Tuberculosis Prediction by Machine Learning Techniques

Tuberculosis is one of the top reasons of death all over the planet. Mycobacterium tuberculosis, bacteria that infects the lungs, is what causes it. For professionals working in the medical field, accurately identifying and timely predicting tuberculosis are major challenges. The course of treatment also varies from patient to patient since occasionally a patient develops drug resistance. Doctors will be given algorithmic support while using machine learning to help them diagnose, treat patients appropriately, and make quicker and better judgments. This paper discusses the many tuberculosis causes and symptoms as well as how accurate and fast prediction and diagnostic investigations have been carried out in recent years with the aid of machine learning (ML) technique

A MODEL FOR PREDICTION OF DRUG RESISTANT TUBERCULOSIS USING DATA MINING TECHNIQUE

The rate of mortality in the recent time because of tuberculosis disease is so alarming. Drug-Resistant Tuberculosis is a communicable disease very dangerous that attack lungs, many victims were not identified due to weak health systems facilities, poor doctor-patient relationship, and inefficient mechanisms for predicting of the disease. Data mining can be applied on medical data to foresee novel, useful and potential knowledge that can save a life, reduce treatment cost, increases diagnostic and prediction accuracy as well as delay taking during prediction which reduce the treatment cost of a patience. Several data mining technique such as classification, clustering, regression, and association rule were used to enhance the prediction of tuberculosis. In this project I used Naïve Bayes Classifier to design a model for predicting tuberculosis. I considered the following parameters; Gender, Chills, Fever, Night sweat, Fatigue, Cough with Blood, Weight loss, and Loss of Appetite for classification phase 1. While Gender Chest Pain, Sputum, Contact DR, Weight Loss, In-adequate treatment for classification phase 2 as the clinical symptom. The Naïve Bayes Classifier has the advantage of attribute independency, it is easy in construction, can classify categorical data, and can work on high dimensional data effectively. The model designed using Naïve Bayes Classifier is divided o into classification phase 1 and classification phase 2 and implemented using Phython 3.2 Programing Language. The result shows that Naïve Bayes Classfier was suitable in predicting drug resistant tuberculosis with performance accuracy of 82%, 98% and area under curve (AUC) is 88%. Keywords: Model Prediction, Tuberculosis. Drug, Resistant, Data Mining

Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradientboosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for thirdline drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other largescale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation

Nanomotion technology in combination with machine learning: a new approach for a rapid antibiotic susceptibility test for Mycobacterium tuberculosis.

Nanomotion technology is a growth-independent approach that can be used to detect and record the vibrations of bacteria attached to cantilevers. We have developed a nanomotion-based antibiotic susceptibility test (AST) protocol for Mycobacterium tuberculosis (MTB). The protocol was used to predict strain phenotype towards isoniazid (INH) and rifampicin (RIF) using a leave-one-out cross-validation (LOOCV) and machine learning techniques. This MTB-nanomotion protocol takes 21 h, including cell suspension preparation, optimized bacterial attachment to functionalized cantilever, and nanomotion recording before and after antibiotic exposure. We applied this protocol to MTB isolates (n = 40) and were able to discriminate between susceptible and resistant strains for INH and RIF with a maximum sensitivity of 97.4% and 100%, respectively, and a maximum specificity of 100% for both antibiotics when considering each nanomotion recording to be a distinct experiment. Grouping recordings as triplicates based on source isolate improved sensitivity and specificity to 100% for both antibiotics. Nanomotion technology can potentially reduce time-to-result significantly compared to the days and weeks currently needed for current phenotypic ASTs for MTB. It can further be extended to other anti-TB drugs to help guide more effective TB treatment

Dynamic Prediction of Treatment Outcomes for Recurrent Tuberculosis Patients

Tuberculosis (TB) is a disease that affects people around the world, especially people in underdeveloped countries. TB is one of the top ten causes of death globally so improvement in understanding diagnosis and treatment of TB affected patients could lead to major improvements in world health. This thesis research evaluated relapse patients specifically, deeming a relapse patient as one who has either been cured or completed their last treatment and then is diagnosed with TB again. This research uses dynamic predictive modeling, based upon the random forest algorithm, to predict treatment outcomes for recurrent TB patients using demographic and follow-up clinical data. The model identifies variables and time periods that are significant in predicting whether the patient will be cured. The model is applied to data provided by the Evaluation System of National Control Program of Tuberculosis in the Republic of Moldova. Our results reveal insights that could be used by physicians to improve treatment strategy and monitor patients more effectively throughout the treatment trajectory

Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques

While tuberculosis (TB) disease was discovered more than a century ago, it has not been eradicated yet. Quite contrary, at present, TB constitutes one of top 10 causes of death and has shown signs of increasing. To complement conventional diagnostic procedure of applying microbiological culture that takes several weeks and remains expensive, high resolution computer tomography (CT) of pulmonary images has been resorted to not only for aiding clinicians to expedite the process of diagnosis but also for monitoring prognosis when administrating antibiotic drugs. This research undertakes the investigation of predicting multi-drug resistant (MDR) patients from drug sensitive (DS) ones based on CT lung images to monitor the effectiveness of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images with limited regions capturing abnormities, patch-based deep convolutional neural network (CNN) allied to support vector machine (SVM) classifier is implemented on a collection of datasets from 230 patients obtained from ImageCLEF 2017 competition. As a result, the proposed architecture of CNN+SVM+patch performs the best with classification accuracy rate at 91.11% (79.80% in terms of patches). In addition, hand-crafted SIFT based approach accomplishes 88.88% in terms of subject and 83.56% with reference to patches, the highest in this study, which can be explained away by the fact that the datasets are in small numbers. Significantly, during the Tuberculosis Competition at ImageCLEF 2017, the authors took part in the task of classification of 5 types of TB disease and achieved top one with regard to averaged classification accuracy (i.e. ACC = 0.4067), which is also premised on the approach of CNN+SVM+patch. On the other hand, when the whole slices of 3D TB datasets are applied to train a CNN network, the best result is achieved through the application of CNN coupled with orderless pooling and SVM at 64.71% accuracy rate

Perspectives for systems biology in the management of tuberculosis

Standardised management of tuberculosis may soon be replaced by individualised, precision medicine-guided therapies informed with knowledge provided by the field of systems biology. Systems biology is a rapidly expanding field of computational and mathematical analysis and modelling of complex biological systems that can provide insights into mechanisms underlying tuberculosis, identify novel biomarkers, and help to optimise prevention, diagnosis and treatment of disease. These advances are critically important in the context of the evolving epidemic of drug-resistant tuberculosis. Here, we review the available evidence on the role of systems biology approaches - human and mycobacterial genomics and transcriptomics, proteomics, lipidomics/metabolomics, immunophenotyping, systems pharmacology and gut microbiomes - in the management of tuberculosis including prediction of risk for disease progression, severity of mycobacterial virulence and drug resistance, adverse events, comorbidities, response to therapy and treatment outcomes. Application of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach demonstrated that at present most of the studies provide "very low" certainty of evidence for answering clinically relevant questions. Further studies in large prospective cohorts of patients, including randomised clinical trials, are necessary to assess the applicability of the findings in tuberculosis prevention and more efficient clinical management of patients.Publisher PDFPeer reviewe

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein and adjust novelty claims accordingly; revise title; various revisions for clarit

Predicting kinase inhibitor resistance: Physics-based and data-driven approaches.

Resistance to small molecule drugs often emerges in cancer cells, viruses, and bacteria as a result of the evolutionary pressure exerted by the therapy. Protein mutations that directly impair drug binding are frequently involved in resistance, and the ability to anticipate these mutations would be beneficial in drug development and clinical practice. Here, we evaluate the ability of three distinct computational methods to predict ligand binding affinity changes upon protein mutation for the cancer target Abl kinase. These structure-based approaches rely on first-principle statistical mechanics, mixed physics- and knowledge-based potentials, and machine learning, and were able to estimate binding affinity changes and identify resistant mutations with remarkable accuracy. We expect that these complementary approaches will enable the routine prediction of resistance-causing mutations in a variety of other target proteins