scholarly journals Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission

2020 ◽  
Author(s):  
Shayantan Banerjee ◽  
Akram Mohammed ◽  
Hector R. Wong ◽  
Nades Palaniyar ◽  
Rishikesan Kamaleswaran
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Critically Ill ◽  
Clinical Data ◽  
Peripheral Blood ◽  
Care Management ◽  
Area Under The Curve ◽  
Critically Ill Children ◽  
Icu Admission ◽  
Artificial Intelligence Systems

AbstractA complicated clinical course for critically ill patients admitted to the ICU usually includes multiorgan dysfunction and subsequent death. Owning to the heterogeneity, complexity, and unpredictability of the disease progression, patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 hours of PICU admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes that were predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with ten iterations, the overall mean area under the curve reached 0.82. Using the same set of genes, we further achieved an overall area under the curve of 0.72 when tested on an external validation set. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1 and NLRP1 that help to predict sepsis severity or mortality. The discovery of eight novel genetic biomarkers related to the overactive innate immune system and neutrophils functions, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival.Research in ContextEvidence before this studyTranscriptomic biomarkers have long been explored as potential means of earlier disease endotyping. Much of the existing literature has however focused on mortality and discrete outcomes. Additionally, much of prior work in this area has been developed on statistical methods, while recent means of selecting features have not been sufficiently explored.Added value of this studyIn this study, we developed a robust machine learning based model for identifying novel biomarkers of complicated disease courses. We found 20 highly stable genes that predict disease complexity with an average derivation AUROC of 0.82 and validation AUROC of 0.72 within critically ill children, using peripheral blood collected within 24 hrs of ICU admission.Implications of all the available evidenceEarlier identification of disease complexity can inform care management and targeted therapy. Therefore, the 20 gene candidates identified by our rigorous approach, can be used to identify, early in their ICU stay, patients who may ultimately develop significant organ dysfunction and complex care management.

scholarly journals Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission

2021 ◽  
Vol 12 ◽  
Author(s):  
Shayantan Banerjee ◽  
Akram Mohammed ◽  
Hector R. Wong ◽  
Nades Palaniyar ◽  
Rishikesan Kamaleswaran
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Data ◽  
Peripheral Blood ◽  
Complex Disease ◽  
External Validation ◽  
Area Under The Curve ◽  
Pediatric Icu ◽  
Artificial Intelligence Systems

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to perform a meta-analysis of previously published gene expression datasets to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 h of pediatric ICU (PICU) admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with 10 iterations, the overall mean area under the curve reached 0.82. Using a subset of the same set of genes, we further achieved an overall area under the curve of 0.72, 0.96, 0.83, and 0.82, respectively, on four independent external validation sets. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers (SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1, and NLRP1) that help predict sepsis severity or mortality. While these genes have been previously associated with sepsis mortality, in this work, we show that these genes are also implicated in complex disease courses, even among survivors. The discovery of eight novel genetic biomarkers related to the overactive innate immune system, including neutrophil function, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Modelling of Traffic Flows and Supply Chains Based on Geospatial Knowledge

2021 ◽  
Vol 2068 (1) ◽  
pp. 012042
Author(s):  
A Kolesnikov ◽  
P Kikin ◽  
E Panidi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chains ◽  
Inventory Management ◽  
Traffic Flows ◽  
Machine Learning Methods ◽  
Customer Interaction ◽  
Long Term Impact ◽  
Artificial Intelligence Systems

Abstract The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.

scholarly journals Machine Learning in Oncology: What Should Clinicians Know?

