Automating reviews in medicine
Source: Analytics Edge Unit 5 Text Analytics Homework
Techniques involved: text mining,CART, ROC curve
The medical literature is enormous and is growing at an amazing speed and therefore has increased the need for reviews, which search databases for papers on a certain topic and then report results from the papers found. While such reviews are often performed manually, with multiple people reviewing each search result, this is tedious and time consuming. In this problem, we will see how text analytics can be used to automate the process of information retrieval.
The dataset consists of the titles (variable title) and abstracts (variable abstract) of papers retrieved in a Pubmed search. Each search result is labeled with whether the paper is a clinical trial testing a drug therapy for cancer (variable trial). These labels were obtained by two people reviewing each search result and accessing the actual paper if necessary, as part of a literature review of clinical trials testing drug therapies for advanced and metastatic breast cancer.
Load the data
setwd("C:/Users/jzchen/Documents/Courses/Analytics Edge/Unit_5_Text_analytics")
trials <- read.csv("clinical_trial.csv", stringsAsFactors = FALSE)
str(trials)## 'data.frame': 1860 obs. of 3 variables:
## $ title : chr "Treatment of Hodgkin's disease and other cancers with 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU; NSC-409962)." "Cell mediated immune status in malignancy--pretherapy and post-therapy assessment." "Neoadjuvant vinorelbine-capecitabine versus docetaxel-doxorubicin-cyclophosphamide in early nonresponsive breast cancer: phase "| __truncated__ "Randomized phase 3 trial of fluorouracil, epirubicin, and cyclophosphamide alone or followed by Paclitaxel for early breast can"| __truncated__ ...
## $ abstract: chr "" "Twenty-eight cases of malignancies of different kinds were studied to assess T-cell activity and population before and after in"| __truncated__ "BACKGROUND: Among breast cancer patients, nonresponse to initial neoadjuvant chemotherapy is associated with unfavorable outcom"| __truncated__ "BACKGROUND: Taxanes are among the most active drugs for the treatment of metastatic breast cancer, and, as a consequence, they "| __truncated__ ...
## $ trial : int 1 0 1 1 1 0 1 0 0 0 ...Explanatory analysis
We can use R’s string functions to learn more about the titles and abstracts of the located papers. The nchar() function counts the number of characters in a piece of text. Using the nchar() function on the variables in the data frame, answer the following questions:
How many characters are there in the longest abstract? (Longest here is defined as the abstract with the largest number of characters.)
max(nchar(trials$abstract))## [1] 3708How many search results provided no abstract? (HINT: A search result provided no abstract if the number of characters in the abstract field is zero.)
nrow(subset(trials, nchar(abstract)==0))## [1] 112Find the observation with the minimum number of characters in the title (the variable “title”) out of all of the observations in this dataset. What is the text of the title of this article?
trials$title[which.min(nchar(trials$title))]## [1] "A decade of letrozole: FACE."preparing the corpus
library(tm)## Loading required package: NLPcorpusTitle <- Corpus(VectorSource(trials$title))
corpusAbstract <- Corpus(VectorSource(trials$abstract))pre-process the corpus
Convert to lowercase
corpusTitle <- tm_map(corpusTitle, tolower)
corpusAbstract <- tm_map(corpusAbstract, tolower)The following step is necessary
corpusTitle <- tm_map(corpusTitle, PlainTextDocument)
corpusAbstract <- tm_map(corpusAbstract, PlainTextDocument)If you don’t include the above command, you will get the following error
dtmTitle <- DocumentTermMatrix(corpusTitle) Error: inherits(doc, "TextDocument") is not TRUE Remove punctuations
corpusTitle <- tm_map(corpusTitle, removePunctuation)
corpusAbstract <- tm_map(corpusAbstract, removePunctuation)Remove stop words
corpusTitle <- tm_map(corpusTitle, removeWords, stopwords("english"))
corpusAbstract <- tm_map(corpusAbstract, removeWords, stopwords("english"))Stem the words
corpusTitle <- tm_map(corpusTitle, stemDocument)
corpusAbstract <- tm_map(corpusAbstract, stemDocument)Build a document term matrix
dtmTitle <- DocumentTermMatrix(corpusTitle)
dtmAbstract <- DocumentTermMatrix(corpusAbstract)Limit dtmTitle and dtmAbstract to terms with sparseness of at most 95%
dtmTitle <- removeSparseTerms(dtmTitle, 0.95)
dtmAbstract <- removeSparseTerms(dtmAbstract, 0.95)Convert them to data frames
dtmTitle <- as.data.frame(as.matrix(dtmTitle))
dtmAbstract <- as.data.frame(as.matrix(dtmAbstract))Note that above we keep the names of dtmTitle and dtmAbstract
How many terms remain in dtmTitle after removing sparse terms (aka how many columns does it have)?
dim(dtmTitle)## [1] 1860 31dim(dtmAbstract)## [1] 1860 335What is the most frequent word stem across all the abstracts? Hint: you can use colSums() to compute the frequency of a word across all the abstracts.
which.max(colSums(dtmAbstract))## patient
## 212We want to combine dtmTitle and dtmAbstract into a single data frame. However, some of the variables in these data frames have the same names. To fix this issue, run the following commands:
colnames(dtmTitle) = paste0("T", colnames(dtmTitle))
colnames(dtmAbstract) = paste0("A", colnames(dtmAbstract))Rebuild the data frame
dtm <- cbind(dtmTitle, dtmAbstract)## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
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Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 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the dependent variable to dtm
dtm$trial <- trials$trialSplit dataset
Split the data
library(caTools)
set.seed(144)
split <- sample.split(dtm$trial, SplitRatio = 0.7)
train <- subset(dtm, split == TRUE)
test <- subset(dtm, split == FALSE)Build a CART model
library(rpart)
library(rpart.plot)
trialCART <- rpart(trial~., data = train, method = "class")
prp(trialCART)
Obtain the training set predictions for the model
train_predict <- predict(trialCART, type = "class")
table(train$trial, train_predict)## train_predict
## 0 1
## 0 631 99
## 1 131 441Training set accuracy is 0.8233487
(631+441)/nrow(train)## [1] 0.8233487The other way to evaluate the model
train_predict_2 <- predict(trialCART)
table(train$trial, train_predict_2[,2] >= 0.5)##
## FALSE TRUE
## 0 631 99
## 1 131 441This will obtain the same result.
Evaluate model
Evaluate the model on the test set
test_predict <- predict(trialCART, newdata = test, type = "class")
table(test$trial, test_predict)## test_predict
## 0 1
## 0 261 52
## 1 83 162Model accuracy is 0.7580645
(261+162)/nrow(test)## [1] 0.7580645Calculate AUC
The following code will generate an error
library(ROCR)
predROCR <- prediction(test_predict, test$trial)
Error in prediction(test_predict, test$trial) :
Format of predictions is invalid.That’s because the first argument in prediction function needs to be numeric value, but previously we have set it to be a categorical variable.
So let’s recalculate test_predict
test_predict <- predict(trialCART, newdata = test)Now we can calculate AUC value
ROC curve
library(ROCR)## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowesspredROCR <- prediction(test_predict[,2], test$trial)
as.numeric(performance(predROCR, "auc")@y.values)## [1] 0.8371063perfROCR <- performance(predROCR, "tpr", "fpr")
plot(perfROCR, colorize = TRUE)