The ability to identify smokers from the general population is important to several health studies. One such initiative was organized by the
Informatics for Integrating Biology to the Bedside (i2b2) project, as described in
this article. The objective is to build a model that will predict the smoking status for a previously unseen patient discharge note. Seemed to be an interesting problem, so I thought I'll try my hand at it. This post describes that effort.
Input
The dataset is provided as 3 XML files, each corresponding to the training, evaluation (ground-truth) and testing (submission) datasets. Since I am not participating in the challenge, I am only interested in the first two. The training set has 398 patient records and the evaluation set has 104. In both cases, the record is annotated with one of 5 smoking statuses - CURRENT SMOKER, PAST SMOKER, NON-SMOKER, SMOKER and UNKNOWN. You can get these datasets
here after registering and signing an end-user NDA.
The files have the following structure.
| <ROOT>
<RECORD ID="123">
<SMOKING STATUS="CURRENT_SMOKER"></SMOKING>
<TEXT>
Plain text with sentences and non-sentences. Sentences are terminated
with period (.).
</TEXT>
</RECORD>
... more records ...
</ROOT>
|
Prior Work
Since the experiment is as much an attempt to educate as entertain myself, I decided to take a look at
Apache cTakes and
Hitex, two popular open-source projects that I know of that analyze and extract information from data in Clinical pipelines, and see how they are doing it. From a limited scan of the code, both of them seem to use each sentence in the (multi-sentence) record for training, setting the target variable for each sentence as the SMOKING@STATUS value for the entire record. At test/prediction time, individual sentences are passed in and the class predicted. Presumably the smoking status attributed to the full discharge note is the maximally predicted class across the sentences (other than UNKNOWN). Both systems use Support Vector Machine (SVM) classifiers.
My Approach - Overview
Although the approach seemed to address the problem of extremely wide data (many more features than examples), I felt that it diluted the value of the smoking annotation, since only a few sentences in the entire record would specifically indicate the status.
So my first attempt was to build a multi-class classifier that treated each record as a training row. I experimented with various classification algorithms and various sizes of feature sets using the
Weka GUI. My best model had an accuracy of 91.21% on the training data (indicating that it wasn't underfitting) and an accuracy of 71.15% on the evaluation data.
Hoping to do better, my second attempt exploits the structure of the target variable to first decide smoking vs non-smoking, then sends the data down two other classifiers which decide the type of smoker or non-smoker. Overall results was slightly better, with an accuracy of 95.48% against training data and 73.08% accuracy against evaluation data. The top level classifier in the stack to detect smoker vs non-smoker did considerably better, achieving an accuracy of 96.23% against training data and 84.62% against evaluation data.
In both cases, the input XML files was read by a parser to extract the smoking status and the sentences from the text. The records were written out into ARFF files (Weka's standard input format). Here is the code for parsing the XML files and writing out ARFF files. The buildMultiClassArff() method builds the ARFF file for the first approach and the buildSmokerNonSmokerArff() and buildSubClassifierArffs() methods are used for the second approach.
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106 | // Source: src/main/scala/com/mycompany/scalcium/smoke/ArffBuilder.scala
package com.mycompany.scalcium.smoke
import java.io.File
import java.io.PrintWriter
import java.io.FileWriter
class ArffBuilder {
def buildMulticlassArff(xmlin: File,
arffout: File): Unit = {
val preprocessor = new Preprocessor()
val arffWriter = openWriter(arffout)
writeHeader(arffWriter, preprocessor.multiClassTargets)
val rootElem = scala.xml.XML.loadFile(xmlin)
(rootElem \ "RECORD").map(record => {
val smoking = (record \ "SMOKING" \ "@STATUS").text
val text = (record \ "TEXT").text
.split("\n")
.filter(line => line.endsWith("."))
.map(line => preprocessor.preprocess(line))
.mkString(" ")
writeData(arffWriter, smoking, text,
preprocessor.multiClassTargets)
})
closeWriter(arffWriter)
}
def buildSmokerNonSmokerArff(xmlin: File,
arffout: File): Unit = {
val preprocessor = new Preprocessor()
val arffWriter = openWriter(arffout)
writeHeader(arffWriter,
preprocessor.smokerNonSmokerTargets)
val rootElem = scala.xml.XML.loadFile(xmlin)
(rootElem \ "RECORD").map(record => {
val smoking = (record \ "SMOKING" \ "@STATUS").text
val text = (record \ "TEXT").text
.split("\n")
.filter(line => line.endsWith("."))
