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Lifelines2: Discovering Temporal Categorical Patterns Across Multiple Records

 

LATEST NEWS

new! December 17, 2007.  Our first paper on Lifelines2 is accepted to CHI 2008, to be held in Florence, Italy.  See the full details in the papers below.

Project Description

Electronic health records (EHRs) contain a wealth of information.  Categorical event data such as complaints, diagnoses, treatments, etc., are important, and play important roles in health providers decision making.  However, past research efforts have been focused on numerical data and single-record visualization techniques.  Discovering patterns of categorical events across multiple records are supported in limited ways.

Lifelines2 is an interactive visualization tool for visualizing temporal categorical data across multiple records.  The goal of the project is to enable discovery and exploration of patterns across these records to support hypothesis generation, and finding cause-and-effect relationships in a population.  These tasks are not  specific to the medical domain, but we were first motivated by EHRs.

Features

In Lifelines2, we advocate a framework of simple operators to allow users to manipulate multiple records simultaneously to understand relative temporal relationships across records.  The three operators are Align, Rank, and Filter, and we affectionately call it the ARF framework.  Alignment forces every record to be aligned by a certain feature (e.g. 3rd Heart Attack) so the events that occur prior to and after the feature can be compared easily.  Rank and Filter are traditional operators analysts are familiar with, and they augment the Align offers.

47 records of heart disease patients are loaded, and filtered by occurrence of Acute Myocardial Infarction (AMI) -- leaving 7/47 records available.

[Align by 1st AMI] to move every patient's first AMI on the same vertical line, revealing diagnoses that were observed just prior to and after their first AMI.  This view facilitates relative comparison with respect to the aligned events.

Zoom is further applied to show details.  It is clearly visible that the Coronary Atherosclerosis (orange triangles) was often co-diagnosed for 1st-time AMI patients.

Lifeliens2 is primarily designed for categorical point events, but analysts can add intervals of validity to visually remind them of implicit durations to aid their decision-making process.

 

 Participants

Publications

new!  Taowei David Wang, Catherine Plaisant, Alex Quinn, Roman Stanchak, Ben Shneiderman, and Shawn Murphy.  Aligning Temporal Data by Sentinel Events: Discovering Patterns in Electronic Health Records.  SIGCHI Conference on Human Factors in Computing Systems (CHI 2008).

Sponsors and Partners

This project is supported in part by the Washington Hospital Center and Harvard Medical School - Partners HealthCare

Other Related Projects from HCIL

Lifelines:  Visualizing patient records, criminal records, and personal histories.

PatternFinder:  integrated interface for visual query and result-set visualization for search and discovery of temporal patterns within multivariate and categorical data sets.

PatternFinder in Amalga:  Temporal Query Formulation and Result Visualization in Action

Related Workshops from HCIL

Personal Medical Devices Workshop: Increasing Patient Healthcare Participation (June 3, 2004)

Interactive Visual Exploration of Electronic Health Records (May 30, 2008)