Predicting Nephrotoxic Acute Kidney Injury in Hospitalized Adults: A Machine Learning Algorithm.

Rationale And Objective: Acute kidney injury (AKI) is a common complication among hospitalized adults, but AKI prediction and prevention among adults has proved challenging. We used machine learning to update the nephrotoxic injury negated by just-in time action (NINJA), a pediatric program that predicts nephrotoxic AKI, to improve accuracy among adults.

Study Design: A retrospective cohort study.

Harnessing Natural Language Processing and High-Dimensional Clinical Notes to Detect Goals-of-Care and Surrogate-Designation Conversations.

Advance care planning, involving goals-of-care and surrogate-designation conversations, is crucial for patient-centered care. However, determining the optimal timing and participants for these conversations remains challenging. This study explored the frequency, timing, and predictors of documenting two advance care planning elements, goals-of-care and surrogate-designation conversations, in clinical notes for patients with advanced illness.

Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.

Purpose: Ability to predict symptom severity and progression across treatment trajectories would allow clinicians to provide timely intervention and treatment planning. However, such predictions are difficult because of sparse and inconsistent assessment, and simplistic measures such as the last observed symptom severity are often used. The purpose of this study is to develop a model for predicting future cancer symptom experiences on the basis of past symptom experiences.

Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department.

Introduction: Delirium is a cerebral dysfunction seen commonly in the acute care setting. It is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) and inpatient care by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions in the hospital setting.

Optimizing warfarin dosing using deep reinforcement learning.

Warfarin is a widely used anticoagulant, and has a narrow therapeutic range. Dosing of warfarin should be individualized, since slight overdosing or underdosing can have catastrophic or even fatal consequences. Despite much research on warfarin dosing, current dosing protocols do not live up to expectations, especially for patients sensitive to warfarin.

Longitudinal Subgrouping of Patients With Cancer Based on Symptom Experiences: An Integrative Review.

Problem Identification: The purpose of this integrative review is to identify literature describing (a) subgrouping patients with cancer based on symptom experiences and their patterns of symptom changes over time and (b) methodologies of subgrouping patients with cancer based on symptom experiences.

Literature Search: PubMed®, CINAHL®, and PsycINFO® were searched through January 2019.

Data Evaluation: Studies were appraised for patterns of symptom change over time and methodologic approach using the QualSyst quality rating scale.

Machine learning approaches to predict the 1-year-after-initial-AMI survival of elderly patients.

Background: While multiple randomized controlled trials (RCTs) are available, their results may not be generalizable to older, unhealthier or less-adherent patients. Observational data can be used to predict outcomes and evaluate treatments; however, exactly which strategy should be used to analyze the outcomes of treatment using observational data is currently unclear. This study aimed to determine the most accurate machine learning technique to predict 1-year-after-initial-acute-myocardial-infarction (AMI) survival of elderly patients and to identify the association of angiotensin-converting- enzyme inhibitors and angiotensin-receptor blockers (ACEi/ARBs) with survival.

Modest Clostridiodes difficile infection prediction using machine learning models in a tertiary care hospital.

Previous studies have shown promising results of machine learning (ML) models for predicting health outcomes. We develop and test ML models for predicting Clostridioides difficile infection (CDI) in hospitalized patients. This is a retrospective cohort study conducted during 2015-2017.

Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview.

Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes.

Modeling and maximizing influence diffusion in social networks for viral marketing.

Modeling influence diffusion in social networks is an important challenge. We investigate influence-diffusion modeling and maximization in the setting of viral marketing, in which a node's influence is measured by the number of nodes it can activate to adopt a new technology or purchase a new product. One of the fundamental problems in viral marketing is to find a small set of initial adopters who can trigger the most further adoptions through word-of-mouth-based influence propagation in the network.

Sustainable use of silver warehou (): effects of storage, processing conditions and simulated gastrointestinal digestion on selected in-vitro bioactivities.

Australian underutilised fish species may serve as a potential source of valuable proteins and potent bioactive peptides. This novel research is the first to investigate the effects of storage-processing conditions and an in-vitro simulated gastrointestinal digestion (pepsin-pancreatin) on bioactive peptides' release during storage of fish fillet, derived from Australian silver warehou (). In-vitro bioactivities including angiotensin-converting enzyme and trypsin inhibitory and antioxidant activities were analysed.

Examining reference frame interaction in spatial memory using a distribution analysis.

Previous research showed competition among reference frames in spatial attention and language. The present studies developed a new distribution analysis to examine reference frame interactions in spatial memory. Participants viewed virtual arrays of colored pegs and were instructed to remember them either from their own perspective or from the perspective aligned with the rectangular floor.