.map(line => preprocessor.preprocess(line))
.mkString(" ")
writeData(arffWriter, smoking, text,
preprocessor.smokerNonSmokerTargets)
})
closeWriter(arffWriter)
}
def buildSubClassifierArrfs(xmlin: File,
smokSubArff: File, nonSmokArff: File): Unit = {
val preprocessor = new Preprocessor()
val smokArffWriter = openWriter(smokSubArff)
val nonSmokArffWriter = openWriter(nonSmokArff)
writeHeader(smokArffWriter,
preprocessor.smokerSubTargets)
writeHeader(nonSmokArffWriter,
preprocessor.nonSmokerSubTargets)
val rootElem = scala.xml.XML.loadFile(xmlin)
(rootElem \ "RECORD").map(record => {
val smoking = (record \ "SMOKING" \ "@STATUS").text
val text = (record \ "TEXT").text
.split("\n")
.filter(line => line.endsWith("."))
.map(line => preprocessor.preprocess(line))
.mkString(" ")
if (preprocessor.smokerNonSmokerTargets(smoking) == 1)
writeData(smokArffWriter, smoking, text,
preprocessor.smokerSubTargets)
else
writeData(nonSmokArffWriter, smoking, text,
preprocessor.nonSmokerSubTargets)
})
closeWriter(smokArffWriter)
closeWriter(nonSmokArffWriter)
}
def openWriter(f: File): PrintWriter =
new PrintWriter(new FileWriter(f))
def writeHeader(w: PrintWriter,
targets: Map[String,Int]): Unit = {
val classes = targets.map(kv => kv._2)
.toSet
.toList
.sortWith(_ < _)
.mkString(",")
w.println("@relation smoke")
w.println()
w.println("@attribute class {%s}".format(classes))
w.println("@attribute text string")
w.println()
w.println("@data")
}
def writeData(w: PrintWriter,
smoking: String, body: String,
targets: Map[String,Int]): Unit = {
w.println("%d,\"%s\"".format(
targets(smoking), body))
}
def closeWriter(w: PrintWriter): Unit = {
w.flush()
w.close()
}
}
|
Each sentence in the text was normalized by lowercasing and removing punctuations and stopwords. The preprocessing logic is kept in a separate class because the test data needs to be preprocessed in the same way during prediction/evaluation time. Here is the code for the preprocessor.
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54 | // Source: src/main/scala/com/mycompany/scalcium/smoke/Preprocessor.scala
package com.mycompany.scalcium.smoke
import java.util.regex.Pattern
import org.apache.lucene.analysis.core.StopAnalyzer
import com.mycompany.scalcium.utils.Tokenizer
class Preprocessor {
val punctPattern = Pattern.compile("\\p{Punct}")
val spacePattern = Pattern.compile("\\s+")
val classPattern = Pattern.compile("class")
val stopwords = StopAnalyzer.ENGLISH_STOP_WORDS_SET
val tokenizer = Tokenizer.getTokenizer("opennlp")
val multiClassTargets = Map(
("CURRENT SMOKER", 0),
("NON-SMOKER", 1),
("PAST SMOKER", 2),
("SMOKER", 3),
("UNKNOWN", 4))
val smokerNonSmokerTargets = Map(
("CURRENT SMOKER", 1),
("PAST SMOKER", 1),
("SMOKER", 1),
("NON-SMOKER", 0),
("UNKNOWN", 0))
val smokerSubTargets = Map(
("CURRENT SMOKER", 0),
("PAST SMOKER", 1),
("SMOKER", 2))
val nonSmokerSubTargets = Map(
("NON-SMOKER", 0),
("UNKNOWN", 1))
def preprocess(sin: String): String = {
val sinClean = replaceAll(classPattern, "clazz",
replaceAll(spacePattern, " ",
replaceAll(punctPattern, " ", sin.toLowerCase())))
// stopword removal
val sinStp = tokenizer.wordTokenize(sinClean)
.filter(word => !stopwords.contains(word))
.mkString(" ")
sinStp
}
def replaceAll(pattern: Pattern,
replacement: String,
input: String): String = {
pattern.matcher(input)
.replaceAll(replacement)
}
}
|
The ARFF file so created were vectorized and binarized from the Weka GUI using Weka's built in filters - the StringToWordVector for vectorizing the text and NumericToBinary for binarizing the resulting word vector. The resulting preprocessed model was saved in a vectorized ARFF which was tested using 3-fold cross validation against various built-in Weka classifiers.
