scholarly journals Machine learning identified distinct serum lipidomic signatures in hospitalized COVID-19-positive and COVID-19-negative patients

2021 ◽  
Author(s):  
Helena Castane ◽  
Simona Iftimie ◽  
Gerard Baiges-Gaya ◽  
Elisabet Rodriguez-Tomas ◽  
Andrea Jimenez-Franco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Healthy Volunteers ◽  
Healthy Subjects ◽  
Clinical Decision Making ◽  
Inflammatory Diseases ◽  
Immunological Response ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Healthy Population ◽  
Lipid Species

Background: Lipids are involved in the interaction between viral infection and the host metabolic and immunological response. Several studies comparing the lipidome of COVID-19-positive hospitalized patients vs. healthy subjects have already been reported. It is largely unknown, however, whether these differences are specific to this disease. The present study compared the lipidomic signature of hospitalized COVID-19-positive patients with that of healthy subjects, and with COVID-19-negative patients hospitalized for other infectious/inflammatory diseases. Potential COVID-19 biomarkers were identified. Methods: We analyzed the lipidomic signature of 126 COVID-19-positive patients, 45 COVID-19-negative patients hospitalized with other infectious/inflammatory diseases and 50 healthy volunteers. Results were interpreted by machine learning. Results: We identified acylcarnitines, lysophosphatidylethanolamines, arachidonic acid and oxylipins as the most altered species in COVID-19-positive patients compared to healthy volunteers. However, we found similar alterations in COVID-19-negative patients. By contrast, we identified lysophosphatidylcholine 22:6-sn2, phosphatidylcholine 36:1 and secondary bile acids as the parameters that had the greatest capacity to discriminate between COVID-19-positive and COVID-19-negative patients. Conclusion: This study shows that COVID-19 infection shares many lipid alterations with other infectious/inflammatory diseases, but differentiating them from the healthy population. Also, we identified some lipid species the alterations of which distinguish COVID-19-positive from Covid-19-negative patients. Our results highlight the value of integrating lipidomics with machine learning algorithms to explore the pathophysiology of COVID-19 and, consequently, improve clinical decision making.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

scholarly journals Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Imogen Schofield ◽  
David C. Brodbelt ◽  
Noel Kennedy ◽  
Stijn J. M. Niessen ◽  
David B. Church ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cushing’S Syndrome ◽  
Clinical Decision Making ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Cushing's Syndrome ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clinical Records

AbstractCushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

Predictive Data Modeling Using sp-kNN for Risk Factor Evaluation in Urban Demographical Healthcare Data

2021 ◽  
Vol 11 (1) ◽  
pp. 7-14
Author(s):  
Uzair Aslam Bhatti ◽  
Linwang Yuan ◽  
Zhaoyuan Yu ◽  
Saqib Ali Nawaz ◽  
Anum Mehmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Clinical Decision ◽  
Eye Diseases ◽  
Machine Learning Algorithms ◽  
Medical Resources ◽  
Individual Level ◽  
Healthcare Data ◽  
Increasing Demand

Healthcare diseases are spreading all around the globe day to day. Hospital datasets are full from the data with much information. It's an urgent requirement to use that data perfectly and efficiently. We propose a novel algorithm for predictive model for eye diseases using KNN with machine learning algorithms and artificial intelligence (AI). The aims are to evaluate the connection between the accumulated preoperative risk variables and different eye diseases and to manufacture a model that can anticipate the results on an individual level, thus giving relevance to impactful factors and geographic and demographic features. Risk factors of the desired diseases were calculated and machine learning algorithm applied to provide the prediction of the diseases. Health monitoring is an economic discipline that focuses on the effective allocation of medical resources, mainly to maximize the benefits of society to health through the available resources. With the increasing demand for medical services and the limited allocation of medical resources, the application of health economics in clinical practice has been paid more and more attention, and it has gradually played an important role in clinical decision-making.

