PKDocClassifier

PKDocClassifier| Data | Reproduce our results | Make new predictions | Citing

This repository contains custom pipes and models to classify scientific publications from PubMed depending on whether they estimate pharmacokinetic (PK) parameters from in vivo studies. The final pipeline retrieved more than 121K PK publications and runs weekly updates available at https://pkpdai.com/pkdocsearch. The architectures in this repository are described in the following publication: https://pubmed.ncbi.nlm.nih.gov/34381873/

Data

The labels assigned to each publication can be found in the labels folder. These labels are available under the terms of the Creative Commons Zero "No rights reserved" data (CC0 1.0 Public domain dedication).

Reproduce our results

1. Installing dependencies

You will need an environment with Python 3.7+. We strongly recommend that you use an isolated Python environment (such as virtualenv or conda) to install the packages related to this project. Our default option will be to create a virtual environment with conda:

If you don't have anaconda installed follow the instructions here

Create conda environment for this project and activate it

conda create -n PKDocClassifier python=3.7
conda activate PKDocClassifier

Clone and access this repository on your local machine
```
git clone https://github.com/fgh95/PKDocClassifier
cd PKDocClassifier
```
If you are on MacOSX install LLVM's OpenMP runtime library, e.g.
```
brew install libomp
```
Install all project dependencies
```
pip install .
```

2. Data download and parsing - Optional

If you would like to reproduce the steps taken for data retrieval and parsing you will need to download the whole MEDLINE dataset and store it into a spark dataframe. Alternatively, you can also skip this step and use the parsed data available at data/subsets/. Alternatively, follow the steps at pubmed_parser wiki and place the resulting medline_lastview.parquet file at data/medline_lastview.parquet. Then, change the spark config file to your spark configuration and run:

python scripts/getready.py

This should generate the files at data/subsets/.

3. Reproduce results

3.1. Field analysis and N-grams

To generate the features run (~30min):
```
python scripts/features_bow.py
```

Bootstrap field analysis (~3h on 12 threads, requires at least 16GB of RAM, set overwrite to False if you want to skip this step)

python scripts/bootstrap_bow.py \
   --input-dir data/encoded/fields \
   --output-dir data/results/fields \
   --output-dir-bootstrap data/results/fields/bootstrap \
   --path-labels data/labels/training_labels.csv \
   --overwrite True

Optional analysis using idf scores for reweighing:

python scripts/bootstrap_bow.py \
   --input-dir data/encoded/fields \
   --output-dir data/results/fields \
   --output-dir-bootstrap data/results/fields/bootstrap \
   --path-labels data/labels/training_labels.csv \
   --use-idf True

Bootstrap n-grams (set overwrite to False if you want to skip this step)

python scripts/bootstrap_bow.py \
   --input-dir data/encoded/ngrams \
   --output-dir data/results/ngrams \
   --output-dir-bootstrap data/results/ngrams/bootstrap \
   --path-labels data/labels/training_labels.csv \
   --overwrite True

Display results

python scripts/display_results.py \
   --input-dir  data/results/fields\
   --output-dir data/final/fields

python scripts/display_results.py \
   --input-dir  data/results/ngrams\
   --output-dir data/final/ngrams

3.2. Distributed representations

Encode using SPECTER. To generate the features with specter you can preprocess the data running:

python preprocess_specter.py

This will generate the following input data as .ids and .json files at data/encoded/specter/. Finally, to generate the input features you will need to clone the SPECTER repo and follow the instructions on how to use the pretrained model. After cloning and installing SPECTER dependencies we ran the following command from the specter directory to encode the documents:

python scripts/embed.py \
   --ids ../data/encoded/specter/training_ids.ids --metadata ../data/encoded/specter/training_meta.json \
   --model ./model.tar.gz \
   --output-file ../data/encoded/specter/training_specter.jsonl \
   --vocab-dir data/vocab/ \
   --batch-size 16 \
   --cuda-device -1

python scripts/embed.py \
   --ids ../data/encoded/specter/test_ids.ids --metadata ../data/encoded/specter/test_meta.json \
   --model ./model.tar.gz \
   --output-file ../data/encoded/specter/test_specter.jsonl \
   --vocab-dir data/vocab/ \
   --batch-size 16 \
   --cuda-device -1

This should output two files in the data directory: /data/encoded/specter/training_specter.jsonl and data/encoded/specter/test_specter.jsonl

