There are many interesting challenges in delivering intelligent decision support in the health domain. Collections of documents such as health records, scholarly publications, clinical trials, or drug orders grow at high rates and are distributed around the globe in a fragmented manner. Health data is highly multi-modal (clinical notes, time series, medical images, genomics etc.) and its interpretation is domain specific. Users of health information systems have different levels of expertise, and information needs, e.g., a patient vs. a primary care physician vs. cancer researcher. At the same time, the data is highly sensitive and subject to legal requirements regarding privacy, security, and confidentiality. This breadth of challenges requires interdisciplinary approaches. The Information Retrieval (IR) and Data Mining (DM) communities are particularly well-positioned to tackle these problems.

Search, recommendation, and information extraction systems help lay and expert users explore ever-growing collections. Decision support systems assist in complex decision making processes. Intelligent user interfaces present the right information at the right time and allow for unobtrusive interaction all the way from the lab to the bedside. Mobile device applications and other sensors help provide a more holistic view on the patient's case than what can be gleaned in an 10-minute physician interview.

Health-related topics of interest include, among others:

• Search over images/genomics/structured data
• Federated multi-modal search combining different data sources
• User interfaces for biomedical/clinical search supporting complex information needs
• Analysis of search logs and social media
• User search behavior studies
• Building and use of medical knowledge bases or ontologies
• Privacy-preserving techniques for clinical data
• Adverse event detection and prediction
• Mobile (mHealth) applications
• Wearables
• Spoken interaction with health data
• Whole exposome modeling and estimation
• Applications of data mining and machine learning
• Ethics, bias, and fairness

Submission files should not exceed 8 pages with additional pages allowed for references. Reviews are double blind; author names and affiliations should be removed. All submissions must be written in English and submitted as PDF files formatted using the standard ACM "sigconf" template: https://www.acm.org/publications/proceedings-template.

Submissions should be made electronically through EasyChair: https://easychair.org/conferences/?conf=hsdm20

• Nov 11, 2019: Submissions due
• Dec 1, 2019: Submission notifications
• Dec 13, 2019: Camera-ready deadline
• Feb 3rd, 2020: HSDM Workshop held jointly with the Healthcare Day

http://www.wsdm-conference.org/2020/healthcare-day-schedule.php

http://ceur-ws.org/Vol-2551/

• Alba Garcia Seco de Herrera (University of Essex)
• Aurelie Neveol (CNRS, LIMSI)
• Bevan Koopman (Australian e-Health Research Centre, CSIRO)
• Dina Demner-Fushman (National Library of Medicine)
• Elad Yom-Tov (Microsoft)
• Eugene Agichtein (Emory University)
• Evgeniy Gabrilovich (Google AI)
• Guido Zuccon (University of Queensland)
• Henning Müller (HES-SO Valais)
• Joao Palotti (Qatar Computing Research Institute)
• Justin Zobel (University of Melbourne)
• Karin Verspoor (University of Melbourne)
• Kirk Roberts (University of Texas)
• Michael Paul (University of Colorado, Boulder)
• Tim Althoff (University of Washington)
• Stephen Bedrick (Oregon Health and Science University)

• Carsten Eickhoff (Brown University)
• Yubin Kim (UPMC Enterprises)
• Ryen White (Microsoft Research)

• carsten@brown.edu

• What should be included in the page header/footer?

Just author names, please.

• How should I format the copyright block?

We recommend using \documentclass[sigconf,nonacm]{acmart} to hide the copyright block.

• The copyright statement you provided reads "Copyright © 2019 [...]." Shouldn't the year be 2020?

Yes, 2020 is correct.

• How should the WSDM overview paper be cited?

This is ultimately up to you but we recommend including an intro sentence along the lines of "This work was presented at the first Health Search and Data Mining Workshop (HSDM 2020) [x]"