scholarly journals Comparing Machine Learning Algorithms for Predicting ICU Admission and Mortality in COVID-19

2020 ◽  
Author(s):  
Sonu Subudhi ◽  
Ashish Verma ◽  
Ankit B. Patel ◽  
C. Corey Hardin ◽  
Melin J. Khandekar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithms ◽  
Disease Outbreaks ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Healthcare Database ◽  
Icu Admission ◽  
Infectious Disease Outbreaks ◽  
Better Than

AbstractAs predicting the trajectory of COVID-19 disease is challenging, machine learning models could assist physicians determine high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) healthcare database, we developed and internally validated models using patients presenting to Emergency Department (ED) between March-April 2020 (n = 1144) and externally validated them using those individuals who encountered ED between May-August 2020 (n = 334). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and procalcitonin levels were important for ICU admission models whereas eGFR <60 ml/min/1.73m2, ventilator use, and potassium levels were the most important variables for predicting mortality. Implementing such models would help in clinical decision-making for future COVID-19 and other infectious disease outbreaks.

scholarly journals Predicting infection and sepsis; what predictors have been used to train machine learning algorithms? A systematic review

2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i18-i18
Author(s):  
N Hassan ◽  
R Slight ◽  
D Weiand ◽  
A Vellinga ◽  
G Morgan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Decision Making ◽  
Quality Assessment ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
Learning Algorithms ◽  
Clinical Decision ◽  
Machine Learning Algorithms

Abstract Introduction Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients who may be at risk of developing infection and subsequent sepsis and assist clinicians with their care management. Aim To identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis and inform clinical decision making. Methods This systematic review was registered in PROSPERO database (CRD42020158685). We searched 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase, using appropriate search terms. We included quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adult population (&gt;18 years) in all care settings, which included data on predictors to develop machine learning algorithms. The timeframe of the search was 1st January 2000 till the 25th November 2019. Data extraction was performed using a data extraction sheet, and a narrative synthesis of eligible studies was undertaken. Narrative analysis was used to arrange the data into key areas, and compare and contrast between the content of included studies. Quality assessment was performed using Newcastle-Ottawa Quality Assessment scale, which was used to evaluate the quality of non-randomized studies. Bias was not assessed due to the non-randomised nature of the included studies. Results Fifteen articles met our inclusion criteria (Figure 1). We identified 194 predictors that were used to train machine learning algorithms to predict infection and subsequent sepsis, with 13 predictors used on average across all included studies. The most significant predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR&lt;60ml/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit &lt; 30%. These predictors were used for the development of all the algorithms in the fifteen articles. All included studies used artificial intelligence techniques to predict the likelihood of sepsis, with average sensitivity 77.5±19.27, and average specificity 69.45±21.25. Conclusion The type of predictors used were found to influence the predictive power and predictive timeframe of the developed machine learning algorithm. Two strengths of our review were that we included studies published since the first definition of sepsis was published in 2001, and identified factors that can improve the predictive ability of algorithms. However, we note that the included studies had some limitations, with three studies not validating the models that they developed, and many tools limited by either their reduced specificity or sensitivity or both. This work has important implications for practice, as predicting the likelihood of sepsis can help inform the management of patients and concentrate finite resources to those patients who are most at risk. Producing a set of predictors can also guide future studies in developing more sensitive and specific algorithms with increased predictive time window to allow for preventive clinical measures.

scholarly journals Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 76
Author(s):  
Fernando Ribeiro ◽  
Filipe Fidalgo ◽  
Arlindo Silva ◽  
José Metrôlho ◽  
Osvaldo Santos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pressure Ulcer ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Pressure Ulcers ◽  
Learning Algorithms ◽  
Clinical Decision ◽  
Predisposing Factors ◽  
Machine Learning Algorithms ◽  
Challenges And Opportunities

Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities.

scholarly journals Perinatal Hypoxia Diagnostic System by using Scalable Machine Learning Algorithms

