User Tools

Site Tools


projects:workgroups:causal_inference

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
Last revision Both sides next revision
projects:workgroups:causal_inference [2019/11/24 01:16]
abbas
projects:workgroups:causal_inference [2020/01/01 02:36]
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. 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   * 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 Lead:**
  
 * [[https://​www.linkedin.com/​in/​ashojaee/​|Abbas Shojaee]] * [[https://​www.linkedin.com/​in/​ashojaee/​|Abbas Shojaee]]
   ​   ​
  
-** 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. ​
  
-  ​Alireza Aani +**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. ​
-  ​Yalda Aryan +
-  ​Menelaos Konstantinidis+
  
 +*** Publications and conference papers: ***
 +[[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|]]
  
-** Ongoing Projects ** +[[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]]
- +
-   * Causal Inference Using Composition of Transactions (CICT)  +
- +
-CICT is a novel computational method that uses large-scale health data to predict causality between clinical conditions, genes, proteins and other interacting factorsA 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. ​+
  
 +*** Participants:​ ***
  
 +  * Alireza Aani
 +  * Yalda Aryan
 +  * Menelaos Konstantinidis
  
 **Repository:​** ​ **Repository:​** ​
projects/workgroups/causal_inference.txt · Last modified: 2020/01/01 02:36 by abbas