Challenges and techniques for mining clinical data
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Challenges and Techniques for Mining Clinical data. Wesley W. Chu Laura Yu Chen. Outline. Introduction of SmartRule association rule mining Case I: mining pregnancy data to discover drug exposure side effects Case II: mining urology clinical data for operation decision making.

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Outline
Outline

  • Introduction of SmartRule association rule mining

  • Case I: mining pregnancy data to discover drug exposure side effects

  • Case II: mining urology clinical data for operation decision making


Smartrule features
SmartRule Features

  • Generate MFIs directly from tabular data

    • Reduce the search space and the support counting time by taking advantage of column structures

  • User select MFIs for rule generation

    • User can select a subset of MFIs to including certain attributes as targets in rule generation

  • Derive rules from targeted MFIs

    • Efficient support-counting by building inverted indices for the collection of itemsets

  • Hierarchically organize rules into trees and use spreadsheet to present the rule trees


System overview of smartrule

Excel Book

TMaxMiner:

Compute MFI from tabular data.

1

3

  • MFI

    • Data

  • Rules

  • Config

Domain experts

2

6

5

4

InvertCount:

- MFIsFIs

- Count sup

RuleTree:

- Generate

- Organize

FI Supports

System overview of SmartRule


Computation complexity
Computation Complexity

  • Efficient MFI mining:

    • Does not require superset checking

    • gather past tail information to determine the next node to explore during the mining process

  • Efficient rule generation:

    • Reduce the computation for support-counting by building inverted indices


Scalability
Scalability

  • Limitation: Microsoft Excel spreadsheet size is 65,536 rows in one spreadsheet

  • When the dataset exceeds the spreadsheet size limit:

    • Partition the dataset into multiple groups of the maximum spreadsheet size to derive MFIs for each spreadsheet

    • Then join these MFIs for generating association rules


Case i mining pregnancy data
Case I: Mining Pregnancy Data

  • Data set: Danish National Birth Cohort (DNBC)

  • Dimension: 4455 patients x 20 attributes

  • Each patient record contain:

    • Exposure status : drug type, timing, and sequence of different drugs

    • Possible confounders: vitamin intake, smoking, alcohol consumption, socio-economic status and psycho-social stress

    • Endpoint: preterm birth, malformations and prenatal complications



Challenges
Challenges

  • Problem: discover side effects of drug exposure during pregnancy

    • E.g.: study how the antidepressants and confounders influence the preterm birth of the new-born

  • Difficulties in finding side effects:

    • Small number of patients suffer side effect

    • Sensitive to the drug exposure time

    • Exposure to sequence of multiple drugs


Derive drug side effects via smartrule 1 low support low confidence rules
Derive Drug Side Effects via SmartRule(1): low-support low-confidence rules

  • Low support or low confidence rules could still be significant because of their contrast to normal pregnant woman

    • For example:

      • If patients exposed to cita in the 3rd trimester, then have preterm birth with support=0.0011, confidence=0.1786

      • If patients not exposured to cita, then have preterm birth with support=0.0433, confidence=0.0444


Derive drug side effects via smartrule 2 temporal sensitive rules
Derive Drug Side Effects via SmartRule(2): temporal sensitive rules

  • Divide the pregnancy period into time slots (e.g. trimester) and combine drug exposure by time:

    • If patients exposed to cita in the 1st trimester and drink alcohol, then have preterm birth with support=0.0011 and confidence=0.132

    • If patients exposed to cita in the 2nd trimester and drink alcohol, then have preterm birth with support=0.0011 and confidence=0.417

    • If patients exposed to cita in the 3rd trimester and drink alcohol, then have preterm birth with support=0.0009 and confidence=0.364

  • Flexible in time slot division, domain user can control granularity


Rule presentation

Hierarchically organize rules into trees sensitive rules

View general rules and then extend to specific rules

Use spreadsheet to present the rule trees

Easy to sort, filter or extend the rule trees to search for the interesting rules

1) In general, patients have preterm birth (sup=0.0454, conf=0.0454)

