Evaluation of HCV Infection Laboratory Test Results Using Machine Learning Methods

ABSTRACTDespite the high morbidity and mortality associated with Acute Respiratory Distress Syndrome (ARDS), discrimination of ARDS from other causes of acute respiratory failure remains challenging, particularly in the first 24 hours of mechanical ventilation. Delay in ARDS identification prevents lung protective strategies from being initiated and delays clinical trial enrolment and quality improvement interventions. Medical records from 1,263 ICU-admitted, mechanically ventilated patients at Northwell Health were retrospectively examined by a clinical team who assigned each patient a diagnosis of “ARDS” or “non-ARDS” (e.g., pulmonary edema). We then applied an iterative pre-processing and machine learning framework to construct a model that would discriminate ARDS versus non-ARDS, and examined features informative in the patient classification process. Data made available to the model included patient demographics, laboratory test results from before the initiation of mechanical ventilation, and features extracted by natural language processing of radiology reports. The resulting model discriminated well between ARDS and non-ARDS causes of respiratory failure (AUC=0.85, 89% precision at 20% recall), and highlighted features unique among ARDS patients, and among and the subset of ARDS patients who would not recover. Importantly, models built using both clinical notes and laboratory test results out-performed models built using either data source alone, akin to the retrospective clinician-based diagnostic process. This work demonstrates the feasibility of using readily available EHR data to discriminate ARDS patients prospectively in a real-world setting at a critical time in their care and highlights novel patient characteristics indicative of ARDS.

Download Full-text

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

PLoS ONE ◽

10.1371/journal.pone.0227188 ◽

2020 ◽

Vol 15 (1) ◽

pp. e0227188

Author(s):

Antonio Rivero-Juárez ◽

David Guijo-Rubio ◽

Francisco Tellez ◽

Rosario Palacios ◽

Dolores Merino ◽

...

Keyword(s):

Machine Learning ◽

Hcv Infection ◽

Learning Methods ◽

Machine Learning Methods

Download Full-text

Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile

Health Data Science ◽

10.34133/2021/7574903 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

He S. Yang ◽

Yu Hou ◽

Hao Zhang ◽

Amy Chadburn ◽

Lars F. Westblade ◽

...

Keyword(s):

Machine Learning ◽

New York ◽

High Risk ◽

Laboratory Test ◽

Laboratory Test Result ◽

Test Results ◽

Rt Pcr ◽

Laboratory Test Results ◽

Laboratory Profile ◽

Test Result

Background. New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods. We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results. A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions. Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

Download Full-text

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation

Journal of Medical Internet Research ◽

10.2196/23948 ◽

2021 ◽

Vol 23 (4) ◽

pp. e23948

Author(s):

Yuanfang Chen ◽

Liu Ouyang ◽

Forrest S Bao ◽

Qian Li ◽

Lei Han ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Laboratory Test ◽

Predictive Accuracy ◽

Learning Approach ◽

Test Results ◽

Laboratory Test Results ◽

Machine Learning Approach ◽

Forest Models ◽

Random Forest Models

Background Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. Objective In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. Methods For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. Results Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. Conclusions Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

Download Full-text

Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning

PLoS Neglected Tropical Diseases ◽

10.1371/journal.pntd.0008960 ◽

2020 ◽

Vol 14 (12) ◽

pp. e0008960

Author(s):

Sheng-Wen Huang ◽

Huey-Pin Tsai ◽

Su-Jhen Hung ◽

Wen-Chien Ko ◽

Jen-Ren Wang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Laboratory Test ◽

Prognostic Models ◽

Severe Dengue ◽

Test Results ◽

Demographic Information ◽

Laboratory Test Results ◽

Artificial Neural

Background Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. Methodology/Principal findings Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subjected to NS1 antigen, IgM and IgG, and qRT-PCR tests, 798 patients including 138 severe cases were enrolled in the study. The primary target outcome was severe dengue. Machine learning models were trained and tested using the patient dataset that included demographic information and qualitative laboratory test results collected on day 1 when they sought medical advice. To develop prognostic models, we applied various machine learning methods, including logistic regression, random forest, gradient boosting machine, support vector classifier, and artificial neural network, and compared the performance of the methods. The artificial neural network showed the highest average discrimination area under the receiver operating characteristic curve (0.8324 ± 0.0268) and balance accuracy (0.7523 ± 0.0273). According to the model explainer that analyzed the contributions/co-contributions of the different factors, patient age and dengue NS1 antigenemia were the two most important risk factors associated with severe dengue. Additionally, co-existence of anti-dengue IgM and IgG in patients with dengue increased the probability of severe dengue. Conclusions/Significance We developed prognostic models for the prediction of dengue severity in patients, using machine learning. The discriminative ability of the artificial neural network exhibited good performance for severe dengue prognosis. This model could help clinicians obtain a rapid prognosis during dengue outbreaks. However, the model requires further validation using external cohorts in future studies.

