Target informed client recruitment for efficient federated learning in healthcare.

Background: Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data.

Risk factors for SARS-CoV-2 infection and severe COVID-19 in unvaccinated solid organ transplant recipients.

The role of immunosuppressive therapy on SARS-CoV-2 infection risk and COVID-19 severity remains unclear in unvaccinated solid organ transplant recipients. We included 1957 organ transplant recipients between July 2020 and April 2021 to analyze whether baseline immunosuppressive therapy and other risk factors are associated with SARS-CoV-2 infection and severe COVID-19. In total, 247 (12.

Hierarchical Biclustering of Mouse Pancreas Mass Spectrometry Imaging Data Using Recursive Rank-2 Non-negative Matrix Factorization.

One of the main challenges in mass spectrometry imaging data analysis remains the analysis of /-spectra displaying a low signal-to-noise ratio caused by their low abundance, sample preparation, matrix effects, fragmentation, and other artifacts. Additionally, we observe that molecules with a high abundance suppress those with lower intensities and misdirect classical tools for MSI data analysis, such as principal component analysis. As a result, the observed significance of a molecule may not always be directly related to its abundance.

Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma.

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis.

The Feasibility of Semi-Continuous and Multi-Frequency Thoracic Bioimpedance Measurements by a Wearable Device during Fluid Changes in Hemodialysis Patients.

Repeated single-point measurements of thoracic bioimpedance at a single (low) frequency are strongly related to fluid changes during hemodialysis. Extension to semi-continuous measurements may provide longitudinal details in the time pattern of the bioimpedance signal, and multi-frequency measurements may add in-depth information on the distribution between intra- and extracellular fluid. This study aimed to investigate the feasibility of semi-continuous multi-frequency thoracic bioimpedance measurements by a wearable device in hemodialysis patients.

Using simple clinical and ultrasound variables to develop a model to predict first trimester pregnancy viability.

Introduction: Early prediction of pregnancies destined to miscarry will allow couples to prepare for this common but often unexpected eventuality, and clinicians to allocate finite resources. We aimed to develop a prediction model combining clinical, demographic, and sonographic data as a clinical tool to aid counselling about first trimester pregnancy outcome.

Material And Methods: This is a prospective, observational cohort study conducted at Queen Charlotte's and Chelsea Hospital, UK from March 2014 to May 2019.

Comparison of whole body versus thoracic bioimpedance in relation to ultrafiltration volume and systolic blood pressure during hemodialysis.

In contrast to whole body bioimpedance, which estimates fluid status at a single point in time, thoracic bioimpedance applied by a wearable device could enable continuous measurements. However, clinical experience with thoracic bioimpedance in patients on dialysis is limited. To test the reproducibility of whole body and thoracic bioimpedance measurements and to compare their relationship with hemodynamic changes during hemodialysis, these parameters were measured pre- and end-dialysis in 54 patients during two sessions.

Peptides From the Variable Domain of Immunoglobulin G as Biomarkers in Chronic Inflammatory Demyelinating Polyradiculoneuropathy.

Background And Objectives: Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a clinically heterogeneous immune-mediated disease. Diagnostic biomarkers for CIDP are currently lacking. Peptides derived from the variable domain of circulating immunoglobulin G (IgG) have earlier been shown to be shared among patients with the same immunologic disease.

Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis.

Mass Spectrometry Imaging (MSI) is a technique used to identify the spatial distribution of molecules in tissues. An MSI experiment results in large amounts of high dimensional data, so efficient computational methods are needed to analyze the output. Topological Data Analysis (TDA) has proven to be effective in all kinds of applications.

Testing the functional reserve of the kidney before hematopoietic stem cell transplantation: doubt remains.

Acute kidney injury is a common and important complication following hematopoietic stem cell transplantation. In the nephrology community, acute kidney injury is no longer viewed as a simple temporary and potentially reversible decline in kidney clearance as acute kidney injury imposes a risk for immediate and future complications. Therefore, stratifying patients for the risk of acute kidney injury following stem cell transplantation would be very helpful to optimize peri-stem cell transplant management and could potentially improve outcomes in this patient population.

System Architecture of a European Platform for Health Policy Decision Making: MIDAS.

Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner.

A Resampling Method to Improve the Prognostic Model of End-Stage Kidney Disease: A Better Strategy for Imbalanced Data.

Background: Prognostic models can help to identify patients at risk for end-stage kidney disease (ESKD) at an earlier stage to provide preventive medical interventions. Previous studies mostly applied the Cox proportional hazards model. The aim of this study is to present a resampling method, which can deal with imbalanced data structure for the prognostic model and help to improve predictive performance.

Forecasting of Patient-Specific Kidney Transplant Function With a Sequence-to-Sequence Deep Learning Model.

Importance: Like other clinical biomarkers, trajectories of estimated glomerular filtration rate (eGFR) after kidney transplant are characterized by intra-individual variability. These fluctuations hamper the distinction between alarming graft functional deterioration or harmless fluctuation within the patient-specific expected reference range of eGFR.