2) If exposed to cita in the 1st trimester, then preterm birth (sup=0.0016, conf=0.0761)

6) If exposed to cita in the 1st trimester and drink alcohol, then preterm birth (sup=0.0011, conf=0.132)

3) If exposed to cita in the 2nd trimester, then preterm birth (sup=0.0013, conf=0.1714)

7) If exposed to cita in the 2nd trimester and drink alcohol, then preterm birth (sup=0.0011, conf=0.417)

4) If exposed to cita in the 3rd trimester, then preterm birth (sup=0.0011, conf=0.1786)

8) If exposed to cita in the 3rd trimester and drink alcohol, then preterm birth (sup=0.0009, conf=0.364)

5) If no exposure to cita, then preterm birth (sup=0.0433, conf=0.0444)

A part of the rule hierarchy for the exposure to the antidepressant citalopram and alcohol at different time period of pregnancy with preterm birth

Rule Presentation


Knowledge discovery from data mining results
Knowledge Discovery from Data Mining Results sensitive rules

  • Challenges:

    • Examining the vast number of rules manually is too labor-intensive

    • Exploring knowledge (rules) without specific goal


Existing approach top down in rule hierarchy
Existing approach: sensitive rulesTop-down in Rule Hierarchy

  • Association rules are represented in general rules, summaries and exception rules (GSE patterns). The GSE pattern presents the discovered rules in a hierarchical fashion. Users can browse the hierarchy from top-down to find interesting exception rules.

  • Due to the low occurance of drug side effects, interesting rules are exception rules and reside at the lower level of the hierarchy. Without user guidance, it requires exploration of the entire GSE hierarchy to locate the interesting exception rules.

    Reference:

    • B. Liu, M. Hu, and W. Hsu, "Multi-level organization and summarization of the discovered rules," Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug, 2000, Boston, USA.

    • B. Liu, M. Hu, and W. Hsu, "Intuitive representation of decision trees using general rules and exceptions.“ Proceedings of Seventeeth National Conference on Artificial Intellgience (AAAI-2000), July 30 - Aug 3, 2000, Austin, Texas, USA.


New effective bottom up technique to find exception rules
New effective bottom up technique to find exception rules sensitive rules

  • Derive a set of seed attributes from high-confidence rules

    • For example, given high-conf rule:

      If exposed to Anxio in the pre, in and post time and use tobacco and have symptoms of depression, then have preterm birth with confidence = 0.6

    • List of seed attributes: Anxio_pre, Anxio_in, Anxio_post, tobacco and symptoms of depression


Using seed attributes to explore exception rules via rule hierarchy

Seed attributes sensitive rules

High-confidence rule

Rule hierarchy

Rule hierarchy

Using seed attributes to explore exception rules via rule hierarchy

  • Explore more rules based on these seed attributes in the rule hierarchies

    • First look for rules that represent effect of each single seed attribute on preterm birth

    • Then further explore the combination of multiple seed attributes


New findings from data mining

1) In general, patients have sensitive rules preterm birth (sup=0.0454, conf=0.0454)

2) If exposed to cita in the 1st trimester, then preterm birth (sup=0.0016, conf=0.0761)

6) If exposed to cita in the 1st trimester and drink alcohol, then preterm birth (sup=0.0011, conf=0.132)

3) If exposed to cita in the 2nd trimester, then preterm birth (sup=0.0013, conf=0.1714)

7) If exposed to cita in the 2nd trimester and drink alcohol, then preterm birth (sup=0.0011, conf=0.417)

4) If exposed to cita in the 3rd trimester, then preterm birth (sup=0.0011, conf=0.1786)

8) If exposed to cita in the 3rd trimester and drink alcohol, then preterm birth (sup=0.0009, conf=0.364)

5) If no exposure to cita, then preterm birth (sup=0.0433, conf=0.0444)

New Findings from Data Mining

  • Finding: combined exposure to citalopram and alcohol in pregnancy is associated with an increased risk of preterm birth

  • Not initially discovered by epidemiology study due to the large number of combinations among all the attributes and their values