Download Full-text

Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning

10.1101/2020.06.17.20133892 ◽

2020 ◽

Cited By ~ 1

Author(s):

He Sarina Yang ◽

Ljiljana V. Vasovic ◽

Peter Steel ◽

Amy Chadburn ◽

Yu Hou ◽

...

Keyword(s):

Machine Learning ◽

Laboratory Test ◽

Patient Care ◽

Characteristic Curve ◽

Rapid Identification ◽

Routine Laboratory ◽

Test Results ◽

Rt Pcr ◽

Diagnostic Strategies ◽

Laboratory Test Results

AbstractBackgroundAccurate diagnostic strategies to rapidly identify SARS-CoV-2 positive individuals for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results of this test are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours.MethodWe developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory test results obtained within two days before the release of SARS-CoV-2-RT-PCR result were used to train a gradient boosted decision tree (GBDT) model from 3,346 SARS-CoV-2 RT-PCR tested patients (1,394 positive and 1,952 negative) evaluated at a large metropolitan hospital.ResultsThe model achieved an area under the receiver operating characteristic curve (AUC) of 0.853 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within two days.ConclusionThis model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-COV-2 infected patients before their RT-PCR results are available. This may facilitate patient care and quarantine, indicate who requires retesting, and direct personal protective equipment use while awaiting definitive RT-PCR results.

Download Full-text

Using machine learning to predict laboratory test results

Annals of Clinical Biochemistry International Journal of Laboratory Medicine ◽

10.1177/0004563216663083 ◽

2016 ◽

Vol 53 (6) ◽

pp. 712-712

Author(s):

Edmund E Wilkes

Keyword(s):

Machine Learning ◽

Laboratory Test ◽

Test Results ◽

Laboratory Test Results

Download Full-text

PERBANDINGAN METODE SMOTE RANDOM FOREST DAN SMOTE XGBOOST UNTUK KLASIFIKASI TINGKAT PENYAKIT HEPATITIS C PADA IMBALANCE CLASS DATA

Jurnal Gaussian ◽

10.14710/j.gauss.v9i3.28915 ◽

2020 ◽

Vol 9 (3) ◽

pp. 227-236

Author(s):

Muhamad Syukron ◽

Rukun Santoso ◽

Tatik Widiharih

Keyword(s):

Machine Learning ◽

Hepatitis C ◽

Random Forest ◽

Liver Biopsy ◽

Laboratory Test ◽

Imbalanced Data ◽

Classification Methods ◽

Test Results ◽

Independent Variables ◽

Laboratory Test Results

Hepatitis causes around 1.4 million people die every year. This number makes hepatitis to be the largest contagious disease in the number of deaths after tuberculosis. Liver biopsy is still the best method for diagnosing the stage of hepatitis C, but this method is an invasive, painful, expensive, and can cause complications. Non-invasively method needs to be developed, one of non-invasif method is machine learning. Random Forest and XGboost are classification methods that are often used, since they have many advantages over classical classification methods. The SMOTE algorithm can be used to improve the accuracy of predictions from imbalanced data. the data in this study have 24 independent variables in the form of patients self-data, hepatitis C symptoms, and laboratory test results. The dependent variable in this study is a binary category, namely the level of hepatitis C disease (fibrosis and cirrhosis). The results showed that the random forest and XGboost had an accuracy of around 74% but the recall value was less than 2%. SMOTE random forest dan SMOTE XGboost have an accuracy & recall value more than 75%. SMOTE random forest has a higher accuracy for predicting fibrosis class while SMOTE XGboost is better in cirrhosis class. Variables that are more influental in determining hepatitis C stage are variables from laboratory test. Keyword : Fibrosis, Cirrhosis, Random Forest, SMOTE, XGboost

Download Full-text

Machine learning analysis highlights the down-trending of the proportion of COVID-19 patients with a distinct laboratory result profile

10.1101/2020.11.28.20240150 ◽

2020 ◽

Author(s):

He S. Yang ◽

Yu Hou ◽

Hao Zhang ◽

Amy Chadburn ◽

Lars F. Westblade ◽

...

Keyword(s):

Machine Learning ◽

New York ◽

High Risk ◽

Laboratory Test ◽

Test Results ◽

Rt Pcr ◽

Viral Loads ◽

Laboratory Test Results ◽

Risk Patients ◽

Learning Analysis

AbstractBackgroundNew York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2 confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome.MethodsWe performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis.ResultsA COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle-threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without COVID19-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase.ConclusionsOur study visualized the down-trending of the proportion of SARS-CoV-2 patients with the distinct COVID19-HRP. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

Download Full-text