AIME'05 1

Learning rules from multisource data

for cardiac monitoring

Elisa Fromont*, René Quiniou, Marie-Odile Cordier

DREAM, IRISA, France

*work supported by the French National Net for Health Technologies as a member of the Cepica project

AIME'05 2

Context (ICCU/ [Calicot03]) Cardiac Arrhythmias Learning for Intelligent Classification of On-line Tracks

0 1000 2000 3000 4000

Signal abstraction:

raw data (ECG)

symbolic descriptions

Chronicle recognition

t

P(normal) P(normal)

t0 t1 t2 t3 t4

QRS(normal) QRS(normal)

Arrhythmia

QRS(abnormal)

On line

Chronicle base

Inductive learning

Symbolic

transformationOff line

Rule baseSignal data

base

[Moody96]

AIME'05 3

Motivations• Why learning rules ?

A knowledge acquisition module can relieve experts of that time-consuming task [Morik00]

• Why using Inductive Logic Programming (ILP) ?– First order rules are easily understandable by doctors – Relational learning allows to take into account temporal constraints

( chronicles)

• Why using multiple sources ?Information from a single source is not always sufficient to give a

precise diagnosis (noise, complementary information, etc.)

Update Calicot for multisource management

AIME'05 4

Multisource data

2 ECG channels, 1 hemodynamical channel:

3 views of the same phenomenon

ECG Chan II :(P, QRS)

ECG Chan V :(QRS)

ABP Chan

Sensor 1

Sensor 2

Sensor 3

AIME'05 5

Monosource learning with ILP• From

– A set of examples E defined on LE labeled by a class c C

– For each class c, E+ = {(ek,c)| k = 1,m} are the positive examples

and E- = {(ek,c’)| k = 1,m, c c’} are the negative examples

– A bias B that defines the language LH of the hypotheses looked for

– A Background knowledge BK defined on L = LH LE

• Find for each class, a set of hypotheses H such that :1. H BK E+ (H covers all the positive examples)

2. H BK E- (H covers no negative example) *

* in practice this property is loosen

AIME'05 6

Declarative bias [Bias96]• Grammar to define :

– the language (specify the vocabulary to use)– the length of the hypotheses looked for– the order in with consider literals

• Mandatory for ILP system such ICL[ICL95]•

AIME'05 7

Example of learned monosource rule

rule(bigeminy) :-qrs(R0, anormal),p_wav(P1, normal), suc(P1,R0), qrs(R1, normal), suc(R1,P1),qrs(R2, anormal, R1), suc(R2,R1),rr1(R1, R2, short).

 

0 500 1000 1500 2000 2500 3000

R0 R1 R2

P1

Example bigeminy e21

Example bigeminy e31

Example X* e41

Example Z* en1

…

Example bigeminy e11

*X…Z bigeminy

Induction+B +BK

AIME'05 8

Multisource learning : 2 approaches(example on two sources for one class)

Consistency : i j (ek,i, c) (ek,j, c’) c = c’

Example bigeminy e12

Example bigeminy e13

Example X* e14

Example Z* e1n

…Example bigeminy e11

Induction+B1 +BK1

Example bigeminy e22

Example bigeminy e23

Example X* e24

Example Z* e2n

…

Example bigeminy e21

Induction+B2 +BK2

H1

H2

H

Example bigeminy e12

Example X* e14

Example Z* e1n

…

Example bigeminy e11

Example Z* e2n

Example X* e24

Example bigeminy e13

Example bigeminy e23

Example bigeminy e22

Example bigeminy e21

Induction+B +BK

aggregated examples

Naive multisource learningmonosource learning on source 1

monosource learning on source 2

Vote between H1 and H2 ?

AIME'05 9

Naive multisource learning problemsWhen number of sources increases

– volume of data increases (aggregation of examples)

– expressiveness of language increases the size of the hypothesis search defined by

B is bigger than both search spaces defined by B1 and B2

• too much computation time• bad results due to important pruning

when looking for hypotheses in the search space

AIME'05 10

Idea : biased multisource learning

• Bias efficiently the multisource learning by using : – monosource learned rules– aggregated examples

• Difficult to define without background knowledge on the problem

create a multisource bias automatically !

AIME'05 11

Algorithm (on two sources)

Resulting search space

L : naive multisource language

H2

LL2

H1L1

Lbbt1 bt2 bt3bt4

bt5

Lb : biased multisource language

Li : langage of source i

AIME'05 12

How to construct bti ?(toy example)

• H1: class(x):-

p_wave(P0,normal),qrs(R0,normal), pr1(P0,R0, normal), suc(R0,P0).

• H2:class(x):-

diastole(D0,normal),systole(S0,normal),suc(S0,D0).

…

class(x):-p_wave(P0,normal),diastole(D0,normal),suci(D0,P0),qrs(R0,normal),systole(S0,normal),suci(S0,R0), pr1(P0,R0,normal), suc(R0,P0), suc(S0,D0).

class(x):-p_wave(P0,normal),qrs(R0,normal), pr1(P0,R0,normal), suc(R0,P0),diastole(D0,normal), suci(D0,R0),systole(S0,normal),suc(S0,D0).

