Decision time for clinical decision support systems

Clinical decision support systems are interactive software systems designed to assist clinicians with decision making tasks, such as determining a diagnosis or recommending a treatment for a patient. Clinical decision support systems are a widely researched topic in the Computer Science community but their inner workings are less well understood by and known to clinicians. In this article we provide a brief explanation of clinical decision support systems and provide some examples of real world systems. We also describe some of the challenges to implementing these systems in clinical environments and posit some of the reasons for limited adoption of decision support systems in practice. We aim to engage clinicians in the development of decision support system that can meaningfully help with their decision making tasks and open up a discussion about the future of automated clinical decision support as a part of healthcare delivery

Real-world Gyroscope-based Gait Event Detection and Gait Feature Extraction

Falls in older adults are a major clinical problem often resulting in serious injury. The costly nature of clinic-based testing for the propensity of falling and a move towards homebased care and monitoring of older adults has led to research in wearable sensing technologies for identifying fall-related parameters from activities of daily living. This paper discusses the development of two algorithms for identifying periods of walking (gait events) and extracting characteristic patterns for each gait event (gait features) with a view to identifying the propensity to fall in older adults. In this paper, we present an evaluation of the algorithms involving a small real-world dataset collected from healthy adults in an uncontrolled environment. 92.5% of gait events were extracted from lower leg gyroscope data from 5 healthy adults (total duration of 33 hours) and over 95% of the gait characteristic points were identified in this data. A user interface to aid clinicians review gait features from walking events captured over multiple days is also proposed. The work presents initial steps in the development of a platform for monitoring patients within their daily routine in uncontrolled environments to inform clinical decision-making related to falls

The impact of liver disease: a leading cause of hospital admissions in people living vith HIV

Background: This study reviews recent trends of HIV inpatient admissions over 5 Infectious diseases Units in Liguria, in 2012. Patients and Methods: Five infectious diseases Units in Liguria, Italy, collected data on inpatient HIV admissions from January to December 2012, including patient demographic, discharge diagnosis, CD4 Tcell count, viral load (VL) and combined anti-retroviral treatment (cART). Results: Rate of patient admissions per 100 years was 6.12 (number=257), in 62.6% (n=161) of admissions a VL under 50 copies/ml was observed. Furthermore, 86.4% (n=222) of admissions were on active cART. Median age was 49 years. Mortality rate was 10.2%. Hepatitis C coinfection occurred in 64.6% of patients (n=166). The most common diagnosis was infectious diseases (29.1%), respiratory diseases (16.6%) and neoplasms (15.%). Chronic HCV infection and its complications (cirrhosis and hepatocellular carcinoma) accounted for 31% of all discharging diagnosis. Conclusions: The majority of inpatients admitted during 2012 in our Units were on cART and virologically suppressed. The complications of hepatitis C coinfection have a major impact on mortality rates and hospitalization rates in Italy. According to these observations, the availability of new drugs for chronic hepatitis C imposes a further effort to improve the quality of life of our patients

A deep learning application to map weed spatial extent from unmanned aerial vehicles imagery

Weed infestation is a global threat to agricultural productivity, leading to low yields and financial losses. Weed detection, based on applying machine learning to imagery collected by Unmanned Aerial Vehicles (UAV) has shown potential in the past; however, validation on large data-sets (e.g., across a wide number of different fields) remains lacking, with few solutions actually made operational. Here, we demonstrate the feasibility of automatically detecting weeds in winter wheat fields based on deep learning methods applied to UAV data at scale. Focusing on black-grass (the most pernicious weed across northwest Europe), we show high performance (i.e., accuracy above 0.9) and highly statistically significant correlation (i.e., ro > 0.75 and p < 0.00001) between imagery-derived local and global weed maps and out-of-bag field survey data, collected by experts over 31 fields (205 hectares) in the UK. We demonstrate how the developed deep learning model can be made available via an easy-to-use docker container, with results accessible through an interactive dashboard. Using this approach, clickable weed maps can be created and deployed rapidly, allowing the user to explore actual model predictions for each field. This shows the potential for this approach to be used operationally and influence agronomic decision-making in the real world

Goal-Driven Structured Argumentation for Patient Management in a Multimorbidity Setting

