Covid-19 rapid test by combining a Random Forest-based web system and blood tests

AbstractBackgroundThe disease caused by the new type of coronavirus, the Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-Cov2 has already caused over 400 thousand deaths to date. The diagnosis of the disease has an important role in combating Covid-19.ObjectiveIn this work, we propose a web system, Heg.IA, which seeks to optimize the diagnosis of Covid-19 through the use of artificial intelligence.MethodThe main ideia is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. It will indicate if the patient is infected with SARS-Cov2 virus, and also predict the type of hospitalization (regular ward, semi-ICU, or ICU).ResultsWe developed a web system called Heg.IA to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU. This application is based on decision trees in a Random Forest architecture with 90 trees. The system showed to be highly efficient, with great results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891% ± 0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations.ConclusionBy using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19. We also expect the system will provide wide access to Covid-19 effective diagnosis and thereby reach and help saving lives.

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Heg.IA: An intelligent system to support diagnosis of Covid-19 based on blood tests

10.21203/rs.3.rs-33864/v1 ◽

2020 ◽

Author(s):

Valter Augusto de Freitas Barbosa ◽

Juliana Carneiro Gomes ◽

Maíra Araújo de Santana ◽

Jeniffer Emídio de Almeida Albuquerque ◽

Rodrigo Gomes de Souza ◽

...

Keyword(s):

Influenza A ◽

Intelligent System ◽

Low Cost ◽

Hematological Parameters ◽

Rapid Test ◽

Albert Einstein ◽

Classification Performance ◽

Kappa Index ◽

Blood Tests ◽

Intelligent Software

Abstract A new kind of coronavirus, the SARS-Cov2, started the biggest pandemic of the century. It has already killed more than 250,000 people. Due to this fact, it is necessary quick and precise easily available diagnosis tests. The current Covid-19 diagnosis benchmark is RT-PCR with DNA identification, but its results takes too long to be available. Tests based on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low when viral charge is reduced. Many studies have been demonstrating the Covid-19 impact in hematological parameters. This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. We employed a dataset provided by Hospital Israelita Albert Einstein, a Brazilian private hospital. The database contains the results of more than one hundred laboratory exams, such as blood count, tests for the presence of viruses such as influenza A, and urine tests, of 5644 patients. Among these patients, 559 of them are infected with SARS-Cov2. We used metaheuristics algorithms to reduce the set of We tested several machine learning methods, and we achieved high classification performance: 95.159% +- 0.693 of overall accuracy, kappa index of 0.903 +- 0.014, sensitivity of 0.968 +- 0.007, precision of 0.938 +- 0.010, and specificity of 0.936 +- 0.011. Experimental results pointed out to Bayes Network as the best configuration. In addition, only 24 blood tests were needed. This points to the possibility of a new low cost rapid test based on common blood exams and intelligent software. The desktop version of the system is fully functional and available for free use.

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A Priori Estimation of the Narrow-Band UVB Phototherapy Outcome for Moderate-to-Severe Psoriasis Based on the Patients’ Questionnaire and Blood Tests Using Random Forest Classifier

Clinical Cosmetic and Investigational Dermatology ◽

10.2147/ccid.s296604 ◽

2021 ◽

Vol Volume 14 ◽

pp. 253-259

Author(s):

Joanna Narbutt ◽

Janusz Krzyścin ◽

Piotr Sobolewski ◽

Małgorzata Skibińska ◽

Marcin Noweta ◽

...

Keyword(s):

Random Forest ◽

Narrow Band ◽

A Priori ◽

Random Forest Classifier ◽

Severe Psoriasis ◽

Blood Tests ◽

A Priori Estimation ◽

Uvb Phototherapy

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ANTHROPOZOONOSIS OF DOGS IN PENITENTIARY INSTITUTIONS

10.31016/978-5-9902340-8-6.2019.20.167-173 ◽

2019 ◽

pp. 167-173

Author(s):

Byakova ◽

Pilip

Keyword(s):