Rule fusion + new relational literals

AIME'05 13

Properties of the biased multisource search space

1. rules learned with the biased multisource method have an equal or higher accuracy than the monosource rules learned for the same class (in the worst case: vote)

2. the biased multisource search space is smaller than the naive multisource search space ( DLAB [DLAB97])

3. there is no guaranty to find the best multisource solution with the biased multisource learning

AIME'05 14

Examples of learned rulesclass(svt):- %ECG

qrs(R0),qrs(R1),suc(R1,R0),

qrs(R2),suc(R2,R1),rr1(R1,R2,short),

rythm(R,R1,R2,regular),

qrs(R3), suc(R3,R2),rr1(R2,R3,short),

qrs(R4),suc(R4,R3),rr1(R3,R4, short).

(covers 2 neg)

class(svt):- %ABP

systole(S0),systole(S1),suc(S1,S0),

amp_ss(S0,S1,normal),

systole(S2),suc(S2,S1),

amp_ss(S1,S2,normal),ss1(S1,S2,short).

(covers 1 neg,

does not cover 1 pos)

class(svt):- %biased multi

qrs(R0),qrs(R1),suc(R1,R0),

qrs(R2), suc(R2,R1),rr1(R1,R2,short),

rythm(R,R1,R2,regular),

qrs(R3), suc(R3,R2),rr1(R2;R3,short),

systole(S0), suci(S0,R3),

qrs(R4), suci(R4,S0),suc(R4,R3),

systole(S1),suc(S1,S0), suci(S1,R4),

amp_ss(S0,S1,normal).

class(svt):- %naive multi qrs(R0), systole(S0), suc(S0,R0), qrs(R1), suc(R1,S0), systole(S1), suc(S1,R1),suc(R1,R0),rr1(R1,R2,short).(covers 12 neg)

AIME'05 15

Results on the whole database

6572273510231063Nb Nodes

54/23/25Cardiaccycles

1221Nb Rules

10.70.841TestACC

10.9160.9981ACC

arrhyt1bigeminy BiasedNaive

Source 2(ABP)

Source 1(ECG)

Multi sourceMono source

363.86*310014.2726.99CPU time

*include monosource computation times

Biased multisource much more efficient than naive multisource

No significant improvement from monosource to biased multisource

Database :

• small(50)

• not noisy

• sources are redundant for the studied arrhythmias

AIME'05 16

Less informative database(new results without multisource cross validation problems and

new constraint on ABP monosource learning)

8/54/4/654Cardiaccycles

2311Rules(H)

0.90.640.860.4TestACC

0.980.9450.940.44ACC

arrhyt1ves

BiasedNaiveSource 2

(ABP)

Source 1

(ECG)

Multi sourceMono source

5235Cardiaccycles

1111Rules(H)

0.920.760.840.94TestACC

0.990.760.9620.96ACC

arrhyt2svt

BiasedNaiveSource 2

(ABP)

Source 1

(ECG)

Multi sourceMono source

AIME'05 17

ConclusionBiased multisource vs monosource: better or equal accuracy less complex rules (less rules or less literals)

Biased multisource method vs naive method: better accuracy narrower search space reduced computation time

Multisource learning can improve the reliability of diagnosis (particularly on complementary data)

The biased method allows scalability

AIME'05 18

References[Calicot03] : Temporal abstraction and inductive logic

programming for arrhythmia recognition from ECG. G. Carrault, M-O. Cordier, R. Quiniou, F. Wang, AIMed 2003

[Moody96] : A database to support development and evaluation of intensive care monitoring. G.B. Moody et al. Computer in Cardiology 96

[ICL95] : Inductive Constraint Logic (ILP). L. De Raedt et W. Van Laer, Inductive Logic Programming 95

[Bias96] : Declarative bias in ILP. Nedellec et al. Advances in ILP 96

[DLAB97] : Clausal discovery. L. De Raedt, L. Dehaspe, Machine Learning 97

[Morik00] : Knowledge discovery and knowledge validation in intensive care. K. Morik et al. AIMed 2000

AIME'05 19

Property on aggregated examples

Let Hic a hypothesis induced by learning from source i, i [1,s] and the class c C

• For all k [1,p], if Hic covers (ei,k, c) then it also covers the aggregated example (ek,c)

• For all k [1,n], for all c’ {C-c},

if Hic does not cover (ei,k, c’) and if for all ji, Li Lj= then Hic does not cover the aggregated negative example (ek ,c’)

AIME'05 20

Activité électrique du cœur : les éléments de l’apprentissage (voies II et V)

AIME'05 21

Voie hémodynamique

Attributs :

- amplitude diastole/systole

- différence d’amplitude entre diastole et systole

- intervalle de temps entre diastole et systole (sd, ds, dd, ss, ….)