Sanne van der Linden

In many domains, multivariate event sequence data is collected focused around an entity (the case). Typically, each event has multiple attributes, for example, in healthcare a patient has events such as hospitalization, medication, and surgery. In addition to the multivariate events, also the case (a specific attribute, e.g., patient) has associated multivariate data (e.g., age, gender, weight). Current work typically only visualizes one attribute per event (label) in the event sequences. As a consequence, events can only be explored from a predefined case-centric perspective. However, to find complex relations from multiple perspectives (e.g., from different case definitions, such as doctor), users also need an event- and attribute-centric perspective. In addition, support is needed to effortlessly switch between and within perspectives. To support such a rich exploration, we present FlexEvent: an exploration and analysis method that enables investigation beyond a fixed case-centric perspective. Based on an adaptation of existing visualization techniques, such as scatterplots and juxtaposed small multiples, we enable flexible switching between different perspectives to explore the multivariate event sequence data needed to answer multi-perspective hypotheses. We evaluated FlexEvent with three domain experts in two use cases with sleep disorder and neonatal ICU data that show our method facilitates experts in exploring and analyzing real-world multivariate sequence data from different perspectives.

Healthcare systems are under pressure from an aging population, rising costs, and increasingly complex conditions and treatments. Although data are determined to play a bigger role in how doctors diagnose and prescribe treatments, they struggle due to a lack of time and an abundance of structured and unstructured information. To address this challenge, we introduce MediCoSpace, a visual decision-support tool for more efficient doctor-patient consultations. The tool links patient reports to past and present diagnoses, diseases, drugs, and treatments, both for the current patient and other patients in comparable situations. MediCoSpace uses textual medical data, deep-learning supported text analysis and concept spaces to facilitate a visual discovery process. The tool is evaluated by five medical doctors. The results show that MediCoSpace facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients.

Electronic Health Records (EHRs) contain rich medical information about patients, possibly hundreds of notes, lab results, images and other information. Doctors can easily be overwhelmed by this wealth of information. For their daily work, they need to derive narratives from all this information to get insights into the main issues of their patients. Standard solutions show all the information in linear lists, often leading to cognitive overload; research solutions provide timelines and relations between the notes but provide too much fragmented information. We propose MEDeNAR, a system for enabling medical professionals to obtain insights from EHRs based on the different tasks in their workflow. The key aspects of our system are the introduction of an intermediate level that summarizes the information using clustering and NLP methods. The results are visualized along a timeline and provide easy access to the detailed descriptions in notes and lab results at the EHR level. We designed the system using an iterative design process in collaboration with 18 doctors, two nurses and 14 domain experts. During the final evaluation, the doctors ranked our system higher than a standard baseline solution and a variation for the used NLP methods.

Knee overstretching, a condition where the knee bends too far backward, requires treatment and proper guidance otherwise it can deteriorate causing pain and knee instability. Patients may overstretch their knees because they lack awareness of their knee posture. Here we explore how to address the need for tele-rehabilitation and selftracking of knee posture throughout the day. Kneehab is an at-home wearable system consisting of a small knee brace with sensors, a pocket-size module connected to the brace which collects the data, and a smartphone application providing feedback and personalized reminders that apply a persuasion profiling approach. An initial evaluation by four physiotherapists, two knee overstretching patients and nine domain experts, is encouraging regarding the perceived potential of the Kneehab system as a therapeutic aid. Preliminary user tests (N=4) show a sensor accuracy of 5 degrees. Future research will seek to empirically validate the efficacy of the system as a therapeutic aid and to explore its potential in enriching the communication between the therapist and the patient.