Clinical Signs ◽

Rapid Test ◽

Complete Information ◽

Disease Diagnosis ◽

Dirofilaria Repens ◽

Smear Microscopy ◽

Service Dogs ◽

Blood Tests ◽

Blood Sucking ◽

The Military

The health of service dogs serving in penitentiary institutions must be closely moni-tored. Anthropozoonosis diseases common to humans and animals are especially dangerous. Service dogs are most susceptible to parasitic disease dirofilariasis. This is facilitated by official business trips, including in areas that are unfavorable for this zoonosis, as well as keeping groups in the open air on the territory of the peni-tentiary institution. Helminth is transmitted only through blood-sucking insects, mainly mosquitoes. The source of dirofilariasis is sick dogs that form the natural reservoir of the invasion. In humans, to the sexually mature state, the helminth does not develop. In dogs, there are two forms of dirofilariasis: the subcutaneous form caused by the pathogen Dirofilaria repens and the heart form caused by the Dirofi-laria immitis. The most frequently recorded is subcutaneous form of dirofilariasis. The source of dirofilariasis are sick dogs, they form a more natural invasion reservoir. Morphological blood tests conducted as a result of annual medical examination do not provide complete information about the disease. Diagnosis of zoonosis is carried out comprehensively using special studies for dirofilariasis: blood smear microscopy, immunochromatographic rapid test, anamnesis, based on clinical signs, as well as morphological and biochemical blood tests. The hem test for D.immitis antigens showed a negative result, which proves the most common subcutaneous form of di-rofilariasis caused by Dirofilaria repens and is confirmed by the morphology of the helminth when it is removed from the subcutaneous seals. In the military unit, the extensiveness of invasion of working dogs by the subcutaneous form of dirofilariasis was 18.18%

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Heg.IA: An intelligent system to support diagnosis of Covid-19 based on blood tests

10.1101/2020.05.14.20102533 ◽

2020 ◽

Cited By ~ 1

Author(s):

Valter Augusto de Freitas Barbosa ◽

Juliana Carneiro Gomes ◽

Maira Araujo de Santana ◽

Jeniffer Emidio de Almeida Albuquerque ◽

Rodrigo Gomes de Souza ◽

...

Keyword(s):

Intelligent System ◽

Low Cost ◽

Hematological Parameters ◽

Computational Cost ◽

Rapid Test ◽

Classification Performance ◽

Kappa Index ◽

Blood Tests ◽

Bayes Network ◽

Precise Diagnosis

A new kind of coronavirus, the SARS-Cov2, started the biggest pandemic of the century. It has already killed more than 250,000 people. Because of this, it is necessary quick and precise diagnosis test. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results takes too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact in hematological parameters. This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. We tested several machine learning methods, and we achieved high classification performance: 95.159% +- 0.693 of overall accuracy, kappa index of 0.903 +- 0.014, sensitivity of 0.968 +- 0.007, precision of 0.938 +- 0.010 and specificity of 0.936 +- 0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed. This points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use.

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Medical diagnosis of COVID-19 using blood tests and machine learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/2161/1/012017 ◽

2022 ◽

Vol 2161 (1) ◽

pp. 012017

Author(s):

Krishnaraj Chadaga ◽

Srikanth Prabhu ◽

K Vivekananda Bhat ◽

Shashikiran Umakanth ◽

Niranjana Sampathila

Keyword(s):

Infectious Disease ◽

Random Forest ◽

Medical Diagnosis ◽

Albert Einstein ◽

Rt Pcr ◽

Chain Reaction ◽

False Negatives ◽

Blood Tests ◽

Nearest Neighbours ◽

Polymerase Chain

Abstract Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2), colloquially known as Coronavirus surfaced in late 2019 and is an extremely dangerous disease. RT-PCR (Reverse transcription Polymerase Chain Reaction) tests are extensively used in COVID-19 diagnosis. However, they are prone to a lot of false negatives and erroneous results. Hence, alternate methods are being researched and discovered for the detection of this infectious disease. We diagnose and forecast COVID-19 with the help of routine blood tests and Artificial Intelligence in this paper. The COVID-19 patient dataset was obtained from Israelita Albert Einstein Hospital, Brazil. Logistic regression, random forest, k nearest neighbours and Xgboost were the classifiers used for prediction. Since the dataset was extremely unbalanced, a technique called SMOTE was used to perform oversampling. Random forest obtained optimal results with an accuracy of 92%. The most important parameters according to the study were leukocytes, eosinophils, platelets and monocytes. This preliminary COVID-19 detection can be utilised in conjunction with RT-PCR testing to improve sensitivity, as well as in further pandemic outbreaks.

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Diagnosis of Silica Induced Granulomatous Hypersensitivity

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100054303 ◽

1982 ◽

Vol 40 ◽

pp. 336-337

Author(s):

C. W. Mehard ◽

W. L. Epstein

Keyword(s):

Electron Microscope ◽

White Female ◽

X Rays ◽

Blood Tests ◽

Analytical Electron ◽

Analytical Electron Microscope

The underlying cause of a disease may not he readily apparent but may have a long history in development. We report one such case which was diagnosed with the aid of the analytical electron microscope.The patient, a 48 yr. old white female, developed a tender nodule on the sole of her foot in December, 1981. Subsequently additional lesions developed on the same foot resulting in deep pain and tenderness. Superficial lesions also extended up to the knee on both legs. No abnormalities were revealed in blood tests or chest X-rays.

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