Advances in quantum machine learning

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.Comment: 38 pages, 17 Figure

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.peer-reviewe

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

A new risk prediction model for critical care: the Intensive Care National Audit & Research Centre (ICNARC) model.

OBJECTIVE: To develop a new model to improve risk prediction for admissions to adult critical care units in the UK. DESIGN: Prospective cohort study. SETTING: The setting was 163 adult, general critical care units in England, Wales, and Northern Ireland, December 1995 to August 2003. PATIENTS: Patients were 216,626 critical care admissions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The performance of different approaches to modeling physiologic measurements was evaluated, and the best methods were selected to produce a new physiology score. This physiology score was combined with other information relating to the critical care admission-age, diagnostic category, source of admission, and cardiopulmonary resuscitation before admission-to develop a risk prediction model. Modeling interactions between diagnostic category and physiology score enabled the inclusion of groups of admissions that are frequently excluded from risk prediction models. The new model showed good discrimination (mean c index 0.870) and fit (mean Shapiro's R 0.665, mean Brier's score 0.132) in 200 repeated validation samples and performed well when compared with recalibrated versions of existing published risk prediction models in the cohort of patients eligible for all models. The hypothesis of perfect fit was rejected for all models, including the Intensive Care National Audit & Research Centre (ICNARC) model, as is to be expected in such a large cohort. CONCLUSIONS: The ICNARC model demonstrated better discrimination and overall fit than existing risk prediction models, even following recalibration of these models. We recommend it be used to replace previously published models for risk adjustment in the UK

The Acute Care Undergraduate TEaching (ACUTE) Initiative : consensus development of core competencies in acute care for undergraduates in the United Kingdom

Background: The care of the acutely ill patient in hospital is often sub-optimal. Poor recognition of critical illness combined with a lack of knowledge, failure to appreciate the clinical urgency of a situation, a lack of supervision, failure to seek advice and poor communication have been identified as contributory factors. At present the training of medical students in these important skills is fragmented. The aim of this study was to use consensus techniques to identify the core competencies in the care of acutely ill or arrested adult patients that medical students should possess at the point of graduation. Design: Healthcare professionals were invited to contribute suggestions for competencies to a website as part of a modified Delphi survey. The competency proposals were grouped into themes and rated by a nominal group comprised of physicians, nurses and students from the UK. The nominal group rated the importance of each competency using a 5-point Likert scale. Results: A total of 359 healthcare professionals contributed 2,629 competency suggestions during the Delphi survey. These were reduced to 88 representative themes covering: airway and oxygenation; breathing and ventilation; circulation; confusion and coma; drugs, therapeutics and protocols; clinical examination; monitoring and investigations; team-working, organisation and communication; patient and societal needs; trauma; equipment; pre-hospital care; infection and inflammation. The nominal group identified 71 essential and 16 optional competencies which students should possess at the point of graduation. Conclusions: We propose these competencies form a core set for undergraduate training in resuscitation and acute care. © Springer-Verlag 2005