The winning model(s) were finally evaluated against the training data (to verify that we don't have an underfitting model) and the evaluation data (to verify the "true" quality of the solution). Since we have to vectorize and binarize the evaluation data, we need access to the original features, so our evaluation process first uses the training data to train the classifier and uses the trained classifier to predict the target variable for the evaluation set. The code to do this is shown below - the evaluate() method does this for the first approach and the evaluateStacked() does this for the second approach.
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146 | // Source: src/main/scala/com/mycompany/scalcium/smoke/SmokingClassifier.scala
package com.mycompany.scalcium.smoke
import java.io.BufferedReader
import java.io.File
import java.io.FileReader
import scala.Array.canBuildFrom
import weka.classifiers.trees.J48
import weka.core.Attribute
import weka.core.Instances
import weka.core.SparseInstance
import weka.classifiers.Classifier
import weka.classifiers.functions.SMO
import weka.classifiers.rules.PART
import weka.core.Instance
case class TrainedModel(classifier: Classifier,
dataset: Instances,
vocab: Map[String,Int])
class SmokingClassifier(arffIn: File,
smokingArffIn: File = null,
nonSmokingArffIn: File = null) {
val preprocessor = new Preprocessor()
val trainedModel = trainJ48(arffIn)
val trainedSmokSubModel = trainSMO(smokingArffIn)
val trainedNonSmokSubModel = trainPART(nonSmokingArffIn)
def evaluate(testfile: File,
targets: Map[String,Int],
model: TrainedModel): Double = {
var numTested = 0D
var numCorrect = 0D
val rootElem = scala.xml.XML.loadFile(testfile)
(rootElem \ "RECORD").map(record => {
val smoking = (record \ "SMOKING" \ "@STATUS").text
val text = (record \ "TEXT").text
.split("\n")
.filter(line => line.endsWith("."))
.map(line => preprocessor.preprocess(line))
.mkString(" ")
val ypred = predict(text, model)
numTested += 1D
if (ypred == targets(smoking))
numCorrect += 1D
})
100.0D * numCorrect / numTested
}
def evaluateStacked(testfile: File,
topModel: TrainedModel,
nonSmokingSubModel: TrainedModel,
smokingSubModel: TrainedModel): Double = {
var numTested = 0D
var numCorrect = 0D
val rootElem = scala.xml.XML.loadFile(testfile)
(rootElem \ "RECORD").map(record => {
val smoking = (record \ "SMOKING" \ "@STATUS").text
val text = (record \ "TEXT").text
.split("\n")
.filter(line => line.endsWith("."))
