Clinical Intervention Prediction and Understanding using Deep Networks Clinical Intervention Prediction and Understanding using Deep Networks
Paper summary #### Goal: Predict interventions on ICU patients using LSTM and CNN. #### Dataset: MIMIC-III v.1.4 https://mimic.physionet.org/ + Patients over 15 years of age with intensive care stay between 12h and 240h. (Only the first stay is considered for each patient) - 34148 unique records. + 5 static variables. + 29 vital signs and test results. + Clinical notes of patients (presented as time series). #### Feature Engineering: + Topic Modeling of clinical notes: Vector of topics using Latent Dirichlet Allocation (LDA) + Physiological Words: Vital / Laboratory results converted to z-scores - [integer values ​​between -4 and 4] and score is one-hot encoded (each vital / lab is replaced by 9 columns). It is good idea to avoid the imputation of missing values as the physiological word in this case is the all-zero vector. Feature vector: + is the concatenation of the static variables, physiological words for each vital/lab and the topic vector. + 1 feature vector / patient / hour. + 6-hour slice used to predict a 4-hour window after a 6-hour gap. All the features values are normalized between 0 and 1. (static variables are replicated). #### Target Classes: For some of the procedures to be predicted there are 4 classes: + Onset: Y goes from 0 to 1 during the prediction window. + Wean: Y goes from 1 to 0 during the prediction window. + Stay On: Y stays at 1 throughout prediction window. + Stay Off: Y stays at 0 for the entire prediction window. #### Setup of the Experiments: + Dataset Split: 70% training, 10% validation, 20% test. Long Short Term Memory (LSTM) Networks: + Dropout P(keep) = 0.8, L2 regularization. + 2 hidden layers: 512 nodes in each. Convolutional Neural Networks + 3 different temporal granularities (3, 4, 5 hours). 64 filters in each. + Features are treated as channels. 1D temporal convolution. + Dropout between fully connected layers. P (keep) = 0.5. TensorFlow 1.0.1 - Adam optimizer. Minibatches of size 128. Validation set used for early stopping (metric: AUC). #### Results: + Baseline for comparison: L2-regularized Logistic Regression + Metrics: + AUC per class. + AUC macro = Arithmetic mean of AUC per class. + Proposed architectures outperforms baseline. + Physiological words improve performance (especially on high class imbalance scenario). #### Model Interpretability: + LSTM: feature occlusion like analysis. The feature is replaced by uniformly distributed noise between 0 and 1 and variation in AUC is computed. + CNN: analysis of the maximally activating trajectories. #### Positive Aspects: + Relevant work: In the healthcare domain is very important to anticipate events. + Built on top of rich and heterogeneous data: It leverages large amounts of ICU data. + The proposed model is not a complete black-box. Interpretability is crucial if the system is to be adopted in the future. #### Caveats: + Some of the methodology is not clearly explained: + How the split of the dataset was performed? Was it on a patient-level? + When testing the logistic regression baseline it is not clear how the feature vector was built. Was it built by simply flattening the 6-hour chunk? + For the raw data test, it was not mentioned the way the missing values were treated.
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Clinical Intervention Prediction and Understanding using Deep Networks
Harini Suresh and Nathan Hunt and Alistair Johnson and Leo Anthony Celi and Peter Szolovits and Marzyeh Ghassemi
arXiv e-Print archive - 2017 via Local arXiv
Keywords: cs.LG

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