AssistMe

Project leadersAnkica Babic, Urban Lönn, Henrik Casimir Ahn

Problem solving 1• Start with clinical questions that

should be supported by decision support and data mining.

• Distinguish levels of decision support: from user driven to structured procedures for knowledge mining: – Cluster analysis, Case Based

Reasoning (CBR), statistical reports– More, specialized reports?

Problem solving 2

• Actively involve the physicians in design, implementation, and evaluation of our web based system.

• Clinical evaluation of extracted knowledge.

System overview

Start page

Homepage for patients

Questionnaires

Homepage for physicians

Add patient cases

Case based reasoning(result)

Case based reasoning (patient case)

Cluster analysis - introduction

Cluster analysis

Cluster analysis• Calculates the equality/difference

between patients

20

60

60

90

kg

years

a

a = age difference = 40 years

b

b = weight difference = 30kg

22 bac

222 bac

90016003040c 22

502500

c

c = “distance” between patients

The difference is:

50

Example: Calculation of difference using age and weight:

Cluster analysis• Calculates the equality/difference of patients• Places “similar” patients in the same groups

(clusters) and “different” patients in different groups.

• The user can choose what variables to use for comparing the patients when the population is divided into subgroups. The number of groups must also be specified.

• Additional information, such as the survival percentage, is provided for the different groups.

Clusters (former page)

0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12 14

0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12 14

Age

1

2

3

4

5

6

Higgins

Outcome

0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12 14

0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12 14

Age

0,87

0,67

1,0

0,5

1,0

0,63

Higgins

What is w and b in the summarization table?•w is short for “within distance”

•b is short for “between distance”

Large within distance

Small between distance

W/b=Large Not a good result!

Large within distance

Small between distance

Small within distance

Large between distance

W/b=Large w/B=SmallNot agood result!

The desired result!

What is w and b in the summarization table?•w is short for “within distance”

•b is short for “between distance”

Homogenization

In order to be able to compare different variables which have different magnitude of values.

1

44

78

100

0

Age

114 7

610,50

0,43

0,57

0,32c

65,032,057,0c 22

Higgins0 15

1

0

Patient 1: Age 61; Higgins 7

72

14

0,82

Patient 2: Age 72; Higgins 14

Automatic cluster

Automatic cluster - setup

Automatic cluster – results

Design of user interface

Design for usability

• The design process is a constant shifting between the following three abilities– The ability to understand and

formulate the design problem– The ability to create design solutions– The ability to evaluate those solutions

How to create premises for the design

• Initial understanding – What? Who? Where? Why?

• Studies of literature• Fields studies• Increased understanding of

What? Who? Where? Why?

Field studies

• Contextual research• Create scenarios• Design/ Style studies• Task analysis

Qualities in useWhat is “good” for this type of

system, these users in this context?

Important qualities and what they are based on• Aesthetic values: the feeling of a trustworthy

system• Practical values: easy to learn, effective use,

possibility to abort actions• Psychological values: cognitive ease of use,

psychological support• Autonomic values: Freedom of choice• Social values: facilitate consent, supporting ”the

team mind”

Design phase

• Sketch, evaluate, comment• Create paper prototype• Test paper prototype• Create computerized prototype• Test computerized prototype• Implementation

“The doctor’s information tool of the future might be some sort of combination between the patient record and the Internet, with the doctor and the patient positioned together at the intersection but not having to pay attention to the technology.” (Smith 1996)

Database design

Layered structure

Application (AssistMe)

Database manager / system

Database interface

Layered structureJava code of AssistMe

Patient cases

Metadataba

se

Archive

database

... ...

Database interface in Java

Patient case

Old database design

• Flat structure (little or no relations)

Data Data

Data

DataData

Data

Data

Data

Data

DataData

Data

DataData

Data Data

Discharge

PostOp

New database design

• Relational database design

Data

Demografi

PreOp

PerOp

RelRel

RelRelData

Data

Data

Data

DataData

Data

Data

Data

Data

Data

Data

Data

Database design

• Structured Query Language, SQL– Standard for commercial database

managers– Easy to transfer information to and

from the database.

Database design

• Dynamical structure– Should be easy to change the type of

data that is stored in the database

• Support for more than one database in the system at once– The system can be used in parallel

for different purposes.

Database interface

• Database interface specially developed for the system– Easy to read and write information

in the database.– Easy to add new tools (Cluster, CBR,

…) that utilizes the databases.

LVAD Outcomes• Overview of the area: functionality,

clinical use (bridge or destination therapy, continued care), types/families of LVAD, short technical descriptions and pictures.

• Scenario from start to end. QoL (including cost consideration).

• This is focused on the aspects of morbidity and mortality. Literature studies.

Mortality

• Definitions, surgical perspective on it, heart transplant specific aspects and reflection over the follow up and waiting time prior to transplantation.

• Accepting the 30 days survival as standard. All mortality is registered including cause of death.

Morbidity

• Complications. Technical and clinical complications with reference to device related problems.

• Definitions of complications (clear cut and/vs. working definitions), motivating the definitions used in this research. Addressing verity and complexity of definitions.

Morbidity

• Motivation or/and pragmatic reasoning about the morbidity.

• Research vs. clinical thinking.• Give better understanding of

mechanisms involved in order to reduce the incidence (Piccione Jr. W. 2000).

Risk Factors

• Overview of risk factors used within the LVAD domain and their usage to assess morbidity and mortality.

• Higgins, Euro scores, other systems for risk stratification.

• Outlines we have accepted in our research.

Patient Selection

• In terms of indications, demographic data, selection criteria in use, ethics around it.

• It is of paramount importance to choose patient that is ‘appropriate’ for treatment to succeed.

• (See Left Ventricular Assist, Fraizer, 1997)