scholarly journals Machine learning for emerging infectious disease field responses

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Han-Yi Robert Chiu ◽  
Chun-Kai Hwang ◽  
Shey-Ying Chen ◽  
Fuh-Yuan Shih ◽  
Hsieh-Cheng Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Decision Rules ◽  
Emerging Infectious Diseases ◽  
Vaccination Strategy ◽  
Learning Technologies ◽  
Severe Illness ◽  
Mass Casualties ◽  
Global Public Health

AbstractEmerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.

scholarly journals Supervised Machine Learning for Risk Stratification of Influenza-Like Illness: A Model to Prioritize Emerging Infectious Disease Disaster Responses

2021 ◽  
Author(s):  
Han-Yi Robert Chiu ◽  
Chun-Kai Hwang ◽  
Shey-Ying Chen ◽  
Fuh-Yuan Shih ◽  
Hsieh-Cheng Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Admissions ◽  
Emerging Infectious Diseases ◽  
Learning Technologies ◽  
Supervised Machine Learning ◽  
Severe Illness ◽  
Mass Casualties ◽  
Global Public Health ◽  
Neural Network Models ◽  
Influenza Like Illness

Abstract Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the best allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study aimed to exploit machine learning technologies to cope with this challenge. The study was based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the conventional decision tree (DT) models built with only 6 features, including age, gender, and four comorbidities, delivered the same level of prediction accuracy as the state-of-the-art deep neural network models built with 18 features. Accordingly, we further studied how to exploit the DT models with different sensitivity levels to determine patient triage and optimize medical resource allocation in different stages of an EID disaster to aid the frontline clinicians and policy-makers. In conclusion, our study demonstrated an approach to exploit machine learning technologies to cope with the challenges during the outbreak of an EID.

Performance Evaluation of Machine Learning Algorithms for Dengue Disease Prediction

2019 ◽  
Vol 16 (12) ◽  
pp. 5105-5110
Author(s):  
S. Kannimuthu ◽  
K. S. Bhuvaneshwari ◽  
D. Bhanu ◽  
A. Vaishnavi ◽  
S. Ahalya
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Severe Illness ◽  
Disease Prediction ◽  
Dengue Disease ◽  
Aedes Mosquitoes ◽  
Disease Prognosis ◽  
C4.5 Algorithm

Dengue is a dangerous disease caused by female mosquitoes. Dengue fever (also called as breakbone fever) is a infection that can cause to a severe illness which is happened by four different viruses and spread by Aedes mosquitoes. It is the necessary to devise effective methodology for dengue disease prognosis. Machine learning is a sub-filed of artificial intelligence (AI) which offers systems the ability to learn and improve from experience without human intervention and being explicitly programmed. In this research work, the performance analysis of various prediction models is done for dengue disease prediction. It is observed that C4.5 algorithm outperforms well in terms of performance measures such as accuracy (89.33%), prediction (88.9%), recall (89.77%) and other measures.

scholarly journals Machine learning: how much does it improve the prediction of unplanned hospital admissions?

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Colin Weaver ◽  
Kerry McBrien ◽  
Tolu Sajobi ◽  
Paul E Ronksley ◽  
Brendan Lethebe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Acute Care ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Unplanned Admission ◽  
Learning Methods ◽  
Regression Methods ◽  
Machine Learning Methods ◽  
Unplanned Hospital Admissions

IntroductionRisk prediction models can be used to inform decision-making in clinical settings. With large and detailed electronic medical record data, machine learning may improve predictions. The objective of this work is to determine the feasibility and accuracy of machine learning versus logistic regression to predict unplanned hospital admissions. Objectives and ApproachData from primary care electronic medical records for community-dwelling adults in Alberta, Canada available from the Canadian Primary Care Sentinel Surveillance Network will be linked to acute care administrative health data held by Alberta Health Services. Two regression methods (forward stepwise logistic, LASSO logistic) will be compared with three machine learning methods (classification tree, random forest, gradient boosted trees). Prior primary and acute care use will be used to predict three outcomes: ≥1 unplanned admission within 1 year, ≥1 unplanned admission within 90 days, and ≥1 unplanned admission within 1 year due to an ambulatory care sensitive condition. ResultsThe results of this work in progress will be presented at the conference. 41,142 patients will have their primary and acute care data linked. We anticipate that the machine learning methods will improve predictive performance but will be more challenging for clinicians and patients to understand, including why a given patient is predicted to be at higher risk. The primary comparison of machine learning and regression methods will be based on positive predictive values corresponding to the top 5% predicted risk threshold, and estimated via 10-fold cross-validation. Conclusion/ImplicationsThis project aims to help researchers decide which statistical methods to use for risk prediction models. When considering machine learning methods the best approach may be to try multiple methods, compare their predictive accuracy and interpretability, and then choose a final method.

scholarly journals 118. Machine Learning Approaches to Predicting Treatment Outcomes for Carbapenem-Resistant Enterobacterales in a Region with High Prevalence of Non-Carbapenemase Producers

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S71-S71
Author(s):  
Cody A Black ◽  
Samantha Aguilar ◽  
Sarah Bandy ◽  
Gerard Gawrys ◽  
Steven Dallas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Outcomes ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Decision Rules ◽  
South Texas ◽  
Control Measures ◽  
Learning Approaches ◽  
Carbapenem Resistant ◽  
High Prevalence

Abstract Background Carbapenem-resistant Enterobacterales are a growing threat globally. Early detection of CRE is necessary for appropriate treatment and infection control measures. Many hospital labs can test for carbapenemase production; however, in some regions, including South Texas, the majority of CRE are non-carbapenemase producing (NCPE). This study had two interrelated aims to develop decision rules tailored to a region with high prevalence of NCPE to predict 1) antimicrobial resistance (AMR) from whole genome sequencing (WGS) data and 2) CRE treatment outcomes. Methods To better understand links between resistome, phenotypic AMR, and prediction of outcomes for CRE, we developed decision rules to build machine learning prediction models. We conducted WGS and antibiotic susceptibility testing (21 antibiotics) on CRE isolates from unique patients across 5 hospitals in the South Texas region between 2013 and 2020. Day 30 outcomes were based on desirability of outcome ranking (DOOR). The overall classification accuracies of the models are reported. Results Overall 146 CRE isolates were included, 97 were used to train each model, and 49 were used for validation. Among the K. pneumoniae and E. coli CRE isolates that were available with susceptibility data, the majority (62%) were NCPE. For the clinical recovery model (DOOR), the combination of admission ICU status, piperacillin-tazobactam (PT) MIC > 16, presence of sul gene, and polymyxin-sparring regimens associated with an overall accuracy of 95% for having a worse DOOR. Majority (60%) of patients were empirically treated with piperacillin-tazobactam; notably, less than 33% isolates had PT MIC ≤ 16. Interestingly, combined effects of isolates that did not harbor carbapenemases, blaOXA-1, blaCTX-M-15, blaCMY, or aac(6’)ib-cr genes resulted in a decision rule with a 95.7% overall accuracy for susceptibility to PT (MIC < 16 ug/mL). Conclusion Herein, we used machine learning approaches to predict AMR and treatment-based outcomes linked with WGS data in a region with predominantly NCPE infections. Machine learning can obtain a model that can make repeatable predictions, further data can improve the accuracy and guide tailored clinical decision-making. Disclosures Grace Lee, PharmD, PhD, BCPS, Merck Co. (Grant/Research Support)NIA/NIH (Research Grant or Support)

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

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