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--- projects:workgroups:causal_inference [2019/11/24 02:29] – abbas
+++ projects:workgroups:causal_inference [2024/07/28 12:34] (current) – abbas
@@ Line 13: / Line 13: @@
-**Coordinator:**
+** 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]]
@@ Line 21: / Line 22: @@
 ==== Causal Inference Using Composition of Transactions (CICT)  ====
-**Project Lead:** [[https://www.linkedin.com/in/ashojaee/|Abbas Shojaee]]
-CICT is a novel computational method that uses large-scale health data to predict potential causal relationships  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.
+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.
+*** Publications and conference papers: ***
+[[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]]
+[[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 ]]
+[[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|]]
+[[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]]
-*** Participants: ***
-  * Alireza Aani
-  * Yalda Aryan
-  * Menelaos Konstantinidis
 **Repository:**