Post on 04-Jan-2016
transcript
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
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50
60
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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
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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)