Code Monkey home page Code Monkey logo

tame's Introduction

Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder

This repository contains the official PyTorch implementation of the following paper:

Identifying Sepsis Subphenotypes via Time-Aware Multi-ModalAuto-Encoder (KDD2020)
Changchang yin, Ruoqi Liu, Dongdong Zhang, Ping Zhang
paper

Abstract: Sepsis is a heterogeneous clinical syndrome that is the leading cause of mortality in hospital intensive care units (ICUs). Identification of sepsis subphenotypes may allow for more precise treatments and lead to more targeted clinical interventions. Recently, sepsis subtyping on electronic health records (EHRs) has attracted interest from healthcare researchers. However, most sepsis subtyping studies ignore the temporality of EHR data and suffer from missing values. In this paper, we propose a new sepsis subtyping framework to address the two issues. Our subtyping framework consists of a novel Time-Aware Multi-modal auto-Encoder (TAME) model which introduces time-aware attention mechanism and incorporates multi-modal inputs (e.g., demographics, diagnoses, medications, lab tests and vital signs) to impute missing values, a dynamic time wrapping (DTW) method to measure patients' temporal similarity based on the imputed EHR data, and a weighted k-means algorithm to cluster patients. Comprehensive experiments on real-world datasets show TAME outperforms the baselines on imputation accuracy. After analyzing TAME-imputed EHR data, we identify four novel subphenotypes of sepsis patients, paving the way for improved personalization of sepsis management.

Framework

TAME takes multi-modal data (including demographic, diagnoses, medication, lab tests and vital signs) as inputs to impute missing values (in lab tests and vital signs).

Given the imputed data, DTW is adopted to compute temporal patient similarity. Based on the similarity matrix, wk-means is proposed to subtype sepsis patients into phenotypes.

Files Directory

TAME
|
|--code
|
|--file                                 * The preprocessing codes will write some files here.
|
|--data                                 * Put the downloaded datasets here.
|    |
|    |--DACMI                           * Put the downloaded DACMI dataset here.
|    |    |
|    |    |--train_groundtruth
|    |    |
|    |    |--train_with_missing
|    |
|    |--MIMIC
|         |
|         |--initial_mimiciii           * Put the downloaded MIMIC-III dataset here.
|         |    |
|         |    |--PATIENTS.csv
|         |    |
|         |    |--ICUSTAYS.csv
|         |    |
|         |    |--DIAGNOSES_ICD.csv
|         |    |
|         |    |--PRESCRIPTIONS.csv
|         |    |
|         |    |--LABEVENTS.csv
|         |    |
|         |    |--pivoted_lab.csv      * Generate pivoted_*.csv with https://github.com/MIT-LCP/mimic-code.
|         |    |
|         |    |--pivoted_vital.csv
|         |    |
|         |    |--pivoted_sofa.csv
|         |
|         |--train_groundtruth
|         |
|         |--train_with_missing
| 
| 
|--result                             * The imputation results and clustering results are here.
     |
     |--DACMI
     |
     |--MIMIC

Environment

Ubuntu16.04, python2.7

Install pytorch 1.3.0

Data preprocessing

DACMI data preprocessing

  1. Download DACMI dataset and put the data in TAME/data/DACMI/.

  2. Preprocess DACMI data.

cd code/preprocessing
python generate_value_distribution.py --dataset DACMI

MIMIC-III data preprocessing

  1. Download MIMIC-III dataset and put the data in TAME/data/MIMIC/initial_mimiciii/.

  2. Generate pivoted files (pivoted_lab.csv, pivoted_vital.csv, pivoted_sofa.csv) according to MIT-LCP/mimic-code, and put the data in TAME/data/MIMIC/initial_mimiciii/.

  • SQL for pivoted file generation can be found here.
  1. Preprocess MIMIC-III data.
cd code/preprocessing
python preprocess_mimic_data.py --dataset MIMIC
python generate_sepsis_variables.py --dataset MIMIC
python generate_value_distribution.py --dataset MIMIC

TAME

  1. Train TAME model.
cd code/TAME
python main.py --dataset DACMI

cd code/TAME
python main.py --dataset MIMIC
  1. Generate the imputation results.
cd code/TAME
python main.py --dataset DACMI --phase test --resume ../../data/DACMI/models/best.ckpt

cd code/TAME
python main.py --dataset MIMIC --phase test --resume ../../data/MIMIC/models/best.ckpt

Results of TAME

The imputation results on DACMI dataset.

