Welcome to PyHealth!#

PyHealth is designed for both ML researchers and medical practitioners. We can make your healthcare AI applications easier to develop, test and validate. Your development process becomes more flexible and more customizable. [GitHub]

[News!] We are running the “PyHealth Live” gathering at 8 PM central time every Wednesday night! Welcome to join the live discussion over zoom. You may also add the schedules to your Google Calender or Microsoft Outlook (.ics).

FYI, the PyHealth Weekly Live will introduce basic pyhealth modules sequentially and showcase the newly developed functions as well as different use cases. For efficiency, all live lasts for around half an hour and the video collections are can be found in YouTube. The future scheduled topics are announced here. Hope to see you all on every wednesday night!

Introduction [Video]#

PyHealth can support diverse electronic health records (EHRs) such as MIMIC and eICU and all OMOP-CDM based databases and provide various advanced deep learning algorithms for handling important healthcare tasks such as diagnosis-based drug recommendation, patient hospitalization and mortality prediction, and ICU length stay forecasting, etc.

Build a healthcare AI pipeline can be as short as 10 lines of code in PyHealth.

Modules#

All healthcare tasks in our package follow a five-stage pipeline:

load dataset -> define task function -> build ML/DL model -> model training -> inference

! We try hard to make sure each stage is as separate as possibe, so that people can customize their own pipeline by only using our data processing steps or the ML models. Each step will call one module and we introduce them using an example.

An ML Pipeline Example#

STEP 1: <pyhealth.datasets> provides a clean structure for the dataset, independent from the tasks. We support MIMIC-III, MIMIC-IV and eICU, as well as the standard OMOP-formatted data. The dataset is stored in a unified Patient-Visit-Event structure.

from pyhealth.datasets import MIMIC3Dataset
mimic3base = MIMIC3Dataset(
    root="https://storage.googleapis.com/pyhealth/Synthetic_MIMIC-III/",
    tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
    # map all NDC codes to ATC 3-rd level codes in these tables
    code_mapping={"NDC": ("ATC", {"target_kwargs": {"level": 3}})},
)

User could also store their own dataset into our <pyhealth.datasets.SampleDataset> structure and then follow the same pipeline below, see Tutorial

STEP 2: <pyhealth.tasks> inputs the <pyhealth.datasets> object and defines how to process each patient’s data into a set of samples for the tasks. In the package, we provide several task examples, such as drug recommendation and length of stay prediction.

from pyhealth.tasks import drug_recommendation_mimic3_fn
from pyhealth.datasets import split_by_patient, get_dataloader

mimic3sample = mimic3base.set_task(task_fn=drug_recommendation_mimic3_fn) # use default task
train_ds, val_ds, test_ds = split_by_patient(mimic3sample, [0.8, 0.1, 0.1])

# create dataloaders (torch.data.DataLoader)
train_loader = get_dataloader(train_ds, batch_size=32, shuffle=True)
val_loader = get_dataloader(val_ds, batch_size=32, shuffle=False)
test_loader = get_dataloader(test_ds, batch_size=32, shuffle=False)

STEP 3: <pyhealth.models> provides the healthcare ML models using <pyhealth.models>. This module also provides model layers, such as pyhealth.models.RETAINLayer for building customized ML architectures. Our model layers can used as easily as torch.nn.Linear.

from pyhealth.models import Transformer

model = Transformer(
    dataset=mimic3sample,
    feature_keys=["conditions", "procedures"],
    label_key="drugs",
    mode="multilabel",
)

STEP 4: <pyhealth.trainer> is the training manager with train_loader, the val_loader, val_metric, and specify other arguemnts, such as epochs, optimizer, learning rate, etc. The trainer will automatically save the best model and output the path in the end.

from pyhealth.trainer import Trainer

trainer = Trainer(model=model)
trainer.train(
    train_dataloader=train_loader,
    val_dataloader=val_loader,
    epochs=50,
    monitor="pr_auc_samples",
)

STEP 5: <pyhealth.metrics> provides several common evaluation metrics (refer to Doc and see what are available) and special metrics in healthcare, such as drug-drug interaction (DDI) rate.

trainer.evaluate(test_loader)

