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--- projects:workgroups:causal_inference [2019/11/24 01:16] – abbas
+++ projects:workgroups:causal_inference [2024/07/28 12:34] (current) – abbas
@@ Line 8: / Line 8: @@
 Large-scale observational data offers a new window for verifying our existing causal understandings and for inferring new causal relationships at a fast pace. We are working to rethink the existing frameworks of causal inference in health sciences by introducing ideas from other scientific disciplines and by inventing new concepts and analytical methods.
-  * Please add your name in the following if you are interested in joining.
+  * If you are working in causal inference add your project in the following. Also, you can find projects to collaborate or join to stay informed.
-  * If you have an ongoing project in causal inference in observational biomedical/ health data, please add the project name/link and a two-line description to the projects section
   * If you have an idea on causal inference that you want to test it or even develop it, you probably will find collaborators here
-**Project Lead(s):**
+** Project Leads:**
 * [[https://www.linkedin.com/in/ashojaee/|Abbas Shojaee]]
+ * [[https://www.linkedin.com/in/yalda-aryan/?original_referer=https%3A%2F%2Fwww%2Egoogle%2Ecom%2F&originalSubdomain=ir|Yalda Aryan]]
-** Participants: **
+===== Ongoing Projects =====
+==== Causal Inference Using Composition of Transactions (CICT)  ====
+CICT is a novel computational method that uses large-scale health data to predict potential causal relationships between clinical conditions. A pipeline for epidemiological etiological inference is also developed to validate the results of CICT. CICT and the validation pipeline have been used by different teams to identify latent risk factors and unknown effects of clinical conditions or procedures, which resulted in reporting of multiple novel findings during 2018-2019. Two large-scale population-level claims datasets from HCUP California and Florida have been used for discovery and validation. OHDSI will be used to empower the engine for the new phase.
+**CICT pipeline can produce highly accurate novel hypotheses and analyses results at a fast pace. ** Accordingly, we are now open to collaboration requests from clinical researchers who are seeking novel hypotheses from data and/or epidemiological evidence from population-level data. We are also eager to start ** collaborations ** with individuals or organizations that can provide us access to large-scale claims/EMR datasets in OHDSI, based on their existing access.
-  * Alireza Aani
+*** Publications and conference papers: ***
-  * Yalda Aryan
-  * Menelaos Konstantinidis
+[[https://www.medrxiv.org/content/10.1101/2021.03.08.21252412v1|Viral Pneumonia is Associated with Increased Risk and Earlier Development of Post-Inflammatory Pulmonary Fibrosis]]
+[[https://academic.oup.com/bib/article/24/6/bbad370/7330999|Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions]]
+[[https://www.biorxiv.org/content/10.1101/439117v1|Asthma-Neoplasms Relationships: New Insights Using Machine Inference, Epidemiological Reasoning, And Big Data]]
+[[https://www.atsjournals.org/doi/pdf/10.1164/ajrccm-conference.2018.197.1_MeetingAbstracts.A5081|The Associations of Invasive Procedures and Subsequent Psychiatric Diagnoses]]
-** Ongoing Projects **
+[[https://www.atsjournals.org/doi/abs/10.1164/ajrccm-conference.2018.197.1_MeetingAbstracts.A7694 |Obstructive Sleep Apnea Increases the Risk of Diastolic Heart Failure: Results of a Large Population Study ]]
-   * Causal Inference Using Composition of Transactions (CICT)
+[[https://www.abstractsonline.com/pp8/#!/5789/presentation/11491|Viral Pneumonia Is an Independent Risk Factor for Pulmonary Fibrosis: Results of Large-Scale Longitudinal Population-Level Data|]]
-CICT is a novel computational method that uses large-scale health data to predict causality between clinical conditions, genes, proteins and other interacting factors. A pipeline for epidemiological etiological inference is also developed to validate the results of CICT. CICT and the validation pipeline have been used by different teams to identify latent risk factor and unknown effects of clinical conditions or procedures, which resulted in reporting of multiple novel findings during 2018-2019. Two large-scale population-level claims datasets from HCUP California and Florida have been used for discovery and validation. OHDSI will be used to empower the engine for the new phase.
+[[https://www.abstractsonline.com/pp8/#!/5789/presentation/22530|Platelet Transfusion Is an Independent Risk Factor for Idiopathic Pulmonary Fibrosis: Results of Large-Scale Longitudinal Population-Level Data]]