APPLICATION : DIAGNOSTIC CODING
 Coding is the translation of
diagnosis terms describing
patients diagnosis or
treatment into a coded
number
 Used for medical bills and
insurance reimbursement
 Used for Disease statistics
 International classification
of diseases, 9th revision
(ICD-9)
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MANUAL CODING (ICD-9) PROCESS
HospitalPatient
Document
DB
– Notes
Patient
Diagnostic
Patients
– Code
Criteria DB
Code
database
Note
Patient
A
B
1
1
C
diagnosis
428
heart failure
250
diabetes
AMI
D
2
414
E
250
F
429
Insurance
3
2
G
Look up ICD-9 codes
SCIP
...
...
Statistics
reimbursement
...
...
...
...
...
...
...
...
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PATIENT RECORDS
HospitalPatient
Document
DB
– Notes
Patient
Note
A
B
1
C
D
E
F
2
G
Diagnostic
Code DB
Code database
Patients – Criteria
RWP/CC1
DICT. XXXXXXXXXXX P
TRANS. XXXXXXXXXX P
DOC.# 1554360
diagnosis
Patient
JOB # XXXXXXXXXX
CC XXXXXXXXXX
FILE CV
XXXXXXXXXXXXXXXXXX.
428
XXXXXXXXXXXXXXXXXX
ORDXXXXXXX, XXXX L
ADM DIAGNOSIS: BRADYCARDIA ANEMIA 1
CHF
250
ORD #: XXXXXXX DX XXXXXXX 14:10
PROCEDURE: CHEST - PA ` LATERAL ACCXXXXXX
REPORT: CLINICAL HISTORY: CHEST PAIN. CHF.
AMI THERE ARE NO PRIOR
AP ERECT AND LATERAL VIEWS OF THE CHEST WERE OBTAINED.
STUDIES AVAILABLE FOR COMPARISON.
THE TRACHEA IS NORMAL IN POSITION. HEART IS MODERATELY ENLARGED.
HEMIDIAPHRAGMS ARE SMOOTH. THERE ARE
2 SMALL BILATERAL
414 PLEURAL EFFUSIONS.
THERE IS ENGORGEMENT OF THE PULMONARY VASCULARITY.
IMPRESSION:
1. CONGESTIVE HEART FAILURE WITH CARDIOMEGALY AND250
SMALL BILATERAL PLEURAL
EFFUSIONS.
2. INCREASING OPACITY AT THE LEFT LUNG BASE LIKELY REPRESENTING PASSIVE
ATELECTASIS.
heart failure
diabetes
….
………………….
Look…………….
up ICD-9 codes
……….
3
SCIP
...
...
Statistics
reimbursement
...
...
...
...
...
...
...
...
SIEMENS
Insurance
429
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PATIENT RECORDS
HospitalPatient
Document
DB
– Notes
Patient
Note
A
B
1
C
D
E
F
2
G
...
Diagnostic
Code DB
Code database
Patients – Criteria
RWP/CC1
DICT. XXXXXXXXXXX P
TRANS. XXXXXXXXXX P
FAMILY HISTORY: IS NONCONTRIBUTORY IN A PATIENT OF THIS AGE GROUP.
DOC.# 1554360
diagnosis
Patient
JOB # XXXXXXXXXX
CC XXXXXXXXXX
SOCIAL HISTORY: SHE IS DIVORCED. THE PATIENT CURRENTLY LIVES AT BERKS HEIM.
FILE CV
SHE IS ACCOMPANIED TODAY ON THIS VISIT BY HER DAUGHTER. SHE DOES NOT SMOKE
XXXXXXXXXXXXXXXXXX.
OR ABUSE ALCOHOLIC BEVERAGES. 428
XXXXXXXXXXXXXXXXXX
ORDXXXXXXX, XXXX L
PHYSICAL EXAMINATION: GENERAL: THIS IS AN ELDERLY, VERY PALE-APPEARING
ADM DIAGNOSIS: BRADYCARDIA
ANEMIA
CHF IN A WHEELCHAIR
FEMALE WHO
IS SITTING
1
250 AND WAS EXAMINED IN HER WHEELCHAIR.
ORD #: XXXXXXX DX XXXXXXX
14:10
HEENT: SHE
IS WEARING GLASSES. SITTING UPRIGHT IN A WHEELCHAIR. NECK: NECK
PROCEDURE: CHESTVEINS
- PA ` LATERAL
ACCXXXXXX I COULD NOT HEAR A LOUD CAROTID BRUIT. LUNGS: HAVE
WERE NONDISTENDED.
REPORT: CLINICAL HISTORY:
CHEST
PAIN. CHF.
DIMINISHED
BREATH
SOUNDS AT THE BASES WITH NO LOUD WHEEZES, RALES OR
AMI THERE ARE NO PRIOR
AP ERECT AND LATERAL
VIEWS HEART:
OF THE HEART
CHEST WERE
RHONCHI.
TONESOBTAINED.
WERE BRADYCARDIC,
REGULAR AND RATHER DISTANT
STUDIES AVAILABLE FOR
WITHCOMPARISON.