.map(line => preprocessor.preprocess(line))
.mkString(" ")
val topPred = predict(text, topModel)
if (topPred == 1) { // smoking
val subPred = predict(text, smokingSubModel)
if (preprocessor.smokerSubTargets.contains(smoking) &&
subPred == preprocessor.smokerSubTargets(smoking))
numCorrect += 1
} else { // non-smoking
val subPred = predict(text, nonSmokingSubModel)
if (preprocessor.nonSmokerSubTargets.contains(smoking) &&
subPred == preprocessor.nonSmokerSubTargets(smoking))
numCorrect += 1
}
numTested += 1
})
100.0D * numCorrect / numTested
}
/////////////////// predict ////////////////////
def predict(input: String,
model: TrainedModel): Int = {
val inst = buildInstance(input, model)
val pdist = model.classifier.distributionForInstance(
inst)
pdist.zipWithIndex.maxBy(_._1)._2
}
def buildInstance(input: String,
model: TrainedModel): Instance = {
val inst = new SparseInstance(model.vocab.size)
input.split(" ")
.foreach(word => {
if (model.vocab.contains(word)) {
inst.setValue(model.vocab(word), 1)
}
})
inst.setDataset(model.dataset)
inst
}
/////////////////// train models //////////////////
def trainJ48(arff: File): TrainedModel = {
trainModel(arff, new J48())
}
def trainSMO(arff: File): TrainedModel = {
if (arff == null) null
else {
val smo = new SMO()
smo.setC(0.1D)
smo.setToleranceParameter(0.1D)
trainModel(arff, smo)
}
}
def trainPART(arff: File): TrainedModel = {
if (arff == null) null
else trainModel(arff, new PART())
}
def trainModel(arff: File,
classifier: Classifier): TrainedModel = {
val reader = new BufferedReader(new FileReader(arff))
val _instances = new Instances(reader)
reader.close()
_instances.setClassIndex(0)
val _vocab = scala.collection.mutable.Map[String,Int]()
val e = _instances.enumerateAttributes()
while (e.hasMoreElements()) {
val attrib = e.nextElement().asInstanceOf[Attribute]
if (! "class".equals(attrib.name())) {
// replace the _binarized suffix
val stripname = attrib.name().replace("_binarized", "")
_vocab += ((stripname, attrib.index()))
}
}
classifier.buildClassifier(_instances)
TrainedModel(classifier, _instances, _vocab.toMap)
}
}
|
Finally, I used this JUnit test class to selectively call methods as I progressed through the experiment.
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82 | // Source: src/test/scala/com/mycompany/scalcium/smoke/SmokingClassifierTest.scala
package com.mycompany.scalcium.smoke
import org.junit.Test
import java.io.File
import org.apache.commons.io.FileUtils
import java.util.regex.Pattern
class SmokingClassifierTest {
val datadir = new File("/path/to/my/data")
val trainfile = new File(datadir, "smokers_surrogate_train_all_version2.xml")
val testfile = new File(datadir, "smokers_surrogate_test_all_groundtruth_version2.xml")
val preprocessor = new Preprocessor()
@Test
def testBuildMultiClassArff(): Unit = {
val ab = new ArffBuilder()
ab.buildMulticlassArff(trainfile,
new File(datadir, "smoke_mc.arff"))
}
@Test
def testBuildSmokerNonSmokerArff(): Unit = {
val ab = new ArffBuilder()
ab.buildSmokerNonSmokerArff(trainfile,
new File(datadir, "smoke_sns.arff"))
}
@Test
def testBuildSubclassifierArffs(): Unit = {
val ab = new ArffBuilder()
ab.buildSubClassifierArrfs(trainfile,
new File(datadir, "smoke_subs.arff"),
new File(datadir, "smoke_subn.arff"))
}
@Test
def testEvaluateMultiClass(): Unit = {
val sc = new SmokingClassifier(
new File(datadir, "smoke_mc_vectorized.arff"))
val trainResult = sc.evaluate(trainfile,
preprocessor.multiClassTargets,
sc.trainedModel)
Console.println("training accuracy=%f".format(trainResult))
val testResult = sc.evaluate(testfile,
preprocessor.multiClassTargets,
sc.trainedModel)
Console.println("test accuracy=%f".format(testResult))
}
@Test
def testEvaluateSmokerNonSmoker(): Unit = {
val sc = new SmokingClassifier(
new File(datadir, "smoke_sns_vectorized.arff"))
val trainResult = sc.evaluate(trainfile,
preprocessor.smokerNonSmokerTargets,
sc.trainedModel)
Console.println("training accuracy=%f".format(trainResult))
val testResult = sc.evaluate(testfile,
preprocessor.smokerNonSmokerTargets,
sc.trainedModel)
Console.println("test accuracy=%f".format(testResult))
}
@Test
def testEvaluateStackedClassifier(): Unit = {
