computational phenotyping of critically ill...

Computational Phenotyping of Critically Ill ChildrenA Deep Learning Approach

presented by David C. Kale1,2

Joint work with Zhengping Che1, Yan Liu1, and Randall Wetzel2

1University of Southern California2Laura P. and Leland K. Whittier VPICU, Children’s Hospital LA

November 15, 2014

Kale (USC/VPICU) Computational Phenotyping November 15, 2014 1 / 22

Complex physiologic patterns as phenotypes [1][2]

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Phenotypes vs. features

Medicine: phenotypes, biomarkers [3]

1 Measurable attributes of patient/disease.

2 Independent of other biomarkers.

3 Separate patients into meaningful groups.

4 Improve outcome prediction, risk assessment.

5 Clinically plausible, interpretable.

Machine learning: features, representations [4]

1 Measurable properties of objects.

2 Independent, disentangle factors of variation.

3 Form natural clusters.

4 Useful for discriminative, predictive tasks.

5 Interpretable, provide insight into problem.


Deep learning of representations

Representation learning : learn transformation of data useful for some task.

Main workhorse: deep learning (i.e., noveau neural networks).

• Date back to 40s; abandoned in 90s.

• Revived as deep learning in 2000s.(new methods, big data, fast CPUs)

• State-of-the-art in vision, speech, NLP

• Google, Apple, Microsoft, Facebook

• Biologically inspired (if not plausible).

• Maximally varying, nonlinear functions.

• Exploit labeled and unlabeled data.

• Layers yield increasing abstraction.


Deep learning: feed-forward neural networks

Ouputs: y (e.g., diagnosis or outcome)

binary label: y = 11+exp{−(w(o))>x(`)}

Hidden layers: x(`) = f(W (`)x(`−1))

where

x(`−1) =

{x(0) for ` = 1

f(a(`−1)) otherwise

and f(u) is a nonlinear function(e.g., sigmoid: 1/(1 + exp{−u}))

Inputs: x(0) (vital signs, lab tests, etc.)

Trained via greedy layer-wise pretraining, back-propagation using(stochastic) gradient descent.


Deep learning for time series

Classifica(on

Feature extrac(on

• Apply to fixed-size windows.

• Classification, feature extraction.

• Correlations across variables, time.

• Relatively few, weak model assumptions.

• Can learn to detect smooth,trajectory-like patterns.

• Inputs to other tasks (e.g., classification),models (e.g., HMM).


Deep learning for time series: sliding window approach

Classifica(on

Feature extrac(on

• Sliding window classification or feature extraction for• Sequential state estimation or detection (e.g., speech recognition).• Features for classification of longer time series.

• Can train, combine feature extractors for various lengths and scales.


Deep learning for time series: training on windows

• Sliding window to extract training data.

• Similar to training on smaller image patches.

• Leverage labels, etc., in different ways:• Unsupervised, all windows to learn patterns.• Supervised: only windows with labels.


Experiments

Time series from ∼ 8, 500 PICU episodes from CHLA:

• 24 hours of data, sampled every 60 minutes (after preprocessing1).

• 13 variables: vitals, labs, inputs/outputs, assessments.

• Binary label: Respiratory diagnostic category.

• Note: 8500(24-S+1) overlapping windows of length S.(e.g., > 1 million for S = 12)

Experiments:

• Train 3-layer fully-connected neural networks for different lengths S.2

• Classification AUC vs. baselines (raw data, sample statistics) for:

1 Classifying first S hours.2 Classifying full 24 hours using sliding window features.

• Visualize physiologic patterns detected by hidden units.

1Age correction, resampling, imputation, rescaling, etc.2See Appendix D for our fast incremental training procedure.


First 24-hour classification

AUC Precision RecallFull 24-hour window onlyNNetInc(Ft) 0.7953± 0.0141 0.6448± 0.0191 0.7213± 0.0257NNet 0.7845± 0.0208 0.6342± 0.0387 0.7086± 0.0176SampleStats 0.7767± 0.0130 0.6419± 0.0147 0.6733± 0.0226NNetInc(FtOnly) 0.7543± 0.0144 0.6015± 0.0173 0.7068± 0.0189Raw 0.7392± 0.0173 0.6096± 0.0173 0.6300± 0.0277S = {8, 12, 16, 20, 24} sliding windowsNNetInc(FtOnly) 0.8065± 0.0085 0.6595± 0.0167 0.7296± 0.0135NNet 0.8014± 0.0087 0.6590± 0.0201 0.7204± 0.0187NNetInc(Ft) 0.8012± 0.0151 0.6597± 0.0227 0.7233± 0.0185SampleStats 0.7948± 0.0125 0.6791± 0.0155 0.6886± 0.0197Raw 0.7293± 0.0176 0.6094± 0.0402 0.6216± 0.0813

• Classifier: linear SVM with L2 regularization.