Objective: To determine whether a deep learning model could accurately predict the patient-specific expected reference range of eGFR after kidney transplant.

Gradient boosted trees with individual explanations: An alternative to logistic regression for viability prediction in the first trimester of pregnancy.

Background: Clinical models to predict first trimester viability are traditionally based on multivariable logistic regression (LR) which is not directly interpretable for non-statistical experts like physicians. Furthermore, LR requires complete datasets and pre-established variables specifications. In this study, we leveraged the internal non-linearity, feature selection and missing values handling mechanisms of machine learning algorithms, along with a post-hoc interpretability strategy, as potential advantages over LR for clinical modeling.

An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge.

Background: The use of Electronic Health Records (EHR) data in clinical research is incredibly increasing, but the abundancy of data resources raises the challenge of data cleaning. It can save time if the data cleaning can be done automatically. In addition, the automated data cleaning tools for data in other domains often process all variables uniformly, meaning that they cannot serve well for clinical data, as there is variable-specific information that needs to be considered.

A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data.

Rationale: Non-negative matrix factorization (NMF) has been used extensively for the analysis of mass spectrometry imaging (MSI) data, visualizing simultaneously the spatial and spectral distributions present in a slice of tissue. The statistical framework offers two related NMF methods: probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), which is a generative model. This work offers a mathematical comparison between NMF, PLSA, and LDA, and includes a detailed evaluation of Kullback-Leibler NMF (KL-NMF) for MSI for the first time.

An ensemble-based feature selection framework to select risk factors of childhood obesity for policy decision making.

Background: The increasing prevalence of childhood obesity makes it essential to study the risk factors with a sample representative of the population covering more health topics for better preventive policies and interventions. It is aimed to develop an ensemble feature selection framework for large-scale data to identify risk factors of childhood obesity with good interpretability and clinical relevance.

Methods: We analyzed the data collected from 426,813 children under 18 during 2000-2019.

Data-driven Derivation and Validation of Novel Phenotypes for Acute Kidney Transplant Rejection using Semi-supervised Clustering.

Background: Over the past decades, an international group of experts iteratively developed a consensus classification of kidney transplant rejection phenotypes, known as the Banff classification. Data-driven clustering of kidney transplant histologic data could simplify the complex and discretionary rules of the Banff classification, while improving the association with graft failure.

Methods: The data consisted of a training set of 3510 kidney-transplant biopsies from an observational cohort of 936 recipients.

Spatially aware clustering of ion images in mass spectrometry imaging data using deep learning.

Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction. The advent of deep learning has greatly facilitated such complex spatial analysis.

Diuretic response and effects of diuretic omission in ambulatory heart failure patients on chronic low-dose loop diuretic therapy.

Aims: To study loop diuretic response and effect of loop diuretic omission in ambulatory heart failure (HF) patients on chronic low-dose loop diuretics.

Methods And Results: Urine collections were performed on two consecutive days in 40 ambulatory HF patients with 40-80 mg furosemide (day 1 with loop diuretic; day 2 without loop diuretic). Three phases were collected each day: (i) first 6 h; (ii) rest of the day; and (iii) night.

Correspondence-Aware Manifold Learning for Microscopic and Spatial Omics Imaging: A Novel Data Fusion Method Bringing Mass Spectrometry Imaging to a Cellular Resolution.

High-dimensional molecular measurements are transforming the field of pathology into a data-driven discipline. While hematoxylin and eosin (H&E) stainings are still the gold standard to diagnose diseases, the integration of microscopic and molecular information is becoming crucial to advance our understanding of tissue heterogeneity. To this end, we propose a data fusion method that integrates spatial omics and microscopic data obtained from the same tissue slide.

Initiation and termination of dialysis in older patients with advanced cancer: providing guidance in a complicated situation.

Cancer and chronic kidney disease prevalence both increase with age. As a consequence, physicians are more frequently encountering older people with cancer who need dialysis, or patients on dialysis diagnosed with cancer. Decisions in this context are particularly complex and multifaceted.

Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining.

Background: The prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration.

Methods: We used a Belgian database collected by extracting coded parameters and more than 100 chronic conditions from the Electronic Health Records of general practitioners to study patients older than 40 years with multiple diagnoses between 1991 and 2015 (N = 65 939).

Prioritization of /-Values in Mass Spectrometry Imaging Profiles Obtained Using Uniform Manifold Approximation and Projection for Dimensionality Reduction.

Mass spectrometry imaging (MSI) is a promising technique to assess the spatial distribution of molecules in a tissue sample. Nonlinear dimensionality reduction methods such as Uniform Manifold Approximation and Projection (UMAP) can be very valuable for the visualization of the massive data sets produced by MSI. These visualizations can offer us good initial insights regarding the heterogeneity and variety of molecular patterns present in the data, but they do not discern which molecules might be driving these observations.