FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks

Federated Learning (FL) and Split Learning (SL) are privacy-preserving Machine-Learning (ML) techniques that enable training ML models over data distributed among clients without requiring direct access to their raw data. Existing FL and SL approaches work on horizontally or vertically partitioned data and cannot handle sequentially partitioned data where segments of multiple-segment sequential data are distributed across clients. In this paper, we propose a novel federated split learning framework, FedSL, to train models on distributed sequential data. The most common ML models to train on sequential data are Recurrent Neural Networks (RNNs). Since the proposed framework is privacy preserving, segments of multiple-segment sequential data cannot be shared between clients or between clients and server. To circumvent this limitation, we propose a novel SL approach tailored for RNNs. A RNN is split into sub-networks, and each sub-network is trained on one client containing single segments of multiple-segment training sequences. During local training, the sub-networks on different clients communicate with each other to capture latent dependencies between consecutive segments of multiple-segment sequential data on different clients, but without sharing raw data or complete model parameters. After training local sub-networks with local sequential data segments, all clients send their sub-networks to a federated server where sub-networks are aggregated to generate a global model. The experimental results on simulated and real-world datasets demonstrate that the proposed method successfully train models on distributed sequential data, while preserving privacy, and outperforms previous FL and centralized learning approaches in terms of achieving higher accuracy in fewer communication rounds

Length of Stay prediction for Hospital Management using Domain Adaptation

Inpatient length of stay (LoS) is an important managerial metric which if known in advance can be used to efficiently plan admissions, allocate resources and improve care. Using historical patient data and machine learning techniques, LoS prediction models can be developed. Ethically, these models can not be used for patient discharge in lieu of unit heads but are of utmost necessity for hospital management systems in charge of effective hospital planning. Therefore, the design of the prediction system should be adapted to work in a true hospital setting. In this study, we predict early hospital LoS at the granular level of admission units by applying domain adaptation to leverage information learned from a potential source domain. Time-varying data from 110,079 and 60,492 patient stays to 8 and 9 intensive care units were respectively extracted from eICU-CRD and MIMIC-IV. These were fed into a Long-Short Term Memory and a Fully connected network to train a source domain model, the weights of which were transferred either partially or fully to initiate training in target domains. Shapley Additive exPlanations (SHAP) algorithms were used to study the effect of weight transfer on model explanability. Compared to the benchmark, the proposed weight transfer model showed statistically significant gains in prediction accuracy (between 1% and 5%) as well as computation time (up to 2hrs) for some target domains. The proposed method thus provides an adapted clinical decision support system for hospital management that can ease processes of data access via ethical committee, computation infrastructures and time

A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources

Federated learning is an appealing framework for analyzing sensitive data from distributed health data networks due to its protection of data privacy. Under this framework, data partners at local sites collaboratively build an analytical model under the orchestration of a coordinating site, while keeping the data decentralized. However, existing federated learning methods mainly assume data across sites are homogeneous samples of the global population, hence failing to properly account for the extra variability across sites in estimation and inference. Drawing on a multi-hospital electronic health records network, we develop an efficient and interpretable tree-based ensemble of personalized treatment effect estimators to join results across hospital sites, while actively modeling for the heterogeneity in data sources through site partitioning. The efficiency of our method is demonstrated by a study of causal effects of oxygen saturation on hospital mortality and backed up by comprehensive numerical results

An eICU/ICU Collaborative to Reduce Sepsis Mortality

Sepsis costs over 20 billion dollars annually to treat making it the most expensive diagnosis for hospitals (Afrefian, et al., 2017) and carries with it an average mortality rate of 45% (SCCM, 2016). The eICU/ICU collaborative project was developed to improve sepsis mortality at Sutter Health’s Solano hospital affiliate from 41.2% to the system-wide goal of 18.8% over the course of a year by implementing two technologies. The first was the onboarding of the non-invasive cardiac output monitoring (NICOM) technology by Sutter Solano to fulfill the 6-hour bundle compliance for septic shock resuscitation. The other technology was the activation and enhancement of the Core Measure Manager (CMM) high-quality data surveillance technology by Sutter’s eICU to screen all patients at Sutter Solano Medical Center for early identification and treatment of sepsis and septic shock. After twelve months of quality improvement measures including education, training, implementation, enhancement, tracking and treatment management; the dashboards revealed Sutter Solano’s sepsis/septic shock mortality rate dropped from 41.2% to 6.1%. Nurses and physicians need to recognize that central venous pressure (CVP) is no longer a recommended or accepted measure of hemodynamic stability. The latest evidence-based practice supports NICOM in conjunction with passive leg raise (PLR) as a foundational guideline for fluid resuscitation. The Clinical Nurse Leader (CNL), as systems analyst and risk anticipator, must manage information as well as the care environment to improve quality patient outcomes in the presence of evolving knowledge and the ever-changing healthcare system (AACN, 2013)

