Improving Lyme disease testing with data driven test design in pediatrics.

Diagnostic advances have not kept pace with the expansion of Lyme disease caused by Borrelia burgdorferi and transmitted by ticks. Lyme disease clinical manifestations can overlap with many other diagnoses making Lyme disease a critical part of many differential diagnoses in endemic areas. Current diagnostic blood tests rely on a 2-tiered algorithm for which the second step is either a time-consuming western blot or a whole cell lysate immunoassay. Neither of these second step tests allow for rapid results of this critical rule out test. We hypothesized that using western blot confirmation information, we could create computational models to propose recombinant second-tier tests that would allow for more rapid, automated, and specific testing algorithms. We propose here a framework for assessing retrospective data to determine putative recombinant assay components. A retrospective pediatric cohort of 2755 samples submitted for Lyme disease screening was assessed using support vector machine learning algorithms to optimize tier 1 diagnostic thresholds for the Vidas IgG II assay and determine optimal tier 2 components for both a positive and negative confirmation test. In cases where the tier 1 screen was negative, but clinical suspicion was high, we found that 1 protein (L58) could be used to reduce false-negative results. For second-tier testing of screen positive cases, we found that 6 proteins could be used to reduce false-positive results (L18, L39M, L39, L41, L45, and L58) with a final machine learning classifier or 2 proteins using a final rules-based approach (L41, L18). This led to an overall accuracy of 92.36% for the proposed algorithm without a final machine learning classifier and 92.12% with integration of the machine learning classifier in the final algorithm when compared to the IgG western blot as the gold-standard. Use of this framework across multiple assays and institutions will allow for a data-driven approach to assay development to provide laboratories and patients with the improvements in turnaround time needed for this testing.

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.

Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output. For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%. These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease.

Machine Learning Classification of False-Positive Human Immunodeficiency Virus Screening Results.

BACKGROUND: Human immunodeficiency virus (HIV) screening has improved significantly in the past decade as we have implemented tests that include antigen detection of p24. Incorporation of p24 detection narrows the window from 4 to 2 weeks between infection acquisition and ability to detect infection, reducing unintentional spread of HIV. The fourth- and fifth-generation HIV (HIV5G) screening tests in low prevalence populations have high numbers of false-positive screens and it is unclear if orthogonal testing improves diagnostic and public health outcomes. METHODS: We used a cohort of 60,587 HIV5G screening tests with molecular and clinical correlates collected from 2016 to 2018 and applied machine learning to generate a classifier that could predict likely true and false positivity. RESULTS: The best classification was achieved by using support vector machines and transformation of results with principle component analysis. The final classifier had an accuracy of 94% for correct classification of false-positive screens and an accuracy of 92% for classification of true-positive screens. CONCLUSIONS: Implementation of this classifier as a screening method for all HIV5G reactive screens allows for improved workflow with likely true positives reported immediately to reduce infection spread and initiate follow-up testing and treatment and likely false positives undergoing orthogonal testing utilizing the same specimen already drawn to reduce distress and follow-up visits. Application of machine learning to the clinical laboratory allows for workflow improvement and decision support to provide improved patient care and public health.