val sc = new SmokingClassifier(
new File(datadir, "smoke_sns_vectorized.arff"),
new File(datadir, "smoke_subs_vectorized.arff"),
new File(datadir, "smoke_subn_vectorized.arff"))
val trainResult = sc.evaluateStacked(trainfile,
sc.trainedModel, sc.trainedNonSmokSubModel,
sc.trainedSmokSubModel)
Console.println("training accuracy=%f".format(trainResult))
val testResult = sc.evaluateStacked(testfile,
sc.trainedModel, sc.trainedNonSmokSubModel,
sc.trainedSmokSubModel)
Console.println("test accuracy=%f".format(testResult))
}
}
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Analysis and Results - Approach #1
I tested 5 different (Weka built-in) classifiers against different sizes of the word vector. By default, StringToWordVector creates a vector from the top 1000 word features. I varied this from 1000 to 5000 for each algorithm and plotted the results. The data and the corresponding plot is shown below:
| #-words | NaiveBayes | SMO | J48 | RandomForest | PART |
|---|
| 1000w | 57.29 | 67.59 | 70.85 | 61.81 | 65.08 |
| 2000w | 59.55 | 67.84 | 79.9 | 62.81 | 76.63 |
| 3000w | 59.55 | 66.33 | 79.15 | 63.56 | 76.38 |
| 4000w | 61.81 | 66.58 | 78.64 | 60.3 | 75.62 |
| 5000w | 61.31 | 67.34 | 76.13 | 62.81 | 73.37 |
As can be seen, accuracy peaks for vector size of 2000 words for all the classifiers, and the best results come from the
J48 (and to a lesser extent, the
PART) classifier, so we continue with these going forward.
My next improvement was to remove stop words. Accuracy for J48 went up to 79.40% for an input vector size of 2000 words. I then tried parsing the sentences with OpenNLP to extract noun phrases and use them as features instead of words - the sweet spot moves up to 3000 words but the accuracy is 61.56%, lower than our previous experiment, so the extra effort of doing noun-phrase chunking doesn't seem to be worth it.
The "best" classifier so far was the J48 classifier with 2000 words and stopwords removed. The confusion matrix for the classifier is shown below. Here non-smokers, past-smokers and unknowns are being classified the best, current-smoker is being misclassified quite frequently as non-smoker and past-smoker.
| Confusion Matrix
-------------------
a b c d e <-- classified as
16 5 10 1 3 | a = 0 (CURRENT_SMOKER)
10 47 4 0 5 | b = 1 (NON-SMOKER)
10 5 19 1 1 | c = 2 (PAST SMOKER)
3 1 1 0 4 | d = 3 (SMOKER)
8 7 1 0 236 | e = 4 (UNKNOWN)
|
The pruned tree for the J48 classifier looks like this. I edited out the _binarized suffixes from the tree labels to make it more readable. But what is interesting is that the classifier uses words such as tobacco, smoker, smokes, etc to figure out the target variable, much the same way a human reader would.
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53 | J48 pruned tree
------------------
quit = 0
| pack = 0
| | smoke = 0
| | | tobacco = 0
| | | | smoking = 0
| | | | | smoker = 0
| | | | | | drinks = 0
| | | | | | | married = 0: 5 (253.0/8.0)
| | | | | | | married = 1
| | | | | | | | bid = 0: 5 (5.0/1.0)
| | | | | | | | bid = 1: 2 (3.0)
| | | | | | drinks = 1: 1 (2.0/1.0)
| | | | | smoker = 1
| | | | | | 100 = 0
| | | | | | | 0 = 0: 1 (3.0)
| | | | | | | 0 = 1: 3 (3.0)
| | | | | | 100 = 1: 2 (3.0)
| | | | smoking = 1
| | | | | 68 = 0
| | | | | | aspirin = 0
| | | | | | | feeling = 0
| | | | | | | | 4 = 0
| | | | | | | | | i = 0: 1 (7.0)
| | | | | | | | | i = 1: 2 (2.0)
| | | | | | | | 4 = 1: 2 (7.0)
| | | | | | | feeling = 1: 3 (2.0)
| | | | | | aspirin = 1: 3 (3.0)
| | | | | 68 = 1: 4 (2.0)
| | | tobacco = 1
| | | | dilatation = 0
| | | | | possibility = 0
| | | | | | former = 0
| | | | | | | passed = 0: 2 (29.0/1.0)
| | | | | | | passed = 1: 3 (2.0/1.0)
| | | | | | former = 1: 3 (2.0)
| | | | | possibility = 1: 3 (2.0/1.0)
| | | | dilatation = 1: 1 (2.0)
| | smoke = 1: 2 (21.0/1.0)
| pack = 1
| | central = 0
| | | 10 = 0: 3 (4.0/1.0)
| | | 10 = 1: 1 (16.0/1.0)
| | central = 1: 5 (2.0)
quit = 1
| added = 0
| | day = 0
| | | 0 = 0: 1 (2.0)
| | | 0 = 1: 3 (2.0)
| | day = 1: 3 (17.0)
| added = 1: 1 (2.0)
|
As mentioned earlier, the performance of this classifier had an accuracy of 91.21% against the training set and 71.15% against the evaluation set.