2020 ◽  
pp. 799-810
Author(s):  
Matthew Nagy ◽  
Nathan Radakovich ◽  
Aziz Nazha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Data ◽  
Cancer Diagnosis ◽  
Health Data ◽  
Computer Processing ◽  
Research And Practice ◽  
New Methods ◽  
Oncology Research ◽  
Processing Power

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

scholarly journals Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning (Preprint)

2020 ◽  
Author(s):  
Rebecca O'Donovan ◽  
Emre Sezgin ◽  
Sven Bambach ◽  
Eric Butter ◽  
Simon Lin
Keyword(s):  
Machine Learning ◽  
Behavioral Disorders ◽  
Clinical Data ◽  
Area Under The Curve ◽  
Tree Model ◽  
Data Set ◽  
Home Setting ◽  
Detection Model ◽  
Audio Data ◽  
Model Training

BACKGROUND Qualitative self- or parent-reports used in assessing children’s behavioral disorders are often inconvenient to collect and can be misleading due to missing information, rater biases, and limited validity. A data-driven approach to quantify behavioral disorders could alleviate these concerns. This study proposes a machine learning approach to identify screams in voice recordings that avoids the need to gather large amounts of clinical data for model training. OBJECTIVE The goal of this study is to evaluate if a machine learning model trained only on publicly available audio data sets could be used to detect screaming sounds in audio streams captured in an at-home setting. METHODS Two sets of audio samples were prepared to evaluate the model: a subset of the publicly available AudioSet data set and a set of audio data extracted from the TV show Supernanny, which was chosen for its similarity to clinical data. Scream events were manually annotated for the Supernanny data, and existing annotations were refined for the AudioSet data. Audio feature extraction was performed with a convolutional neural network pretrained on AudioSet. A gradient-boosted tree model was trained and cross-validated for scream classification on the AudioSet data and then validated independently on the Supernanny audio. RESULTS On the held-out AudioSet clips, the model achieved a receiver operating characteristic (ROC)–area under the curve (AUC) of 0.86. The same model applied to three full episodes of Supernanny audio achieved an ROC-AUC of 0.95 and an average precision (positive predictive value) of 42% despite screams only making up 1.3% (n=92/7166 seconds) of the total run time. CONCLUSIONS These results suggest that a scream-detection model trained with publicly available data could be valuable for monitoring clinical recordings and identifying tantrums as opposed to depending on collecting costly privacy-protected clinical data for model training.

scholarly journals Experiencing ProvLake to Manage the Data Lineage of AI Workflows

2020 ◽  
Author(s):  
Leonardo Guerreiro Azevedo ◽  
Renan Souza ◽  
Raphael Melo Thiago ◽  
Elton Soares ◽  
Marcio Moreno
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Management ◽  
Oil And Gas ◽  
Core Concept ◽  
Data Lineage ◽  
Oil And Gas Exploration ◽  
Provenance Data ◽  
Management Techniques ◽  
Artificial Intelligence Systems

Machine Learning (ML) is a core concept behind Artificial Intelligence systems, which work driven by data and generate ML models. These models are used for decision making, and it is crucial to trust their outputs by, e.g., understanding the process that derives them. One way to explain the derivation of ML models is by tracking the whole ML lifecycle, generating its data lineage, which may be accomplished by provenance data management techniques. In this work, we present the use of ProvLake tool for ML provenance data management in the ML lifecycle for Well Top Picking, an essential process in Oil and Gas exploration. We show how ProvLake supported the validation of ML models, the understanding of whether the ML models generalize respecting the domain characteristics, and their derivation.

scholarly journals Entropy of Artificial Intelligence

Universe ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 53
Author(s):  
T. S. Biró ◽  
Antal Jakovác
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Process ◽  
Probability Space ◽  
Natural Processes ◽  
Artificial Intelligence Systems

We describe a model of artificial intelligence systems based on the dimension of the probability space of the input set available for recognition. In this scenario, we can understand a subset, which means that we can decide whether an object is an element of a given subset or not in an efficient way. In the machine learning (ML) process we define appropriate features, in this way shrinking the defining bit-length of classified sets during the learning process. This can also be described in the language of entropy: while natural processes tend to increase the disorder, that is, increase the entropy, learning creates order, and we expect that it decreases a properly defined entropy.

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