dmmi17

4^th Workshop on Data Mining for Medical Informatics:
Causal Inference for Health Data Analytics

Nov 4, 2017, Washington, DC

To be held in conjunction with the AMIA 2017 Annual Symposium

DMMI 2017 workshop is sponsored by AMIA Knowledge Discovery and Data Mining Working Group

The biomedical sciences and healthcare are contributing significantly to the big data revolution through advances in genomic sequencing technology and imaging, clinical and personally-generated data. Data mining and machine learning techniques have played an increasingly important role in medical informatics with the goal of discovering knowledge and insights from various data sources. Causal inference is an important methodological pool from which one can draw powerful techniques for knowledge discovery and data-driven insights. Causal discovery methods were developed to address the financial and ethical concerns associated with randomized controlled trials. An attestation to their significance is that they have been recognized with the Turing Award in computer science and the Nobel Prize in economics, and have led to exciting interdisciplinary research in statistics, philosophy, social sciences, and neuroscience. Discovery of causality is a major goal in basic, translational and clinical science. In computational biology, neuroscience, epidemiology and biomedicine one often faces the daunting task of finding causal relationships in very large-dimensional data. This highlights the necessity to develop and evaluate algorithms and tools to improve the current state of the art in causal discovery from experimental, quasi-experimental and non-experimental (i.e., observational) data.

The main theme of the workshop this year is causal inference for health data analytics, which aims to address both the theoretical and experimental underpinnings of these methods. This includes development and applications of the methods and discussions on how to make them practically useful to clinicians, patients and other healthcare stakeholders. This topic is timely and has received a lot of interest recently. We would like to invite researchers from both academia and industry who are interested in this topic to participate in this workshop, share their opinions and experience, as well as discuss future directions.

This year's DMMI workshop will be co-located with the 2017 American Medical Informatics Association (AMIA) Annual Symposium.
For more information on the prior DMMI Workshops click here (2014, Washington, DC), here (2015, San Francisco, CA), or here (2016, Chicago, IL).

Topics and Scope

Topic areas for the workshop include (but are not limited to) the following:

Research design for causal inference in real-world data
Causal structure discovery in large-scale, observational data
Intersection of machine learning and causal inference
Real-world medical and health applications of causal analysis
Causal inference from personally-generated data and surrogate data sources
Causal inference software and tools

Paper Submission and Format Guidelines

We encourage a diverse range of submissions and demonstrations from academic, healthcare organizations, and industry that addresses any of the topics listed above. Submissions can be for (1) paper / podium presentations, or (2) abstract / podium presentations.

Paper submissions must be no more than six pages in length, inclusive of figures and references.

Abstract submissions are limited to two pages.

Papers should be formatted in AMIA format styles. Manuscripts must be submitted as Adobe Portable Document Format (PDF) files.
Other file formats will not be accepted.

Full papers and abstracts must be submitted electronically through the EasyChair system at this link:
https://easychair.org/conferences/?conf=dmmi2017

Selected submissions will be invited to the Journal of Health Informatics Research, the journal website is here.

Important Dates

Deadline for submission:	~~August 31, 2017~~
Notification of acceptance:	~~September 15, 2017~~
Camera-ready Papers Due:	~~October 20, 2017~~
Workshop:	~~November 4, 2017~~

Workshop Organizing Committee

Kenney Ng IBM Research
Bisakha Ray New York University
SiSi Ma University of Minnesota Kun Zhang Carnegie Mellon University Fei Wang Cornell University

Program Committee

Zach Shahn, IBM Research
Cao Xiao, IBM Research
Erich Kummerfeld, University of Minnesota
Chih-Lin Chi, University of Minnesota
Narges Razavian, NYU School of Medicine
Himanshu Grover, NYU School of Medicine

Workshop Location

Date/Time: 8:30 AM - 4:30 PM, November 4, 2017
Location: International Ballroom East, Washington Hilton (floorplan)
Links:

Workshop Schedule

W07: Data Mining for Medical Informatics (DMMI) – Causal Inference for Health Data Analytics

(sponsored by the Knowledge Discovery and Data Mining Working Group)

