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

Developments in ROOT I/O and trees

For the last several months the main focus of development in the ROOT I/O package has been code consolidation and performance improvements. Access to remote files is affected both by bandwidth and latency. We introduced a pre-fetch mechanism to minimize the number of transactions between client and server and hence reducing the effect of latency. We will review the implementation and how well it works in different conditions (gain of an order of magnitude for remote file access). We will also review new utilities, including a faster implementation of TTree cloning (gain of an order of magnitude), a generic mechanism for object references, and a new entry list mechanism tuned both for small and large number of selections. In addition to reducing the coupling with the core module and becoming its owns library (libRIO) (as part of the general restructuration of the ROOT libraries), the I/O package has been enhanced in the area of XML and SQL support, thread safety, schema evolution, TTreeFormula, and many other areas. We will also discuss various ways, ROOT will be able to benefit from multi-core architecture to improve I/O performances

Проблемы формирования государственной экологической политики в условиях ограниченности ресурсов

Проблема ограниченности ресурсов является основой развития экономики. Однако также ограниченность оказывает влияние и на экологическую политику в стране. В статье рассмотрены ключевые механизмы формирования государственной экологической политики, экологического менеджмента; предложены пути повышения эффективности экологической политики.The problem of limited resources is the basis for economic development. However, the limited nature also influences the environmental policy in the country. The article considers the key mechanisms for the formation of state environmental policy, environmental management; ways to improve the effectiveness of environmental policy

Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches

PMCID: PMC3668194SEP was directly funded by the National Institute for Health Research Cardiovascular Biomedical Research Unit at Barts. SN acknowledges support from the Oxford NIHR Biomedical Research Centre and from the Oxford British Heart Foundation Centre of Research Excellence. SP and PL are funded by a BHF Senior Clinical Research fellowship. RC is supported by a BHF Research Chair and acknowledges the support of the Oxford BHF Centre for Research Excellence and the MRC and Wellcome Trust. PMM gratefully acknowledges training fellowships supporting his laboratory from the Wellcome Trust, GlaxoSmithKline and the Medical Research Council