To explore safety outcomes associated with APD, we aim to build a causal model for delirium in the ICU using large observational data sets connecting various covariates correlated with delirium. We utilized the MIMIC III database, an extensive electronic health records (EHR) dataset with 53,423 distinct hospital admissions. Our null hypothesis is: there is no significant difference in outcomes for delirium patients under different drug-group in the ICU. Through our exploratory, machine learning based and causal analysis, we had findings such as: mean length-of-stay and max length-of-stay is higher for patients in Haloperidol drug group, and haloperidol group has a higher rate of death in a year compared to other two-groups. Our generated causal model explicitly shows the functional relationships between different covariates. For future work, we plan to do time-varying analysis on the dataset.