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README.md

Assessment and Plan Modeling

Code for the paper "Structured Understanding of Assessment and Plans in Clinician Documentation" by Stupp et al. Available as a preprint on medrxiv: https://www.medrxiv.org/content/10.1101/2022.04.13.22273438v1

Citation for the paper:

Citation TBA

The annotations dataset accompanying the paper can be found on Zenodo: DOI

Instructions for running

The full model training process can be run using the run.sh script, which also creates a suitable virtual environment.

To run individual steps, consult the instructions below.

Prerequisites

Python3 is required, code was tested on 3.9.9 but should work on 3.7+. Create and active a virtual environment:

python3 -m venv env
source env/bin/activate

pip install -r requirements.txt

Tests are executable using the test specific module, for example:

python3 -m assessment_plan_modeling.ap_parsing.ap_parsing_task_test

Data Generation

To generate TensorFlow examples for model training, run the beam pipeline presented in data_gen_main.py. Input notes are expected to be in the MIMIC-III note_event csv format. Annotations (all_model_ratings.csv) can be downloaded from the aforementioned dataset.

Unfortunately as the vocabulary contains MIMIC-III data it is up to the user to generate an appropriate vocabulary. The file consists of unquoted, escaped strings, with each token on a new line. An example vocabulary is found in the repository head sample_vocab.txt.

DATA_DIR="path/to/data"
python assessment_plan_modeling/ap_parsing/data_gen_main.py \
  --input_note_events="${DATA_DIR}/notes.csv" \
  --input_ratings="${DATA_DIR}/all_model_ratings.csv" \
  --output_path="${DATA_DIR}/ap_parsing_tf_examples/$(date +%Y%m%d)" \
  --vocab_file="${DATA_DIR}/sample_vocab.txt" \
  --section_markers="assessment_plan_modeling/note_sectioning/mimic_note_section_markers.json" \
  --n_downsample=100 \
  --max_seq_length=2048

Model Training

Model training is configurable by yaml as described in the TensorFlow model garden repository.

Test Run

Using dummy data, should complete in several seconds on CPU.

MODEL_DIR="/tmp/test_model_$(date +%Y%m%d-%H%M)"
WORK_DIR="$PWD/assessment_plan_modeling/ap_parsing"
EXP_TYPE="ap_parsing"
CONFIG_DIR="${WORK_DIR}/configs"
python ${WORK_DIR}/train.py \
  --experiment=${EXP_TYPE} \
  --config_file="${CONFIG_DIR}/local_example.yaml" \
  --mode=train \
  --model_dir=${MODEL_DIR} \
  --alsologtostderr

Full Run

Running on a single V100 GPU, this should take approximately 2 hours.

MODEL_DIR="/tmp/model_$(date +%Y%m%d-%H%M)"
WORK_DIR="$PWD/assessment_plan_modeling/ap_parsing"
EXP_TYPE="ap_parsing"
CONFIG_DIR="${WORK_DIR}/configs"
DATA_DIR="/path/to/tfrecords"

TRAIN_DATA="${DATA_DIR}/train_rated_nonaugmented.tfrecord*"
VAL_DATA="${DATA_DIR}/val_set.tfrecord*"
PARAMS_OVERRIDE="task.use_crf=true"
PARAMS_OVERRIDE="${PARAMS_OVERRIDE},task.train_data.input_path='${TRAIN_DATA}'"
PARAMS_OVERRIDE="${PARAMS_OVERRIDE},task.validation_data.input_path='${VAL_DATA}'"
PARAMS_OVERRIDE="${PARAMS_OVERRIDE},trainer.train_steps=5000"

python ${WORK_DIR}/train.py \
  --experiment=${EXP_TYPE} \
  --config_file="${CONFIG_DIR}/base_ap_parsing_model_config.yaml" \
  --config_file="${CONFIG_DIR}/base_ap_parsing_task_config.yaml" \
  --params_override=${PARAMS_OVERRIDE} \
  --mode=train_and_eval \
  --model_dir=${MODEL_DIR} \
  --alsologtostderr

Inference and Evaluation

Inference and evaluation libraries are supplied as is.