Type	Time	Presenter	Title
Opening	08:30-08:35	Eileen Koski	KDDM WG Opening Remarks
Welcome	08:35-08:45	Kenney Ng	Welcome (slides)
Invited Talk	08:45-09:30	Miguel Hernan	An algorithm for causal inference from observational data (slides)
Long Paper	09:30-09:45	Erich Kummerfeld	A New Method for Estimating Causal Model Learning Accuracy (slides)
Long Paper	09:45-10:00	Fattaneh Jabbari	An Assessment of the Calibration of Causal Relationships Learned Using RFCI and Bootstrapping (slides)
Long Paper	10:00-10:15	Zach Shahn	Self-Controlled Structural Nested Mean Models (slides)
Coffee Break	10:15-10:30		Coffee Break
Invited Talk	10:30-11:15	David Danks	Causal discovery from time series data: From theory to application (slides)
Long Paper	11:15-11:30	Yanick Brice	Meaningful Use of Electronic Health Record and Patient Utilization Outcomes within 30 Days of Hospital Discharge (slides)
Long Paper	11:30-11:45	Jinghe Zhang	Exploring the Causal Relationships between Initial Opioid Prescriptions and Outcomes (slides)
Short Paper	11:45-12:00	Victor Castro	Identifying Causal Effects of Medication Exposures from Electronic Health Record Data with i2b2 (slides)
Lunch Break	12:00-13:00		Lunch Break
Invited Talk	13:00-13:45	Constantin Aliferis	Causal Feature Selection (slides)
Long Paper	13:45-14:00	Scott Malec	Using the Literature to Construct Causal Models for Pharmacovigilance (slides)
Long Paper	14:00-14:15	Subramani Mani	Causal Discovery from Pediatric Infectious Disease Protein Biomarker Data (slides)
Long Paper	14:15-14:30	Ali Bakhtiari	Inferring immune cell subtype interactions from gene expression of immune related genes in tumor microenvironment (slides)
Coffee Break	14:30-15:00		Coffee Break
Short Paper	15:00-15:15	Ryan Sandefer	Discovering relationships in consumer use of personal health information: A causal modeling approach (slides)
Long Paper	15:15-15:30	Jeremy Espino	The Causal Modeling and Discovery Software Suite (slides)
Invited Talk	15:30-16:15	Gregory Cooper	Graphical Causal Discovery from Big Biomedical Data (slides)
Closing	16:15-16:20	Kenney Ng	Closing Remarks (slides)

Invited Speakers

Speaker	Topic	Abstract
Prof. Miguel Hernan	An algorithm for causal inference from observational data	Making decisions among several courses of action requires knowledge about the causal effects of each action. Randomized experiments are the preferred method to quantify those causal effects. When randomized experiments are not feasible or available, causal effects are estimated from non-experimental or observational databases. Therefore, causal inference from observational databases can be viewed as an attempt to emulate a hypothetical randomized experiment—the target experiment or target trial—that would quantify the causal effect of interest. This talk outlines a general algorithm for causal inference using observational databases that makes the target trial explicit. This causal framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational analyses, and helps avoid common methodologic pitfalls.
Prof. David Danks	Causal discovery from time series data: From theory to application	Many biomedical and scientific investigations aim to understand a system that dynamically changes over time, such as neural processes in the brain, development of cancerous tissues, or health trajectories for diverse patients. Moreover, we frequently need to learn causal models of the underlying dynamical systems, as we want to not only predict their behavior, but also design effective interventions--actions, policies, modifications, and so forth--to control them and achieve desired health outcomes. In this talk, I will discuss different strategies for causal discovery from dynamical or time series data, with a particular focus on learning from complex types of data or datasets. There are important considerations that must be resolved in advance of the causal discovery process, but these can be encapsulated in a few key questions. Finally, I will provide some biomedical examples of causal discovery from time series data.
Prof. Constantin Aliferis	Causal Feature Selection	Causality is not only important for designing interventions that will steer a system of interest to a desired state, but also has a central importance for feature selection for predictive modeling. This talk will discuss Markov Boundary inference as a solution to the vanilla feature selection problem. We will first describe theoretical foundations, then describe algorithmic approaches and finally we will examine empirical results from a variety of domains that test how well theoretical expectations are reflected in real-world data analysis. We will also contrast causal with non-causal feature selection both theoretically and empirically.
Prof. Gregory Cooper	Graphical Causal Discovery from Big Biomedical Data	Science is centrally concerned with the discovery of causal relationships in nature. In the past 25 years there has been tremendous progress in the development of graphical methods for representing and discovering causal relationships from data, including big biomedical data. The Center for Causal Discovery (CCD) is developing and making available state-of-the-art graphical causal discovery software that is capable of analyzing very large biomedical datasets. This talk will present a brief overview of the CCD, an introduction to graphical causal discovery methods, and several examples of the use of these methods in analyzing biomedical data.

Post Workshop Survey

Please complete the following survey after the workshop to provide feedback to improve future workshops: https://www.surveymonkey.com/r/AMIA2017_W07

AMIA2017_DMMI_workshop_closing.pdf

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Kenney Ng,

Nov 5, 2017, 6:48 PM

4th Workshop on Data Mining for Medical Informatics: Causal Inference for Health Data Analytics