A SYSTOLIC MURMUR HEARD AT THE LEFT LOWER STERNAL BORDER. I COULD NOT
THE TRACHEA IS NORMAL
HEART
IS MODERATELY
HEARIN
A POSITION.
LOUD GALLOP
RHYTHM
WITH HER ENLARGED.
SITTING UPRIGHT OR A LOUD DIASTOLIC
HEMIDIAPHRAGMS ARE
SMOOTH.
THERE ARE
SMALL
PLEURALEXTREMITIES:
EFFUSIONS. ARE REMARKABLE FOR
2WAS
MURMUR.
ABDOMEN:
SOFTBILATERAL
AND 414
NONTENDER.
THERE IS ENGORGEMENT
OF THE
PULMONARY
THE FACT
THAT
SHE HAS AVASCULARITY.
BRACE ON HER LEFT LOWER EXTREMITY. THERE DID NOT
IMPRESSION:
APPEAR TO BE SIGNIFICANT PERIPHERAL EDEMA. NEUROLOGIC: SHE CLEARLY HAD
1. CONGESTIVE HEART
FAILUREHEMIPARESIS
WITH CARDIOMEGALY
AND
SMALL BILATERAL
250
RESIDUAL
FROM HER
PREVIOUS
STROKE, PLEURAL
BUT SHE WAS AWAKE AND ALERT
EFFUSIONS.
AND ANSWERING QUESTIONS APPROPRIATELY.
2. INCREASING OPACITY AT THE LEFT LUNG BASE LIKELY REPRESENTING PASSIVE
ATELECTASIS.
heart failure
diabetes
Insurance
………………
….
…………………. ……………….. 3
………..
Look…………….
up ICD-9 codes
…………
……….
………
……..
…….
429
SCIP
...
Statistics
reimbursement
...
...
...
...
...
...
...
...
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COMPUTER ASSISTED CODING
HospitalPatient
Document
DB
– Notes
Patient
Diagnostic
Patients
– Code
Criteria DB
Code
database
Note
Patient
A
B
1
C
D
Computer
coding
system
1
diagnosis
428
heart failure
250
diabetes
AMI
2
414
E
250
F
429
Insurance
3
2
G
SCIP
...
...
Statistics
reimbursement
...
...
...
...
...
...
...
...
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HYBRID APPROACH (KNOWLEDGE-BASED)
Existing approaches are rule-based systems that solve the
coding task using a set of hand crafted expert rules
Our Solution:
Human Knowledge
Medical textbook, medical
ontology, clinical practice
Machine Intelligence
Natural language processing,
statistical text mining
Computerized Coding
In-house DB with
300,000 records
from 15,000 patients
Diagnostic code
DB
Papers in IJCNLP 2008, ICMLA
2007, ECML 2008
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Automatic Medical Coding of Patient Records
 J. Xu, S. Yu, Jinbo Bi, L. Lita, S. Niculescu, Automatic Medical Coding of Patient Records via
Weighted Ridge Regression, Proceedings of the 6th International Conference on Machine
Learning and Applications, (ICMLA) 2007.
 L. Lita, S. Yu, S. Niculescu, Jinbo Bi, Large Scale Diagnostic Code Classification for Medical
Patient Records, Proceedings of the 3rd International Joint Conference on Natural Language
Processing, (IJCNLP) 2008
 Jinbo Bi et al, Incorporating Medical Knowledge into Automatic Medical Coding of Patient
Records, Patent Invention Disclosure of Siemens Medical Solutions, Technical Report, 2008.
Joint Optimization of Classifiers for Clinically Interrelated Diseases
 Jinbo Bi et al. An Improved Multi-task Learning Approach with Applications in Medical
Diagnosis, Proceedings of the 18th European Conference on Machine Learning (ECML), 2008.
 Jinbo Bi et al. A Mathematical Programming Formulation for Sparse Collaborative
Computer Aided Diagnosis, Proceedings of the 22nd International Conference on Artificial
Intelligence, (AAAI) 2007.
 T. Xiong, Jinbo Bi, B. Rao, V. Cherkassky, Probabilistic Joint Feature Selection for Multi-task
Learning, Proceedings of SIAM International Conference on Data Mining, (SDM) 2006.
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STANDALONE ACCURACY OF CAC
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Hybrid
hybrid MTL
1.2
1
0.8
0.6
0.4
0.2
IP
SC
)
CA
P(
PN
HF
I
AM
25
0
42
8
41
4
0
41
0
Area Under ROC Curve
No prior
No prior: pure data-driven SVM classifier (IJNLP 2008); Hybrid: combine
medical knowledge with SVM classifier; Hybrid MTL: combine medical
knowledge with collaborative prediction method (ECML 2008)
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CONCLUSIONS
 Preliminary results show combining known medical knowledge
with statistical learning techniques strengthened the data mining
applications in coding process
 A lot more … …
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POTENTIAL RESEARCH
Images
Patient
factors
Proteomics
Personalized Knowledge Models
Genomics
Clinical
Decision
Support
Treatment
plans
Known Medical Knowledge
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