Providing views of the driving scene to drivers' conversation partners mitigates cell-phone-related distraction.

Cell-phone use impairs driving safety and performance. This impairment may stem from the remote partner's lack of awareness about the driving situation. In this study, pairs of participants completed a driving simulator task while conversing naturally in the car and while talking on a hands-free cell phone.

Differentiating spatial memory from spatial transformations.

The perspective-taking task is one of the most common paradigms used to study the nature of spatial memory, and better performance for certain orientations is generally interpreted as evidence of spatial representations using these reference directions. However, performance advantages can also result from the relative ease in certain transformations/rotations. To differentiate spatial memory from spatial transformations, the present study took a new approach based on the hypothesis that responses may be biased toward the original representation but not a transformed one.

Use of music in the treatment of insomnia: a pilot study.

The purpose of this pilot study was to evaluate the use of music to improve quality of sleep. Adults with insomnia listened to music at bedtime for 30 days. Measured by the Pittsburg Sleep Quality Index, results indicated that the use of music significantly increased sleep quality.

Activity of endogenous muscle proteases from 4 Australian underutilized fish species as affected by ionic strength, pH, and temperature.

Storage conditions may influence the hydrolytic activity of endogenous muscle enzymes postmortem, rate of autolysis of myofibrillar proteins, and biological properties of hydrolyzed end products. This study investigated the effect of ionic strength, pH, and temperature on the activity of endogenous calpain-like, cathepsins B and B+L measured in crude extract obtained from deepwater flathead, silver warehou, ribaldo, and ribbonfish muscles. Activity of calpain-like enzymes in 3 examined species was significantly higher at pH 6.

Adverse drug effect detection.

Large collections of electronic patient records provide abundant but under-explored information on the real-world use of medicines. Although they are maintained for patient administration, they provide a broad range of clinical information for data analysis. One growing interest is drug safety signal detection from these longitudinal observational data.

What counts as the evidence for three-dimensional and four-dimensional spatial representations?

The dimension of spatial representations can be assessed by above-chance performance in novel shortcut or spatial reasoning tasks independent of accuracy levels, systematic biases, mosaic/segmentation across space, separate coding of individual dimensions, and reference frames. Based on this criterion, humans and some other animals exhibited sufficient evidence for the existence of three-dimensional and/or four-dimensional spatial representations.

Proteolytic activities in fillets of selected underutilized Australian fish species.

The hydrolytic activity of major endogenous proteases, responsible for proteolysis of myofibrillar proteins during post-mortem storage, may be an indicator of the textural quality of fish which influences consumer purchasing behaviour and thus market value of the final product. Furthermore, it may also influence the type and bioactive properties of the peptides released during post-mortem proteolysis of myofibrillar proteins. This study compared the activities of cathepsins B, B+L, D, H and calpain-like enzymes in crude muscle extracted from 16 Australian underutilized fish species.

Building a computer program to support children, parents, and distraction during healthcare procedures.

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models.

Change blindness and inattentional blindness.

Change blindness and inattentional blindness are both failures of visual awareness. Change blindness is the failure to notice an obvious change. Inattentional blindness is the failure to notice the existence of an unexpected item.

Object recognition in Williams syndrome: uneven ventral stream activation.

Williams syndrome (WS) is a genetic disorder associated with severe visuospatial deficits, relatively strong language skills, heightened social interest, and increased attention to faces. On the basis of the visuospatial deficits, this disorder has been characterized primarily as a deficit of the dorsal stream, the occipitoparietal brain regions that subserve visuospatial processing. However, some evidence indicates that this disorder may also affect the development of the ventral stream, the occipitotemporal cortical regions that subserve face and object recognition.

A decision support system for cost-effective diagnosis.

Objective: Speed, cost, and accuracy are three important goals in disease diagnosis. This paper proposes a machine learning-based expert system algorithm to optimize these goals and assist diagnostic decisions in a sequential decision-making setting.

Methods: The algorithm consists of three components that work together to identify the sequence of diagnostic tests that attains the treatment or no test threshold probability for a query case with adequate certainty: lazy-learning classifiers, confident diagnosis, and locally sequential feature selection (LSFS).

Recovery of environmental human DNA by insects.

We tested the hypotheses that foraging insects can acquire human DNA from the environment and that insect-delivered human DNA is of sufficient quantity and quality to permit standard forensic analyses. Houseflies, German cockroaches, and camel crickets were exposed to dusty surfaces and then assayed for human mitochondrial and nuclear loci by conventional and qPCR, and multiplex STR amplification. Over two experiments, 100% of insect groups and 94% of dust controls tested positive for human DNA.