We use computational argumentation to both analyse and generate solutions for reasoning in multimorbidity about consistent recommendations, according to different patient-centric goals. Reasoning in this setting carries a complexity related to the multiple variables involved. These variables reflect the co-existing health conditions that should be considered when defining a proper therapy. However, current Clinical Decision Support Systems (CDSSs) are not equipped to deal with such a situation. They do not go beyond the straightforward application of the rules that build their knowledge base and simple interpretation of Computer-Interpretable Guidelines (CIGs). We provide a computational argumentation system equipped with goal-seeking mechanisms to combine independently generated recommendations, with the ability to resolve conflicts and generate explanations for its results. We also discuss its advantages over and relation to Multiple-criteria Decision-making (MCDM) in this particular setting.- (undefined

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

MULTI-MODALITY IMAGING IN AORTIC STENOSIS:AN EACVI CLINICAL CONSENSUS DOCUMENT

International audienceIn this EACVI clinical scientific update, we will explore the current use of multi-modality imaging in the diagnosis, risk stratification, and follow-up of patients with aortic stenosis, with a particular focus on recent developments and future directions. Echocardiography is and will likely remain the key method of diagnosis and surveillance of aortic stenosis providing detailed assessments of valve haemodynamics and the cardiac remodelling response. Computed tomography (CT) is already widely used in the planning of transcutaneous aortic valve implantation. We anticipate its increased use as an anatomical adjudicator to clarify disease severity in patients with discordant echocardiographic measurements. CT calcium scoring is currently used for this purpose; however, contrast CT techniques are emerging that allow identification of both calcific and fibrotic valve thickening. Additionally, improved assessments of myocardial decompensation with echocardiography, cardiac magnetic resonance, and CT will become more commonplace in our routine assessment of aortic stenosis. Underpinning all of this will be widespread application of artificial intelligence. In combination, we believe this new era of multi-modality imaging in aortic stenosis will improve the diagnosis, follow-up, and timing of intervention in aortic stenosis as well as potentially accelerate the development of the novel pharmacological treatments required for this disease

Efeitos do condicionamento seguido ou não de secagem em sementes de Pterogyne nitens Tul. sob estresse.

Pterogyne nitens Tul. é conhecida popularmente como amendoim do campo, é uma espécie arbórea, heliófita, secundária inicial que se regenera intensamente em áreas abertas e pastagens. Pode ser empregada como espécie ornamental e na reposição de mata ciliar, em locais sujeitos a inundações periódicas, em sítios arenosos e degradados. O condicionamento é uma técnica pós-colheita usada com o objetivo de aumentar a velocidade de germinação, emergência, bem como ampliar a tolerância a vários tipos de estresse. O objetivo deste trabalho foi avaliar a eficiência do condicionamento com ou sem secagem posterior em aumentar a resistência a diferentes tipos de estresse. As sementes selecionadas foram escarificadas com ácido sulfúrico durante 15 min. e depois condicionadas em água destilada e soluções de manitol -0,5 e -1,0 MPa durante 24h a 10oC. Para cada solução de condicionamento, o lote de sementes foi dividido em dois grupos, um dos quais foi seco até atingir o teor de umidade apresentado antes do condicionamento, e o segundo foi imediatamente usado nos testes. Os diferentes grupos de sementes foram expostos ao envelhecimento acelerado (100% U.R. sob 35 e 40oC), ao estresse térmico (24h a 60 e 70oC) e o teste de exaustão (24h submersos a 10 e 27oC). Para todos os testes foram utilizadas quatro repetições de 25 unidades e os dados de porcentagem e velocidade de germinação foram submetidos à análise de variância e ao teste de Tukey. Com o aumento da intensidade do estresse houve diminuição na germinação e no vigor das sementes, que as diferentes formas de condicionamento não reverteram. Em geral, o condicionamento com o uso manitol a -1,0 MPa diminuiu a qualidade fisiológica das sementes e, soluções a -0,5 MPa ou água destilada aumentaram a porcentagem e/ou velocidade de germinação de sementes submetidas aos diferentes tipos de estresse

Informatics for Health 2017 : advancing both science and practice

Conference report, The Informatics for Health congress, 24-26 April 2017, in Manchester, UK.Introduction : The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress. Discussion : The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes. Conclusions : Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.Publisher PDFPeer reviewe