Medical Code Map#

<pyhealth.codemap> provides two core functionalities: (i) looking up information for a given medical code (e.g., name, category, sub-concept); (ii) mapping codes across coding systems (e.g., ICD9CM to CCSCM). This module can be independently applied to your research.
For code mapping between two coding systems

from pyhealth.medcode import CrossMap

codemap = CrossMap.load("ICD9CM", "CCSCM")
codemap.map("82101") # use it like a dict

codemap = CrossMap.load("NDC", "ATC")
codemap.map("00527051210")

For code ontology lookup within one system

from pyhealth.medcode import InnerMap

icd9cm = InnerMap.load("ICD9CM")
icd9cm.lookup("428.0") # get detailed info
icd9cm.get_ancestors("428.0") # get parents

Medical Code Tokenizer#

<pyhealth.tokenizer> is used for transformations between string-based tokens and integer-based indices, based on the overall token space. We provide flexible functions to tokenize 1D, 2D and 3D lists. This module can be independently applied to your research.

from pyhealth.tokenizer import Tokenizer

# Example: we use a list of ATC3 code as the token
token_space = ['A01A', 'A02A', 'A02B', 'A02X', 'A03A', 'A03B', 'A03C', 'A03D', \
        'A03F', 'A04A', 'A05A', 'A05B', 'A05C', 'A06A', 'A07A', 'A07B', 'A07C', \
        'A12B', 'A12C', 'A13A', 'A14A', 'A14B', 'A16A']
tokenizer = Tokenizer(tokens=token_space, special_tokens=["<pad>", "<unk>"])

# 2d encode
tokens = [['A03C', 'A03D', 'A03E', 'A03F'], ['A04A', 'B035', 'C129']]
indices = tokenizer.batch_encode_2d(tokens) # [[8, 9, 10, 11], [12, 1, 1, 0]]

# 2d decode
indices = [[8, 9, 10, 11], [12, 1, 1, 0]]
tokens = tokenizer.batch_decode_2d(indices) # [['A03C', 'A03D', 'A03E', 'A03F'], ['A04A', '<unk>', '<unk>']]

Users can customize their healthcare AI pipeline as simply as calling one module

process your OMOP data via pyhealth.datasets
process the open eICU (e.g., MIMIC) data via pyhealth.datasets
define your own task on existing databases via pyhealth.tasks
use existing healthcare models or build upon it (e.g., RETAIN) via pyhealth.models.
code map between for conditions and medicaitons via pyhealth.codemap.

Datasets#

We provide the following datasets for general purpose healthcare AI research:

Dataset	Module	Year	Information
MIMIC-III	`pyhealth.datasets.MIMIC3BaseDataset`	2016	MIMIC-III Clinical Database
MIMIC-IV	`pyhealth.datasets.MIMIC4BaseDataset`	2020	MIMIC-IV Clinical Database
eICU	`pyhealth.datasets.eICUBaseDataset`	2018	eICU Collaborative Research Database
OMOP	`pyhealth.datasets.OMOPBaseDataset`		OMOP-CDM schema based dataset

Machine/Deep Learning Models#

Model Name	Type	Module	Year	Reference
Convolutional Neural Network (CNN)	deep learning	`pyhealth.models.CNN`	1989	Handwritten Digit Recognition with a Back-Propagation Network
Recurrent Neural Nets (RNN)	deep Learning	`pyhealth.models.RNN`	2011	Recurrent neural network based language model
Transformer	deep Learning	`pyhealth.models.Transformer`	2017	Atention is All you Need
RETAIN	deep Learning	`pyhealth.models.RETAIN`	2016	RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
GAMENet	deep Learning	`pyhealth.models.GAMENet`	2019	GAMENet: Graph Attention Mechanism for Explainable Electronic Health Record Prediction
MICRON	deep Learning	`pyhealth.models.MICRON`	2021	Change Matters: Medication Change Prediction with Recurrent Residual Networks
SafeDrug	deep Learning	`pyhealth.models.SafeDrug`	2021	SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations

Benchmark on Healthcare Tasks#

Here is our benchmark doc on healthcare tasks. You can also check this below.

We also provide function for leaderboard generation, check it out in our github repo.

Here are the dynamic visualizations of the leaderboard. You can click the checkbox and easily compare the performance for different models doing different tasks on different datasets!

import sys
sys.path.append('../..')

from leaderboard import leaderboard_gen, utils
args = leaderboard_gen.construct_args()
leaderboard_gen.plots_generation(args)