• NNet: Layer 3 hidden unit activations.

• SampleStats: mean, standard deviation, median, extremes, etc.

• 5-fold cross validation to estimate mean performance, 95% confidence intervals.


Example 12-hour respiratory phenotype #1

• Elevated capillary refill rate (CRR), end-tidal CO2 (ETCO2),fraction-inspired O2 (FIO2).

• Complications from chronic bronchopulmonary dysplasia (BPD).


Example 12-hour respiratory phenotype #2

• Elevated CRR, ETCO2, FIO2, heart rate (HR), respiratory rate (RR);declining cognitive function (GCS).

• Common symptoms of pneumonia.


References

1 B. Marlin, D. Kale, R. Khemani, and R. Wetzel. Unsupervised Pattern Discoveryin Electronic Health Care Data Using Probabilistic Clustering Models. Proceedingsof the 2nd ACM SIGHIT International Health Informatics Symposium (IHI), 2012.

2 T.A. Lasko, J.C. Denny, M.A. Levy. Computational Phenotype Discovery UsingUnsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data.PLoS ONE 8 (6): e66341, 2013.

3 Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints:preferred definitions and conceptual framework. Clin Pharmacol Ther, 69 (3):89-95, Mar 2001.

4 Y. Bengio, A. Courville, and P. Vincent. Representation Learning: A Review andNew Perspectives. IEEE Transactions on Pattern Analysis and MachineIntelligence, 35 (8): 1798-1828, Aug 2013.

5 S.V. Desai, T.J. Law, and D.M. Needham. Long-term complications of criticalcare. Crit Care Med, 39 (2): 371-9, Feb 2011.

6 C.A. McKiernan and S.A. Lieberman. Circulatory shock in children: an overview.Pediatrics in Review, 26 (12): 451-460, Dec 2005.

7 M. Denil, et al. Predicting Parameters in Deep Learning. Advances in NeuralInformation Processing Systems 26: 2148-2156, 2013.


Conclusion

Deep learning a powerful tool for computational phenotyping. Next steps:− More experiments: looking for collaborators to share data, validate results!− Incorporate treatment effects; handle missing data, irregular sampling.− Apply to mobile health data and multitask learning.− Other architectures: recurrent and convolutional networks.

Dave Kale: http://www-scf.usc.edu/~dkale/

Yan Liu: http://www-bcf.usc.edu/~liu32/

Melady Lab: time series (health, climate, etc.); active/transfer learning

Whitter Virtual PICU (VPICU): http://vpicu.org/Meaningful Use of Complex Medical Data Symposium: http://mucmd.org/

Support: NSF research grants IIS-1134990 and IIS-1254206, Okawa Foundation Research

Award, Alfred E. Mann Innovation in Engineering Fellowship, Laura P. and Leland K. Whittier

Foundation grant, Children’s Hospital Los Angeles.

Thank you and fight on!


http://www-scf.usc.edu/~dkale/

http://www-bcf.usc.edu/~liu32/

http://www-bcf.usc.edu/~liu32/melady.html

http://vpicu.org/

http://mucmd.org/

Appendix A: critical care medicine

Among the most important areas of medicine.

• >5 million patients admitted to US ICUs annually.3

• Cost: $81.7 billion in US in 2005: 13.4% hospital costs, ∼1% GDP.1

• Mortality rates up to 30%, depending on condition, care, age.1

• Long-term impact: physical impairment, pain, depression [5].

Keys to improving outcomes and reducing costs:

• Rapid, early recognition and diagnosis of underlying illness.

• Targeted, personalized treatment.

3Society of Critical Care Medicine website, Statistics page.Kale (USC/VPICU) Computational Phenotyping November 15, 2014 15 / 22

http://www.sccm.org/Communications/Pages/CriticalCareStats.aspx

Appendix A: challenges of critical care medicine

Among the most challenging clinical decision-making environments.

