MIH'17: The First Workshop on Medical Informatics and Healthcare Nova Scotia, Canada, August 14, 2017 |
Conference website | http://datasys.cs.iit.edu/events/MIH17/index.html |
Submission link | https://easychair.org/conferences/?conf=mih17 |
Abstract registration deadline | May 26, 2017 |
Submission deadline | May 31, 2017 |
This workshop is on medical data mining to improve healthcare. It aims to provide a forum for data miners, informaticians, data scientists, and clinical researchers to share their latest investigations in applying data mining techniques to healthcare data residing in electronic health records (EHR). The increasing availability of large and complex medical data sets to the research community triggers the need to develop more advanced and sophisticated big data analytical techniques to exploit and manage these big data. The broader context of the workshop comprehends artificial intelligence, information retrieval, machine learning, natural language processing. Submissions are invited to address the need for developing new methods to mine, summarize and integrate the huge volume and diverse modalities of the structured and unstructured biomedical and healthcare data that can potentially lead to significant advances in the field. Accepted papers will be published in the Proceedings of Machine Learning Research (PMLR) and will be posted on the workshop website. We plan to organize a journal special issue and invite extended versions of the accepted papers for submissions.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference.
The maximum number of pages is eight. All accepted submissions need to follow the SIGKDD Explorations format as shown in the template: template.doc. For submitting your papers, please click on the following link:
List of Topics
Full papers have can address the following topics:
- Clustering big data in EHRs to identify patients with similar disease/symptom/treatment
- Building predictive models for diseases from big data in the EHR.
- Generating lexicons/vocabularies of diseases of interest using deep learning algorithms
- Establishing patients’ cohorts with targeted diseases using information retrieval techniques
- Discovering risk factors of diseases using natural language processing methods
- Longitudinal analysis of temporal data in EHRs
- EHR summarization
- Topic modeling / detection in large amounts of clinical text data
- Integrating structured (tabulated) and unstructured (text narratives) data in the EHR.
- Developing efficient computational algorithms for mining/analyzing big EHR data
- Novel visualization techniques to facilitate the query and analysis of clinical data
- Statistics and probability in large-scale EHR data mining
- Medical image data mining
- Pharmacogenomics data mining
- Data preprocessing and cleansing to deal with noise and missing data in the EHR.
- Developing decision support approaches (especially with uncertain data) in the EHR
- Multi-view learning of the heterogeneous EHR
Committees
Program Committee
- Hamidreza Chitsaz, Colorado State University, chitsaz@chitsazlab.org
- Adam Gaweda, University of Louisville, adam.gaweda@louisville.edu
- Rosa Figueroa, University of Utah, rosfigue@gmail.com
- Maryellen Giger, University of Chicago, m-giger@uchicago.edu
- Hamid Soltanian-Zadeh, Henry Ford health System, hamids@rad.hfh.edu
- Abbas Babajani-Feremi, University of Tennessee Health Science Center, ababajan@uthsc.edu
- Jonathan Bate , Yale University, jonathan.bates@yale.edu
- Robert Mcdougal, Yale University, robert.mcdougal@yale.edu
- Ali Haddad, Yale University, Ali.haddad@yale.edu
- Douglas Redd, George Washington University, doug_redd@email.gwu.edu
- Mohammed Siadat, Oakland University, siadat@oakland.edu
- Jose Martin, University of Valencia, jose.d.martin@uv.es
- Samuel Armato, University of Chicago, s-armato@uchicago.edu
- Jonathan Gemmell, DePaul University, jonathan.gemmell@gmail.com
- Henning Mueller, University Hospital of Geneva, Switzerland, henning.mueller@hevs.ch
- Sameer Antani, NIH, NLM, santani@mail.nih.gov
- Jacob Furst, DePaul University, jfurst@cdm.depaul.edu
Organizing committee
- Chair: Samah Jamal Fodeh, Yale University, samah.fodeh@yale.edu
- Co-Chair: Daniela Stan Raicu, DePaul University, draicu@cdm.depaul.edu
Contact
All questions about submissions should be emailed to samah.fodeh@yale.edu and draicu@cdm.depaul.edu