This PR reproduces the Temporal Pointwise Convolution (TPC) model and introduces a new hourly ICU length-of-stay prediction task in PyHealth.

All implementation, tests, and example pipelines are included. The contribution follows PyHealth BaseModel and task conventions and includes fast synthetic tests for reproducibility.

Primary files:

• pyhealth/models/tpc.py
• pyhealth/tasks/hourly_los.py
• examples/*
• tests/*
• docs/api/*

CS598 Deep Learning for Healthcare Project

This PR reproduces and contributes an implementation of:

Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit
Rocheteau et al., ACM CHIL 2021
Paper: https://arxiv.org/abs/2007.09483

Contributors

• Michael Edukonis (meduk2@illinois.edu)
• Keon Young Lee (kylee7@illinois.edu)
• Tanmay Thareja (tanmayt3@illinois.edu)

PR Overview

Contribution Types

This PR includes:

• Model contribution
• Standalone Task contribution
• Synthetic data tests
• End to End Pipeline (Model + Task + Dataset configs) example scripts + combined dual dataset run

Problem Overview

Efficient ICU bed management depends critically on estimating how long a patient will remain in the ICU.

This is formulated as:

•	Input: Patient data up to hour _t_
•	Output: Remaining ICU length of stay _(LoS)_ at time _t_

We follow the formulation in the original paper, predicting remaining LoS at each hour of the ICU stay.

Implementation Details

1) Model Contribution

pyhealth.models.tpc

We implement the Temporal Pointwise Convolution (TPC) model as a PyHealth-compatible model by extending BaseModel and follows the original paper’s architecture with adaptations for PyHealth’s input/output interfaces. Index files were updated to include the new modules accordingly.

2) Task Contribution

We implement a custom PyHealth task: Hourly Remaining Length of Stay. Index files were updated to include the new modules accordingly.

pyhealth.tasks.hourly_los

Task Definition

•	Predict remaining LoS at every hour
•	Predictions start after first 5 hours
•	Continuous regression task

Motivation

•	Mimics real-world ICU monitoring
•	Enables dynamic prediction updates

3) Ablation Study/ Example Usage

We implemented scripts for runnning the pipeline end to end with support for different experiemental setups or ablations.

examples/eicu_hourly_los_tpc.py : This provides an end-to-end script for reproducing and evaluating pipeline on EICU dataset.
examples/mimic4_hourly_los_tpc.py : This provides an end-to-end script for reproducing and evaluating pipeline on MIMIC-IV dataset.
examples/run_dual_dataset_tpc.py : This utility scripts runs the pipeline on both datasets and produces a combined report.

4) Test Cases

We implemented fast performing test cases for our Model and Task contribution using Sythentic Data.

tests/models/test_tpc.py
tests/tasks/test_hourly_los.py

Experimental Setup and Findings

1. Ablations to include/exclude domain specific engineering (skip connections, decay indicators, etc)

We varied key optimization and architectural hyperparameters (e.g. learning rate, dropout rate, etc) while keeping preprocessing and data splits fixed.

File Structure

.
├── pyhealth/models/tpc.py
├── pyhealth/tasks/hourly_los.py
├── pyhealth/datasets/configs/eicu_tpc.yaml
├── pyhealth/datasets/configs/mimic4_ehr_tpc.yaml
├── examples/eicu_hourly_los_tpc.py
├── examples/mimic4_hourly_los_tpc.py
├── examples/run_dual_dataset_tpc.py
├── tests/models/test_tpc.py
├── tests/tasks/test_hourly_los.py
├── docs/api/models.rst
├── docs/api/models/pyhealth.models.tpc.rst
├── docs/api/tasks.rst
├── docs/api/tasks/pyhealth.tasks.hourly_los.rst
└── README_TPC_LOS.md

Setup

1. Clone the repository.
2. Create and activate a python virtual environment.
3. Install project dependencies.
4. Set dataset paths with environment variables for the example scripts.

Quick Start (Synthetic Data)

eICU example

EICU_ROOT=/path/to/synthetic/eicu/data \
PYTHONPATH=. python3 examples/eicu_hourly_los_tpc.py \
  --epochs 1 \
  --batch_size 2 \
  --max_samples 8 \
  --model_variant full \
  --loss msle \
  --num_workers 1

MIMIC-IV example

MIMIC4_ROOT=/path/to/synthetic/mimic4/data \
PYTHONPATH=. python3 examples/mimic4_hourly_los_tpc.py \
  --epochs 1 \
  --batch_size 2 \
  --max_samples 8 \
  --loss msle \
  --num_workers 1

Combined dual-dataset run

EICU_ROOT=/path/to/synthetic/eicu/data \
MIMIC4_ROOT=/path/to/synthetic/mimic4/data \
PYTHONPATH=. python3 examples/run_dual_dataset_tpc.py \
  --eicu_cache_dir /path/to/eicu/cache/location
  --mimic_cache_dir /path/to/mimic/cache/location
  --eicu_max_samples 8 \
  --mimic_max_samples 8 \
  --eicu_epochs 1 \
  --mimic_epochs 1 \
  --model_variant full \
  --loss msle \
  --num_workers 1

Notes on Real Data

For full eICU or MIMIC-IV experiments, point EICU_ROOT or MIMIC4_ROOT to the real dataset locations and increase settings such as --epochs, --batch_size, and --max_samples as needed.

Tests

Run the project-specific tests with:

python3 -m pytest tests/models/test_tpc.py tests/tasks/test_hourly_los.py -q

Documentation

API documentation entries were added for:

• pyhealth.models.tpc
• pyhealth.tasks.hourly_los

Output

The example scripts print compact summary lines for quick validation:

• ABLATION_SUMMARY for eICU
• MIMIC_SUMMARY for MIMIC-IV

The dual-dataset runner parses both and prints a combined summary table.

Environment

This project is designed to run within the PyHealth environment.

Recommended setup:

• Python 3.12
• pyhealth (>= 2.0.0)
• torch
• standard scientific Python stack (numpy, pandas)

Install PyHealth and dependencies following the main repository instructions.