Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models

Hussain, Owais A.; Junejo, Khurum Nazir

doi:10.1080/17538157.2018.1433676

Cited by 23 publications

(25 citation statements)

References 20 publications

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“…This approach has also been used at health facilities to predict disease severity in patients with dengue fever 20 and malaria, 39 and children with acute infections. 40 Researchers have used this approach to quantify the risk of tuberculosis treatment failure 41 and assess the risk of cognitive sequelae after malaria infection in children. 42 See Online for appendix…”

Section: Ai-driven Interventions For Healthmentioning

confidence: 99%

Artificial intelligence and the future of global health

Schwalbe¹,

2020

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Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.

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Section: Ai-driven Interventions For Healthmentioning

confidence: 99%

Artificial intelligence and the future of global health

Schwalbe¹,

2020

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“…It is important to note that Peetluk et al [23] do not classify LR as machine learning in their review as the LR analysis was used as a statistical methodology to understand the relationship between attributes and their prevalence. In the few machine learning studies identified, it was used primarily for predicting treatment completion [39] or unfavourable outcomes [40,41].…”

Section: Related Workmentioning

confidence: 99%

“…Hussain and Junejo [39] propose and evaluate three machine learning models-SVM, RF and Neural Network (NN). Their data set comprised 4213 records from an unidentified location; 64.37% of the records represented completed treatments.…”

Section: Related Workmentioning

confidence: 99%

“…SVM is a set of supervised learning methods that analyze data and recognize patterns. It is commonly used for the classification and regression analyzes [63], and has been used in TB prognosis research [39,41,42] SVM is based on the structural risk minimization criterion and its goal is to find the optimal separating hyperplane where the separating margin should be maximized. This approach improves the generalization ability of the learning machine and is effective in solving problems such as non-linear, high dimension data separation and classification problems that lack prior knowledge [64].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…The forest chooses a class with the maximum N votes [1]. It has been widely used in TB detection and in three of the identified studies on TB prognosis [39,41,42].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

See 2 more Smart Citations

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

et al. 2021

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Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class.

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Clinical application of intelligent technologies and integration in medical laboratories

Huang

Ding

et al. 2023

iLABMED

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With the development of scientific technology, the transition to the intelligent era of digitalization and automation is an irresistible trend for medical laboratories. Medical diagnosis systems have undergone significant changes as a result of intelligent technologies, such as machine learning, artificial intelligence, and the Internet of Things, from the collection, transmission, and detection of test samples to the review of reports and the provision of clinical feedback. In addition to significantly enhancing the efficiency, consistency, and accuracy of medical laboratory testing, these technologies also assist the improvement of individualized healthcare and medical expert systems, as well as the early detection and treatment of diseases. The future development of medical laboratories will focus on integrating big data and diverse intelligent resources, cooperating more closely with clinical departments, and realizing the effective pathway of patient‐centered care. The purpose of this review is to illustrate the current state of intelligent technology integration in medical laboratories and provide a preliminary discussion about the potential future influences of intelligent technology development on the evolution of medical laboratories.

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Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models

Cited by 23 publications

References 20 publications

Artificial intelligence and the future of global health

Artificial intelligence and the future of global health

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

Clinical application of intelligent technologies and integration in medical laboratories

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