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Method  |     PCL     |      PK     |    PLCO2    |     PNA     |     HCT     |     HGB     |     MCV     |     PLT     |     WBC     |     RDW     |    PBUN     |    PCRE     |    PGLU     |    Mean 
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Mean    |    0.295    |    0.277    |    0.301    |    0.293    |    0.287    |    0.292    |    0.309    |    0.319    |    0.299    |    0.318    |    0.313    |    0.306    |    0.282    |    0.299
KNN     |    0.220    |    0.249    |    0.241    |    0.228    |    0.220    |    0.221    |    0.267    |    0.248    |    0.251    |    0.253    |    0.238    |    0.244    |    0.264    |    0.242
3DMICE  |    0.200    |    0.263    |    0.231    |    0.214    |    0.150    |    0.149    |    0.229    |    0.256    |    0.246    |    0.185    |    0.234    |    0.277    |    0.224    |    0.220
T-LGBM  |    0.135    |    0.226    |    0.179    |    0.156    |    0.100    |    0.092    |    0.229    |    0.158    |    0.199    |    0.202    |    0.134    |    0.183    |    0.240    |    0.172
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
BRNN    |    0.155    |    0.230    |    0.196    |    0.174    |    0.090    |    0.087    |    0.245    |    0.175    |    0.211    |    0.208    |    0.154    |    0.210    |    0.252    |    0.184
CATSI   |    0.174    |    0.243    |    0.203    |    0.196    |    0.144    |    0.135    |    0.253    |    0.186    |    0.227    |    0.213    |    0.157    |    0.206    |    0.260    |    0.200
DETROIT |    0.138    |    0.219    |    0.172    |    0.155    |    0.093    |    0.087    |    0.234    |    0.152    |    0.199    |    0.201    |    0.137    |    0.181    |    0.262    |    0.172
BRITS   |    0.142    |    0.208    |    0.176    |    0.154    |    0.121    |    0.115    |    0.244    |    0.165    |    0.206    |    0.218    |    0.167    |    0.192    |    0.268    |    0.183
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
TAME-T  |    0.102    |    0.187    |    0.144    |    0.128    |    0.079    |    0.074    |    0.227    |    0.144    |    0.199    |    0.211    |    0.128    |    0.201    |    0.228    |    0.158
TAME-V  |    0.121    |    0.193    |    0.164    |    0.145    |    0.081    |    0.076    |    0.238    |    0.168    |    0.203    |    0.201    |    0.141    |    0.210    |    0.222    |    0.166
TAME    |    0.100    |    0.179    |    0.155    |    0.125    |    0.073    |    0.077    |    0.218    |    0.136    |    0.198    |    0.180    |    0.121    |    0.185    |    0.221    |    0.151
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

The imputation results on MIMIC-III dataset (all 27 variables).

