TSLink2

A fast timeseries record linkage algorithm. This program efficiently links patients to their ergometric tests based on timeseries data.

Requirements

Install the Boost Filesystem and System libraries

sudo apt install libboost-filesystem-dev

Data preparation

A simulated dataset accompanies this repository. The dataset could be downloaded from figshare or fetched automatically using the supplied script as follows:

scripts/download_data.sh

Optionally, to prepare data for linking integer values, use the following command to drop the fractions from all heart rate values.

gawk 'BEGIN{FS=","} {if(NR==1) {print($0)} else {printf("%d,%d,%d\n",$1,$2,$3)}} END{}' data/sim_ergo_1600.csv > data/sim_ergo_1600_int.csv
gawk 'BEGIN{FS=","} {if(NR==1) {print($0)} else {printf("%d,%d,%d\n",$1,$2,$3)}} END{}' data/sim_pat_1600.csv > data/sim_pat_1600_int.csv

Optionally, to prepare data for linking categorical values, use the following command to convert heart rate values into discrete levels.

scripts/gen_categorical_dataset.py

This script loads the simulation dataset files, convert the values therein into series of characters, and save the results as ../data/sim_ergo_1600_cat.csv and ../data/sim_pat_1600_cat.csv. The script converts the heart rate values as follows. First the values are divided by 10, then converted into integer, and finally the integer values [0...25] are mapped into lowercase characters [ab...z] For example, the heart rate values [94.3, 100.5, 130.1, 110.5] would be mapped into characters [j,k,n,l] respectively.

Data set description

This dataset constitutes two files (after decompression), namely: sim_ergo_1600.csv and sim_pat_1600.csv.

• sim_ergo_1600.csv contains heart rate timeseries data for 1600 patients' ergometric tests. For each patient, 20 different ergometric tests were simulated. Each row in this file constitutes three field values: Ergo_ID, Heart Rate (BPM), and timestamp.

• sim_pat_1600.csv contains only four sample readings from each of the patient's 20 ergometric tests. Each row contains three values: patient_ID, Heart Rate, and timestamp.

Objective

The goal is to link patients (identified by their patient_ID in the pat.csv file) to their corresponding ergometric tests (identified by their Ergo_ID in the ergo.csv file). This is done solely on matching the timestamp-value pairs from both files.

Performance evaluation

Data is simulated such that correctly linked/matched identifiers follow the following formula:

( |Ergo_ID - patient_ID| ) mod 10000 == 0

The above formula is useful in evaluating the linkage algorithm performance.

How to build

To build just one target, use either

make tslink1

or

make tslink2

To build both implementations, use

make all

How to use

Assuming that the datasets (the two csv files) have been downloaded and uncompressed into directory data. Run tslink1 or tslink2 on these simulated datasets as follows:

bin/tslink1 data/sim_pat_1600.csv data/sim_ergo_1600.csv out/link_results.csv
bin/tslink2 data/sim_pat_1600.csv data/sim_ergo_1600.csv out/link_results.csv

Both executables take three arguments in the following order: <patients dataset>, <ergometric dataset>, and <output path> Each time an experiment is run, the results are appended to the file designated by the output path (third argument). In the example above, out/link_results.csv is the output path. Any path could be used for reporting the results. If the file does not exist, it is automatically created and initialized with the header line.

Results

Each row in the output csv file constitutes the following data:

algo,n_patients,n_ergos,t,t1,t2,t3,t4,t5,TP,FP

Where algo is the algorithm signature which is either TSLink1_C++ or TSLink2_C++, n_patients is the number of unique patients in the dataset, n_ergos is the number of unique ergo tests, t is the total run time in seconds, t1 is the date-to-epoch conversion time (N/A) (always = -), t2 is the time taken to form the first sequence S1, t3 is the time taken to form the S2 sequence, t4 is the time taken to sort S1, S2, merge them into S, and sort the merged sequence S, t5 is the time taken to scan the sequence S and perform pat-to-ergo associations followed by linkage. TP is the number of true positive links, and FP is the number of false positive links.