Ivan Protsyuk
I am a software engineer at Google Health working on the team that builds a mammography screening software to predict breast cancer. My path in Alphabet started in 2018, when I joined DeepMind and started working on a team developing a DNN architecture to predict several adversary clinical events from patients' medical records focussing on acute kidney injury. This work was eventually published in Nature.
Prior to Alphabet, I was a bioinformatician at European Molecular Biology Laboratory (Heidelberg, Germany) developing novel methods for mass-spectrometry-based spatial metabolomics. And before that I was part of the team developing an open-source bioinformatics software suite Unipro UGENE (Novosibirsk, Russia) for the analysis and visualization of genomic data.
I got MS with honours and BS with honours in Physics at Novosibirsk State University.
Prior to Alphabet, I was a bioinformatician at European Molecular Biology Laboratory (Heidelberg, Germany) developing novel methods for mass-spectrometry-based spatial metabolomics. And before that I was part of the team developing an open-source bioinformatics software suite Unipro UGENE (Novosibirsk, Russia) for the analysis and visualization of genomic data.
I got MS with honours and BS with honours in Physics at Novosibirsk State University.
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Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
Nenad Tomašev
Sebastien Baur
Anne Mottram
Xavier Glorot
Jack William Rae
Michal Zielinski
Harry Askham
Andre Saraiva
Valerio Magliulo
Clemens Meyer
Suman Venkatesh Ravuri
Alistair Connell
Cían Hughes
Julien Cornebise
Hugh Montgomery
Geraint Rees
Christopher Laing
Clifton R. Baker
Thomas Osborne
Ruth Reeves
Demis Hassabis
Dominic King
Mustafa Suleyman
Trevor John Back
Christopher Nielsen
Martin Gamunu Seneviratne
Shakir Mohamad
Nature Protocols (2021)
Preview abstract
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks.
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Concept-based model explanations for Electronic Health Records
Diana Mincu
Eric Loreaux
Shaobo Hou
Sebastien Baur
Martin G Seneviratne
Anne Mottram
Nenad Tomasev
Association for Computing Machinery, New York, NY, USA (2021), 36–46
Preview abstract
Recurrent Neural Networks (RNNs) are often used for sequential modeling of adverse outcomes in electronic health records (EHRs) due to their ability to encode past clinical states. These deep, recurrent architectures have displayed increased performance compared to other modeling approaches in a number of tasks, fueling the interest in deploying deep models in clinical settings. One of the key elements in ensuring safe model deployment and building user trust is model explainability. Testing with Concept Activation Vectors (TCAV) has recently been introduced as a way of providing human-understandable explanations by comparing high-level concepts to the network's gradients. While the technique has shown promising results in real-world imaging applications, it has not been applied to structured temporal inputs. To enable an application of TCAV to sequential predictions in the EHR, we propose an extension of the method to time series data. We evaluate the proposed approach on an open EHR benchmark from the intensive care unit, as well as synthetic data where we are able to better isolate individual effects.
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Multi-task prediction of organ dysfunction in the ICU using sequential sub-network routing
Diana Mincu
Eric Loreaux
Anne Mottram
Hugh Montgomery
Ali Connell
Nenad Tomašev
Martin Seneviratne
Journal of the American Medical Informatics Association (JAMIA) (2021)
Preview abstract
Introduction:
Multi-task learning (MTL) using electronic health records (EHRs) allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential sub-network routing (SeqSNR) architecture which uses soft parameter sharing to find related tasks and encourage cross-learning between them.
Materials and Methods:
Using the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we train deep neural network models to predict the onset of six endpoints including specific organ dysfunctions and general clinical outcomes: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay. We compare single task models (ST) with naive multi-task (shared bottom, SB) and SeqSNR in terms of discriminative performance and label efficiency.
Results:
SeqSNR showed a modest yet statistically significant performance boost across at least 4 out of 6 tasks compared to SB and ST. When the size of the training dataset was reduced for a given task, SeqSNR outperformed ST for all cases showing an average AU PRC boost of 2.1%, 2.9%, and 2.1% for tasks using 1%, 5%, and 10% of labels respectively.
Discussion and Conclusion:
Multi-task learning has variable performance compared to single-task learning, with the possibility for negative transfer. The SeqSNR architecture outperforms SB and ST in discriminative performance and shows superior performance in terms of label efficiency. SeqSNR should be considered for multi-task predictive modeling using EHR data.
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A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomašev
Xavier Glorot
Jack W Rae
Michal Zielinski
Harry Askham
Andre Saraiva
Anne Mottram
Clemens Meyer
Suman Ravuri
Alistair Connell
Cían O Hughes
Julien Cornebise
Hugh Montgomery
Geraint Rees
Chris Laing
Clifton R Baker
Kelly Peterson
Ruth Reeves
Demis Hassabis
Dominic King
Mustafa Suleyman
Trevor Back
Christopher Nielson
Shakir Mohamed
Nature, 572 (2019), pp. 116-119
Preview abstract
The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records and using acute kidney injury—a common and potentially life-threatening condition—as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment.
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