-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Model   |  aniongap  |bicarbonate | creatinine |  chloride  |  glucose   | hemoglobin |  lactate   |  platelet  |    ptt     |    inr     |     pt     |   sodium   |    bun     |    wbc     |    spo2    | C-reactive | heartrate  | hematocrit |   sysbp    |   tempc    |   diasbp   |     gcs    |  resprate  |   bands    |   meanbp   | Magnesium  |urineoutput |    Mean    
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Mean    |    0.29    |    0.24    |    0.25    |    0.22    |    0.30    |    0.27    |    0.42    |    0.27    |    0.46    |    0.32    |    0.41    |    0.24    |    0.23    |    0.26    |    0.32    |    1.37    |    0.40    |    0.31    |    0.31    |    0.38    |    0.32    |    0.37    |    0.31    |    0.86    |    0.22    |    0.42    |    0.34    |    0.37
KNN     |    0.28    |    0.22    |    0.22    |    0.22    |    0.30    |    0.25    |    0.44    |    0.26    |    0.38    |    0.31    |    0.29    |    0.24    |    0.22    |    0.25    |    0.30    |    1.31    |    0.37    |    0.28    |    0.24    |    0.37    |    0.24    |    0.38    |    0.27    |    0.80    |    0.17    |    0.41    |    0.33    |    0.34
3DMICe  |    0.22    |    0.19    |    0.22    |    0.18    |    0.27    |    0.18    |    0.42    |    0.25    |    0.40    |    0.25    |    0.29    |    0.20    |    0.22    |    0.25    |    0.27    |    1.20    |    0.34    |    0.28    |    0.24    |    0.36    |    0.20    |    0.33    |    0.27    |    0.79    |    0.15    |    0.38    |    0.30    |    0.32
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
BRNN    |    0.15    |    0.17    |    0.20    |    0.13    |    0.29    |    0.12    |    0.40    |    0.20    |    0.41    |    0.16    |    0.24    |    0.18    |    0.17    |    0.26    |    0.25    |    1.28    |    0.31    |    0.25    |    0.18    |    0.26    |    0.17    |    0.23    |    0.24    |    0.96    |    0.13    |    0.36    |    0.27    |    0.30
CATSI   |    0.12    |    0.12    |    0.22    |    0.13    |    0.29    |    0.14    |    0.41    |    0.22    |    0.42    |    0.20    |    0.25    |    0.18    |    0.20    |    0.23    |    0.25    |    1.13    |    0.34    |    0.25    |    0.18    |    0.24    |    0.16    |    0.22    |    0.24    |    0.85    |    0.13    |    0.33    |    0.27    |    0.29
DETROIT |    0.11    |    0.09    |    0.28    |    0.09    |    0.27    |    0.13    |    0.38    |    0.22    |    0.46    |    0.17    |    0.24    |    0.10    |    0.17    |    0.22    |    0.26    |    1.10    |    0.31    |    0.26    |    0.18    |    0.24    |    0.16    |    0.23    |    0.24    |    0.78    |    0.13    |    0.33    |    0.25    |    0.27
BRITS   |    0.12    |    0.08    |    0.23    |    0.12    |    0.27    |    0.12    |    0.39    |    0.20    |    0.41    |    0.18    |    0.24    |    0.16    |    0.20    |    0.20    |    0.26    |    1.22    |    0.32    |    0.23    |    0.19    |    0.23    |    0.15    |    0.20    |    0.24    |    0.84    |    0.13    |    0.34    |    0.26    |    0.28
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
TAME-T  |    0.13    |    0.11    |    0.24    |    0.10    |    0.25    |    0.11    |    0.34    |    0.19    |    0.36    |    0.21    |    0.24    |    0.11    |    0.17    |    0.20    |    0.24    |    1.29    |    0.24    |    0.17    |    0.15    |    0.25    |    0.13    |    0.16    |    0.23    |    0.71    |    0.12    |    0.30    |    0.24    |    0.26
TAME-V  |    0.16    |    0.13    |    0.23    |    0.12    |    0.26    |    0.11    |    0.36    |    0.20    |    0.38    |    0.19    |    0.22    |    0.14    |    0.17    |    0.20    |    0.23    |    1.29    |    0.23    |    0.18    |    0.13    |    0.21    |    0.12    |    0.15    |    0.22    |    0.70    |    0.11    |    0.31    |    0.24    |    0.26
TAME-M  |    0.13    |    0.11    |    0.24    |    0.10    |    0.25    |    0.11    |    0.34    |    0.19    |    0.36    |    0.21    |    0.24    |    0.11    |    0.17    |    0.23    |    0.24    |    1.33    |    0.24    |    0.20    |    0.14    |    0.25    |    0.14    |    0.16    |    0.24    |    0.73    |    0.14    |    0.32    |    0.25    |    0.27
TAME    |    0.11    |    0.09    |    0.19    |    0.08    |    0.26    |    0.09    |    0.35    |    0.18    |    0.38    |    0.15    |    0.20    |    0.10    |    0.14    |    0.21    |    0.22    |    1.16    |    0.23    |    0.19    |    0.13    |    0.24    |    0.12    |    0.16    |    0.20    |    0.73    |    0.12    |    0.31    |    0.23    |    0.25
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

DTW

Compute temporal patient similarity with DTW (on MIMIC-III dataset).

cd code/DTW
python main.py

Weighted k-means

Cluster patients with weighted k-means (on MIMIC-III dataset).

cd code/wkmeans
python main.py

K value selection for wieghted k-means

We conduct experiments with different K and compute the mean P-value for the variables. As is shown in the figure, K=4 is the best option (which is the elbow point).

Results of weighted k-means

Sepsis phenotype analysis

The ICU mortality rates in four phenotypes.

The variables' severity heatmap across the four phenotypes.

tame's People

Contributors

yinchangchang avatar

Stargazers

avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar

Watchers

avatar

Forkers

kiminh ankithmo naimahmednesaragi pickleyang landandland aligator4sah

tame's Issues

How to calculate P-value, Calinski-Harabasz Index (CHI) and Davis-Bouldin Index (DBI)?

Hello, Dr. Yin.
I'm very interested in your work using weighted kmeans for clustering. I want to know how to calculate p-value. Would you please provide me with relevant reference materials about it? In addition, the center of cluster is needed when calculating CHI and CBI. In this work, multi-dimensional time series data with different length are clustered, so how to get the center of the cluster?

Looking forward to your reply. Thank you!

Problems in “preprocess mimic-III data”：The feat-list is None

When I executing "preprocess mimic III data" according to readme, main.py Unable to execute because the variable feat_list in generate_value_distribution.py is None. After debugging, I found that this is because in the data / mimic / train_groundtruth dictionary, there is no .csv files. I noticed that you deleted the. csv file in this folder in some steps. I modified the relevant code, but it's still report error. So I'd like to ask if there are some codes that need to be modified in the "preprocess mimic III data".

Different dataset (e.g. mimic iv)

Hi, I found your work is interesting.

In processing of my own implementation, I have some issues.
If I have mimic iv dataset, will it work with your preprocessing codes, as I find the mimic iv and iii have different structures?
In addition, since I have mimic iv dataset, how can I generate this file "icu_hadm_dict.json" ? . I found this file is the initial input to your 1st preprocessing "preprocess_mimic_data.py".

Thank you.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.