Statistical analysis vs data mining

Statistical analysis sensitive rules

Infeasible to test all potential hypotheses for large number of attributes

Testing hypotheses with small sample size has limited statistical power

Statistical Analysis VS. Data Mining

  • Data mining

  • No hypothesis, mine association in large dataset with multiple temporal attributes

  • Can generate association rules independent of the sample size

  • Derive rules with temporal information of drug exposure


Case ii mining urology clinical data
Case II: sensitive rulesMining Urology Clinical Data

  • Data set: urology surgeries operated during 1995 to 2002 at the UCLA Pediatric Urology Clinic

  • Dimension: 130 patients x 28 attributes



Training data attributes
Training Data Attributes sensitive rules

  • Each patient record contain:

    • Pre-operative conditions:

      • Demography data: age, gender, etc.

      • patient ambulatory status (A)

      • catheterizing skills (CS)

      • amount of creatinine in the blood (SerumCrPre)

      • leak point pressure (LPP)

      • urodynamics, such as the minimum volume of saline infused into a bladder when its pressure reached 20 cm of water (20%min)

    • Type of surgery performed:

      • Op-1 Bladder Neck Reconstruction with Augmentation

      • Op-2 Bladder Neck Reconstruction without Augmentation

      • Op-3 Bladder Neck Closure without Augmentation

      • Op-4 Bladder Neck Closure with Augmentation

    • Post-op complications: infection, complication, etc.

    • Final outcome of the surgery: urine continence  wet or dry



Goals and challenges
Goals and Challenges sensitive rules

  • Goal:

    • Derive a set of rules from the clinical data set (training set) that summarize the outcome based on patients’ pre-op data

    • Predict operation outcome based on a given patient’s pre-op data (test set), and recommend the best operation to perform

  • Challenge:

    • Small sample size, large number of attributes

    • Continuous-value attributes such as uro-dynamics measurements


Data mining steps
Data Mining Steps sensitive rules

  • 1. Separate the patients into four groups based on their type of surgery performed

  • 2. In each group, partition the continuous value attributes into discrete intervals or cells. Since the sample size is very small, we use a hybrid technique to determine the optimal number of cells and cell sizes.

  • 3. Generate association rules for each patient group based on the partitioned continues value attributes

  • 4. For a given patient with a specific set of pre-op conditions, the generated rules from the training set can be used to predict success or failure rate for a specific operation


Partitioning continuous value attributes
Partitioning Continuous Value Attributes sensitive rules

  • Current approach to partition continuous attribute:

    • Using domain expert guidance can be biased and inconsistent

    • Statistical clustering technique fails when the training set size is small and the number of attributes is large

  • New hybrid approach:

    • Using data mining technique to select a small set of key attributes

    • Using statistical classification technique to perform the optimal partition (determine the cell sizes and the number of cells) from the small set of key attributes


Hybrid clustering technique
Hybrid Clustering Technique sensitive rules

  • Select a small key attribute set (via data mining):

    • Use domain expert partition to perform mining on the training set

    • Select a set of key attributes that contribute to high confidence and support rules

  • Optimal partition (via statistical classification)

    • Use statistical classification techniques (e.g. CART) to determine the optimal number of cells and their corresponding cell sizes for the attributes

      Mining optimally partitioned attribute data yields better quality rules


Partition of continuous variables for operations
Partition of continuous variables for operations sensitive rules

  • Partition of continuous variables into optimal number of discrete intervals (cells) and cell sizes for four types of operations.

Operation Type 1

Operation Type 2

Operation Type 3

Operation Type 4


Recommending operation based on rules derived from training set
Recommending operation based on rules derived from training set

  • Transform the patient’s pre-op data of the continues value attributes using the optimal partitions for each operation

  • Find a set of rules (from the training set) that matches the patients’ pre-op data

  • Compare the matched rules from each operation, recommend the type of sugary that provides the best match


Example prediction for matt
Example: Prediction for Matt set

Patient Matt’s pre-operative conditions

Discretized pre-operative conditions of patient Matt’s pre-op conditions. The attributes not used in rule generation are denoted as n/a


Rule trees selected from the knowledge base that match patient matt s pre op profile
Rule trees selected from the knowledge base that match patient Matt’s pre-op profile

Based on the rule tree, we note that Operations 3 and 4 both match patient Matt’s pre-op conditions. However, Operation 4 matches more attributes in Matt’s pre-op conditions than Operation 3. Thus, Operation 4 is more desirable for patient Matt.