Analysis and Results - Approach #2
Something I noticed about the annotations is that they are somewhat ad-hoc, ie, SMOKER could mean CURRENT or PAST SMOKER, but is a category by itself. UNKNOWN is a bit tricky, we could assume that the smoking status was not considered because the patient was
obviously a non-smoker, or the clinician neglected to record it. So we could think of the annotations as being structured - the top split would be SMOKER and NON-SMOKER. Within SMOKER would be CURRENT SMOKER, PAST SMOKER and SMOKER, and within NON-SMOKER would be UNKNOWN and NON-SMOKER.
So I decided to use a classifier to first determine SMOKER vs NON-SMOKER, then based on the results of that send it to a second classifier to determine the "type" of SMOKER or NON-SMOKER.
Once again, I rebuilt the ARFF file for the top level classifier, then used the Weka GUI to vectorize and binarize the text, and tried the J48 and PART classifiers against different word vector sizes. The data and plot are shown below. Corresponding accuracies were 96.23% against the training set and 84.62% against the evaluation test - so we can be pretty accurate in predicting smoker vs non-smoker from the discharge notes.
| #-words | J48 | PART |
|---|
| 1000w | 84.67 | 79.65 |
| 2000w | 87.69 | 83.64 |
| 3000w | 84.92 | 84.17 |
| 4000w | 85.16 | 84.17 |
| 5000w | 86.68 | 85.43 |
Once more, the winner appears to be J48 with a 2000 word vector. We then build two sub-ARFF files one for the SMOKER sub-classes, and one for the NON-SMOKER subclasses. For each ARFF file, I tested various classifiers with word vector size 2000.
For the smoking category, the best performer was
SMO with c=0.1 and toleranceParameter=0.1. 3-fold cross validation gave 57.5% accuracy (a slight bump from 55% with the default parameters, although this did not affect the accuracy of the final stacked classifier).
For the non-smoking category, the best performer was PART with an accuracy of 87.73% with 3-fold cross validation.
The overall accuracy against the training set was 95.48% and 73.08% against the evaluation set. Overall accuracy was calculated by sending each record through the classifier stack and comparing the final prediction with the original.
Acknowledgements
- Datasets for this experiment was provided by i2b2, for which I am very thankful. i2b2 also requires users of the datasets to acknowledge them with the following boilerplate below:
"Deidentified clinical records used in this research were provided by the i2b2 National Center for Biomedical Computing funded by U54LM008748 and were originally prepared for the Shared Tasks for Challenges in NLP for Clinical Data organized by Dr. Ozlem Uzuner, i2b2 and SUNY."
- For generating all the models, I used the Weka data mining toolkit, without which I would not have been able to try out and compare so many models in the time I took. Weka is required to be cited as follows:
Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
Additionally, as a result of the NDA with i2b2, I am forbidden from sharing the contents of the datasets I used (even for showing snippets for illustrative purposes). If you want the data, you will need to register and sign the NDA yourself.
Also, as a condition for using Weka (since it is
GPL), my model and source code must be made freely available for other people to use - the ARFF files with (vectorized and binarized with top 2000 features) data corresponding to the original multiclass classifier and the three classifiers comprising the stacked classifier can be found
on GitHub here. All the source code used in this experiment is available on this page.
In addition, here are some links for documents that I found invaluable when building this solution.