Generate BioBERT representations:
```
python scripts/features_dist.py
```

Run bootstrap iterations for distributed representations (set overwrite to False if you want to skip this step):

python scripts/bootstrap_dist.py \
   --is-specter True \
   --use-bow False \
   --input-dir data/encoded/specter \
   --output-dir data/results/distributional \
   --output-dir-bootstrap data/results/distributional/bootstrap \
   --path-labels data/labels/training_labels.csv \
   --path-optimal-bow data/encoded/ngrams/training_unigrams.parquet \
   --overwrite True

python scripts/bootstrap_dist.py \
   --is-specter False \
   --use-bow False \
   --input-dir data/encoded/biobert \
   --output-dir data/results/distributional \
   --output-dir-bootstrap data/results/distributional/bootstrap \
   --path-labels data/labels/training_labels.csv \
   --path-optimal-bow data/encoded/ngrams/training_unigrams.parquet \
   --overwrite True

Display results

python scripts/display_results.py \
   --input-dir  data/results/distributional \
   --output-dir data/final/distributional \
   --convert-latex

python scripts/display_results.py \
   --input-dir  data/results/distributional/bow_and_distributional \
   --output-dir data/final/distributional/bow_and_distributional \
   --convert-latex

From these plots we can see that the best-performing architecture on the training data, on average, is the one using average embeddings from BioBERT and unigram features.

3.3. Final pipeline

Run the cross-validation analyses:

python scripts/cross_validate.py \
   --training-embeddings  data/encoded/biobert/training_biobert_avg.parquet \
   --training-optimal-bow  data/encoded/ngrams/training_unigrams.parquet \
   --training-labels  data/labels/training_labels.csv\
   --output-dir  data/results/final-pipeline

Train the final pipeline (preprocessing, encoding, decoding) from scratch with optimal hyperparameters and apply it to the test set:

python scripts/train_test_final.py \
   --path-train  data/subsets/training_subset.parquet \
   --train-labels  data/labels/training_labels.csv \
   --path-test  data/subsets/test_subset.parquet \
   --test-labels  data/labels/test_labels.csv \
   --cv-dir  data/results/final-pipeline \
   --output-dir  data/results/final-pipeline \
   --train-pipeline  True

Final results on the test set should be printed on the terminal.

Make new predictions

You can make new predictions in three simple steps:

import pandas as pd
import joblib

# 1. Import data
data = pd.read_csv('data/examples/to_classify.csv').reset_index(drop=True)
data['pmid'] = data['pmid'].fillna(0).astype(int).fillna('')
data.head()
#>>>                                             abstract  mesh_terms  ...                                              title      pmid
#>>> 0  Rituximab, an anti-CD20 monoclonal antibody, i...         NaN  ...  Pharmacokinetics, efficacy and safety of the r...  28766389
#>>> 1  Background: Biosimilars are highly similar to ...         NaN  ...  A Randomized, Double-Blind, Efficacy and Safet...  31820339
#>>> 2  AIMS: Rituximab is standard care in a number o...         NaN  ...  Pharmacokinetics, exposure, efficacy and safet...  31050355
#>>> 3  BACKGROUND: Studies in patients with rheumatoi...         NaN  ...  Efficacy, pharmacokinetics, and safety of the ...  28712940
#>>> 4  Rituximab, a chimeric monoclonal antibody targ...         NaN  ...  Rituximab (monoclonal anti-CD20 antibody): mec...  14576843

# 2. Import trained model
pipeline_trained = joblib.load("data/results/final-pipeline/optimal_pipeline.pkl")

# 3. Make predictions
pred_test = pipeline_trained.predict(data)
print(pred_test)
#>>> array(['Not Relevant', 'Not Relevant', 'Relevant', 'Relevant',
#       'Not Relevant'], dtype=object)

You can find this example on a jupyter notebook.

Citation

@article{hernandez2021automated,
  title={An automated approach to identify scientific publications reporting pharmacokinetic parameters},
  author={Hernandez, Ferran Gonzalez and Carter, Simon J and Iso-Sipil{\"a}, Juha and Goldsmith, Paul and Almousa, Ahmed A and Gastine, Silke and Lilaonitkul, Watjana and Kloprogge, Frank and Standing, Joseph F},
  journal={Wellcome Open Research},
  volume={6},
  number={88},
  pages={88},
  year={2021},
  publisher={F1000 Research Limited}
}

PKPDAI/PKDocClassifier