Representing rules in a hierarchical structure

sup=32.55%,conf=0.78 patient Matt’s pre-op profile

A4Success

sup=25.58%,conf=0.79

A4CS1Success

sup=18.6%,conf=0.8

A4CS1Lpp2Success

sup=13.95%,conf=1

A4CS1M1Success

sup=13.95%,conf=1

A4CS1M1Lpp2Success

Representing rules in a hierarchical structure

  • Favorable user feedback in using the spreadsheet interface because of its ease in rule searching and sorting

Represent rule trees for Op-4 by spreadsheet

Rule tree for Op-4


Lesson learn from mining data with small sample size
Lesson learn from mining data with small sample size patient Matt’s pre-op profile

  • For small sample size, hybrid clustering yield better than conventional unsupervised clustering techniques

  • Hybrid clustering enables us to generate useful rules for small sample sizes, which could not be done using data mining or statistical classifying methods alone


Conclusion
Conclusion patient Matt’s pre-op profile

  • Mining pregnancy data:

    • Discover drug exposure side effects (association)

    • Advantage over traditional statistical approaches:

      • Independent of hypotheses

      • Independent of the sample size

      • Derive rules with temporal information

    • Using seed attribute approach to effectively discover exception rules via rule hierarchy

  • Mining urology clinical data:

    • Deriving association rules based on patient’s pre-op conditions and their operation outcomes according to different type of operations

    • Hybrid clustering technique to derive optimal partition for continuous value attributes . This technique is critical for deriving high quality rules for small sample size with large number of attributes


Reference
Reference patient Matt’s pre-op profile

  • Qinghua Zou, Yu Chen, Wesley W. Chu and Xinchun Lu. Mining association rules from tabular data guided by maximal frequent itemset. Book Chapter in “Foundations and Advances in Data Mining”, edited by Wesley W. Chu and T.Y. Lin, Springer, 2005.

  • Yu Chen, Lars Henning Pedersen, Wesley W. Chu and Jorn Olsen. "Drug Exposure Side Effects from Mining Pregnancy Data" In SIGKDD Explorations (Volume 9, Issue 1), June 2007, Special Issue on Data Mining for Health Informatics, Guest Editors: Raymond Ng and Jian Pei .

  • Q. Zou, W.W. Chu, and B. Lu. SmartMiner: A depth-first search algorithm guided by tail information for mining maximal frequent itemsets. In Proc. of the IEEE Intl. Conf. on Data Mining, 2002.

  • R. Agrawal and R. Srikant: Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994.

  • D. Burdick, M. Calimlim, and J. Gehrke: MAFIA: a maximal frequent itemset algorithm for transactional databases. In Intl. Conf. on Data Engineering, Apr. 2001.

  • K. Gouda and M.J. Zaki: Efficiently Mining Maximal Frequent Itemsets. Proc. of the IEEE Int. Conference on Data Mining, San Jose, 2001.


Reference1
Reference patient Matt’s pre-op profile

  • B. Liu, M. Hu, and W. Hsu, "Multi-level organization and summarization of the discovered rules," Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug, 2000, Boston, USA.

  • B. Liu, M. Hu, and W. Hsu, "Intuitive representation of decision trees using general rules and exceptions.“ Proceedings of Seventeeth National Conference on Artificial Intellgience (AAAI-2000), July 30 - Aug 3, 2000, Austin, Texas, USA.

  • Frequent Itemset Mining Implementations Repository, http://fimi.cs.helsinki.fi/

  • http://www.ics.uci.edu/~mlearn/MLRepository.html


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