2019 ◽  
Vol 8 (12) ◽  
pp. 1954-1959
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Learning Approaches ◽  
Perinatal Hypoxia ◽  
Fast Computation

Collaborating big data and machine learning approaches in healthcare can help in improving clinical decision making and treatment by identifying and accumulating accurate features. Prenatal hypoxia can also be identified by cardiotocography (CTG) monitoring that helps in identifying the condition of the fetus. Imposing the data over distributed approaches can help in fast computation to rate the fetal and mother wellbeing before delivery. Our research aims to propose and implement a scalable Machine learning Algorithm based perinatal Hypoxia diagnostic system for larger datasets. This system was implemented on the CTG dataset using python and pyspark models like SVM, Random Forest, and Logistic regression. In the proposed method experiment results contributing to spark RF are more accurate than other techniques and achieved the precision of 0.97, recall of 0.99, f-1 score of 0. 98, AUC of 0.97 and gained 97% accuracy

Demystifying Disease Identification and Diagnosis Using Machine Learning Classification Algorithms

2020 ◽  
pp. 200-249
Author(s):  
Ashok Suragala ◽  
PapaRao A. V.
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Explanatory Variables ◽  
Machine Learning Classification ◽  
Healthcare Data ◽  
Disease Identification ◽  
Mlp Classifier

The exponential surge in healthcare data is providing new opportunities to discover meaningful data-driven characteristics and patterns of diseases. Machine learning and deep learning models have been employed for many computational phenotyping and healthcare prediction tasks. However, machine learning models are crucial for wide adaption in medical research and clinical decision making. In this chapter, the authors introduce demystifying diseases identification and diagnosis of various disease using machine learning algorithms like logistic regression, naive Bayes, decision tree, MLP classifier, random forest in order to cure liver disease, hepatitis disease, and diabetes mellitus. This work leverages the initial discoveries made through data exploration, statistical analysis of data to reduce the number of explanatory variables, which led to identifying the statistically significant attributes, mapping those significant attributes to the response, and building classification techniques to predict the outcome and demystify clinical diseases.

scholarly journals Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sonu Subudhi ◽  
Ashish Verma ◽  
Ankit B. Patel ◽  
C. Corey Hardin ◽  
Melin J. Khandekar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithms ◽  
Disease Outbreaks ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Healthcare Database ◽  
Icu Admission ◽  
Infectious Disease Outbreaks ◽  
Better Than

AbstractAs predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (n = 3597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (n = 1711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O2 saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m2, and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.

scholarly journals Rummage of Machine Learning Algorithms in Cancer Diagnosis

2021 ◽  
Vol 12 (1) ◽  
pp. 1-15
Author(s):  
Prashant Johri ◽  
Vivek sen Saxena ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithms ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Healthcare Sector ◽  
Healthcare Data ◽  
Digital Imaging Technology ◽  
Health Studies

With the continuous improvement of digital imaging technology and rapid increase in the use of digital medical records in last decade, artificial intelligence has provided various techniques to analyze these data. Machine learning, a subset of artificial intelligence techniques, provides the ability to learn from past and present and to predict the future on the basis of data. Various AI-enabled support systems are designed by using machine learning algorithms in order to optimize and computerize the process of clinical decision making and to bring about a massive archetype change in the healthcare sector such as timely identification, revealing and treatment of disease, as well as outcome prediction. Machine learning algorithms are implemented in the healthcare sector and helped in diagnosis of critical illness such as cancer, neurology, cardiac, and kidney disease as well as with easing in anticipation of disease progression. By applying and executing machine learning algorithms over healthcare data, one can evaluate, analyze, and generate the results that can be used not only to advance the prior health studies but also to aid in forecasting a patient's chances of developing of various diseases. The aim in this article is to present an overview of machine learning and to cover various algorithms of machine learning and their present implementation in the healthcare sector.

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