Soft Phenotyping for Sepsis via EHR Time-aware Soft Clustering

Sepsis is one of the most serious hospital conditions associated with high mortality. Sepsis is the result of a dysregulated immune response to infection that can lead to multiple organ dysfunction and death. Due to the wide variability in the causes of sepsis, clinical presentation, and the recovery trajectories identifying sepsis sub-phenotypes is crucial to advance our understanding of sepsis characterization, identifying targeted treatments and optimal timing of interventions, and improving prognostication. Prior studies have described different sub-phenotypes of sepsis with organ-specific characteristics. These studies applied clustering algorithms to electronic health records (EHRs) to identify disease sub-phenotypes. However, prior approaches did not capture temporal information and made uncertain assumptions about the relationships between the sub-phenotypes for clustering procedures. We develop a time-aware soft clustering algorithm guided by clinical context to identify sepsis sub-phenotypes using data from the EHR. We identified six novel sepsis hybrid sub-phenotypes and evaluated them for medical plausibility. In addition, we built an early-warning sepsis prediction model using logistic regression. Our results suggest that these novel sepsis hybrid sub-phenotypes are promising to provide more precise information on the recovery trajectory which can be important to inform management decisions and sepsis prognosis

Conditional survival with increasing duration of ICU admission: an observational study of three intensive care databases.

OBJECTIVES: Prolonged admissions to an ICU are associated with high resource utilization and personal cost to the patient. Previous reports suggest increasing length of stay may be associated with poor outcomes. Conditional survival represents the probability of future survival after a defined period of treatment on an ICU providing a description of how prognosis evolves over time. Our objective was to describe conditional survival as length of ICU stay increased. DESIGN: Retrospective observational cohort study of three large intensive care databases. SETTING: Three intensive care databases, two in the United States (Medical Information Mart for Intensive Care III and electronic ICU) and one in United Kingdom (Post Intensive Care Risk-Adjusted Alerting and Monitoring). PATIENTS: Index admissions to intensive care for patients 18 years or older. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 11,648, 38,532, and 165,125 index admissions were analyzed from Post Intensive Care Risk-Adjusted Alerting and Monitoring, Medical Information Mart for Intensive Care III and electronic ICU databases respectively. In all three cohorts, conditional survival declined over the first 5-10 days after ICU admission and changed little thereafter. In patients greater than or equal to 75 years old conditional survival continued to decline with increasing length of stay. CONCLUSIONS: After an initial period of 5-10 days, probability of future survival does not appear to decrease with increasing length of stay in unselected patients admitted to ICUs in United Kingdom and United States [corrected]. These findings were consistent between the three populations and suggest that a prolonged admission to an ICU is not a reason for a pessimism in younger patients but may indicate a poor prognosis in the older population

Representation Learning With Autoencoders For Electronic Health Records

Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this research, we propose a predictive modeling approach based on deep feature representations and word embedding techniques. Our method uses different deep architectures (stacked sparse autoencoders, deep belief network, adversarial autoencoders and variational autoencoders) for feature representation in higher-level abstraction to obtain effective and robust features from EHRs, and then build prediction models on top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled data is scarce. We investigate the performance of representation learning through a supervised learning approach. Our focus is to present a comparative study to evaluate the performance of different deep architectures through supervised learning and provide insights for the choice of deep feature representation techniques. Our experiments demonstrate that for small data sets, stacked sparse autoencoder demonstrates a superior generality performance in prediction due to sparsity regularization whereas variational autoencoders outperform the competing approaches for large data sets due to its capability of learning the representation distribution