• Time-pressure: patients can deteriorate rapidly; require fast response.

• Patients generate a data deluge (100’s of data points/patient/hour).

• Symptoms and signs often complex, overlap, vary across patients.

• Diverse conditions: respiratory distress, traumas, cardiovascularfailure, sepsis, shock, etc.

• Patient physiology/responses highly individualized.

Potential solution: computer-aided diagnosis?Requires new definitions of disease and new methods of discovery.









Potential solution: computer-aided diagnosis?

Requires new definitions of disease and new methods of discovery.









Potential solution: computer-aided diagnosis?Requires new definitions of disease and new methods of discovery.


Appendix B: septic shock phenotypes [6, Table 1]

Pathophysiology Signs and Symptoms Treatment

↑ CO, ↓ SVR↑ HR, ↓ BP, ↑ pulses, delayedCR, hyperpnea, MS changes,third-spacing, edema

Repeat boluses of 20 mL/kg crystalloid; may need>60 mL/kg in first hour

Consider colloid if poor response to crystalloidPharmacologic support of BP with dopamine or

norepinephrine

↓ CO, ↑ SVR↑ HR, normal to ↓ BP, ↓ pulses,delayed CR, hyperpnea, MSchanges, third-spacing, edema


Consider colloid if poor response to crystalloidPharmacologic support of CO with dopamine or

epinephrine

↓ CO, ↓ SVR↑ HR, ↓ BP, ↓ pulses, delayedCR, hyperpnea, MS changes,third-spacing, edema


Consider colloid if poor response to crystalloidPharmacologic support of BP and CO with

dopamine or epinephrine

+ Parsimonious, easy to remember.

+ Abstract, flexible.

– Imprecise, overlap, ignore other data.

– Difficult to automate.

Traditional: targeted to human caregivers, discovery driven by anecdote. [2]

Alternative: computational discovery, traditional verification. [1] aaaaaaaaaa


Appendix C: Back-propagation training of neural networks

Loss L(y, y) for predicted output y:

e.g., − (y log(y) + (1− y) log(1− y))

Cost C({W (`)}`) =∑

(x,y)∈D L(y, y)

for y predicted by parameters {W (`)}`.

Update for (j, i)th weight for layer `:

w(`)ij = w

(`)ji − γ

∂C

∂w(`)ji

where γ is learning rate

We compute gradients in topologicalorder, starting at root (output node) andmoving downward toward leaves.


Appendix D: varying length feature extractors

Combining feature extractors of different window lengths T :

Classify time series of any length T :


Appendix D: incremental training of neural networks

Question: can we incrementally learn neural nets of increasing size?

• x(1) = f(W (1)x(0) + b(1))

• Features x(1)i exploit smoothness in data.

• Weights W often low-rank W = UV forW ∈ Rnh×nv , U ∈ Rnh×nα , V ∈ Rnα×nvand nα � nh, nv.

• Denil et al. [7]: W = k>α(Kα + λI)−1

)Wα

• Kα,kα similarity matrices (e.g., covariance)

• Applies to higher layers, too.

Observation: Time series exhibit temporal structure and smoothness.Strategy: Use smaller network’s parameters to initialize larger network.


Appendix D: incremental training of neural networks

Insight: overlap in time series =⇒ overlap in parameters

𝑾1 Δ𝑾𝑛𝑒

Δ𝑾𝑒𝑛 Δ𝑾𝑛𝑛

𝑁1

𝑛

𝐷 𝑑

𝑥

Δ𝑥

𝐷

𝑑

ℎ1

Δℎ

𝑁1

𝑛

𝑏1

Δ𝑏

𝑁1

𝑛

= 𝑓 +

Initialize training for D + d-window with D-window parameters:

1 W1: weight matrix from D-window neural net.

2 ∆Wne: similarity-weighted linear combinations of columns in W1.

3 ∆Wen,∆Wnn: sample from empirical distribution of W1 weights.


Appendix D: incremental vs. classic training

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NNet NNetInc(Ft) NNetInc(FtOnly) NNetInc(Pt)

Method # Epochs Training Time Val. Error Test Error

NNetInc(FtOnly) 73 396.23 28.64% 28.89%NNetInc(Pt) 24.4 419.71 31.08% 30.85%NNet 23.6 471.74 32.23% 32.07%NNetInc(Ft) 51.4 671.24 29.88% 29.91%


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