A machine learning approach to triaging patients with chronic obstructive pulmonary disease

Swaminathan, Sumanth; Qirko, Klajdi; Smith, Theodore; Corcoran, Ethan; Wysham, Nicholas G.; Bazaz, Gaurav; Kappel, George; Gerber, Anthony N.

doi:10.1371/journal.pone.0188532

Cited by 88 publications

(43 citation statements)

References 36 publications

(24 reference statements)

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“…• Sensitivity (also known as detection rate) quantifies the avoidance of false negatives and demonstrates the capability of a classifier to predict the universe of relevant instances [46]. In a multi-class classification, the sensitivity of the model is calculated as the average among the classes, as follows.…”

Section: Plos Onementioning

confidence: 99%

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Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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In medicine, a misdiagnosis or the absence of specialists can affect the patient's health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods: color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms: support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model. To conduct the research, our database included 160 images as testing set and 720 images as training and validation sets of 180 patients. We repeatedly trained the learning models using the training dataset and evaluated them using the validation dataset to thus obtain the best feature extraction method and learning model that produce the highest validation accuracy. The results showed that the SVM and k-NN presented the best performance followed by decision trees model. Finally, we performed a classification stage-i.e., diagnosis-using testing data, where the SVM model achieved an average classification accuracy of 93.9%, average sensitivity of 87.8%, average specificity of 95.9%, and average positive predictive value of 87.7%. The results show that this system might be used for general practitioners as a reference to make better decisions in the ear pathologies diagnosis.

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Section: Plos Onementioning

confidence: 99%

“…• Positive predictive value-PPV (often called precision) is the ratio between the correctly classified instances from a given class and the total number of instances classified as belonging to that class [46]. The PPV in a multi-class classification approach is averaged among all the classes, as shown below.…”

Section: Plos Onementioning

confidence: 99%

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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“…ML has previously been successfully applied to the task of triage in other fields. For example, ML has been shown to be accurate in the triage of COPD exacerbations based upon pre‐defined categorical and continuous variables, as opposed to the clinical text used in this project. Our pilot study is distinct as we demonstrated with a relatively small sample size that NLP can accurately identify urgent referrals from the full unfiltered spectrum of clinical ophthalmology referrals instead of just a specific disease process.…”

Section: Discussionmentioning

confidence: 99%

Triaging ophthalmology outpatient referrals with machine learning: A pilot study

Tan

Bacchi

Casson

et al. 2019

Clinical Exper Ophthalmology

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Importance Triaging of outpatient referrals to ophthalmology services is required for the maintenance of patient care and appropriate resource allocation. Machine learning (ML), in particular natural language processing, may be able to assist with the triaging process. Background To determine whether ML can accurately predict triage category based on ophthalmology outpatient referrals. Design Retrospective cohort study. Participants The data of 208 participants was included in the project. Methods The synopses of consecutive ophthalmology outpatient referrals at a tertiary hospital were extracted along with their triage categorizations. Following pre‐processing, ML models were applied to determine how accurately they could predict the likely triage categorization allocated. Data was split into training and testing sets (75%/25% split). ML models were tested on an unseen test set, after development on the training dataset. Main Outcome Measure Area under the receiver operator curve (AUC) for category one vs non‐category one classification. Results For the main outcome measure, convolutional neural network (CNN) provided the best AUC (0.83) and accuracy on the test set (0.81), with the artificial neural network (AUC 0.81 and accuracy 0.77) being the next best performing model. When the CNN was applied to the classification task of identifying which referrals should be allocated a category one vs category two vs category three priority, a lower accuracy was achieved (0.65). Conclusions and Relevance ML may be able to accurately assist with the triaging of ophthalmology referrals. Future studies with data from multiple centres and larger sample sizes may be beneficial.

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“…Studies proposed a data-driven methodology that can help to produce COPD predictive models and asthma exacerbations. It would be useful to support both patients and physicians [39].…”

Section: Quality Evaluationmentioning

confidence: 99%

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni

Sagaro

Chinatalapudi

et al. 2020

JPM

221

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This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

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A machine learning approach to triaging patients with chronic obstructive pulmonary disease

Cited by 88 publications

References 36 publications

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

Triaging ophthalmology outpatient referrals with machine learning: A pilot study

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

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