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Fast and accurate diagnosis of COVID-19 is important to prevent dissemination and disease progression. Artificial Intelligence is known as a universal fitting tool and can be used on the formulation of predictive models for the disease's diagnosis. Thus, we aimed to obtain a neural network (ANN) to diagnose patients as positive or negative COVID-19 based on patient data and blood tests. Data from 1003 patients followed between June/2020 and October/2020 were used. Covid-19 was confirmed in 777 patients by RT-PCR. The inputs considered were: sex, age, ethinicity, body mass index, tabagism, ex-tabagism, alveolar infiltrate, arterial hypertension, diabetes, heart rate, respiration rate, body temperature, oxygen saturation, D-dimer, activated partial thromboplastin time, prothrombin time, levels of: hemoglobin, platelet, leukocytes, lymphocytes, monocytes, neutrophils, lactate dehydrogenase, C-reactive protein, and creatinine. Blood was collected at the patient's admission. The ANNs had 25 inputs and the output was the Covid-19 diagnosis. ANNs with one and two hidden layers were proposed. The number of neurons ranged from 5 to 35. The best result was obtained with an ANN containing 15 neurons in the first and second hidden layers, respectively. The model presented accuracy of 83%, and high capacity for the prediction of true positives (precision of 0.90). The results showed that the ANNs are promising to diagnose Covid-19 based on clinical parameters and blood tests. After future refinements and proper validation, this model could be used to diagnose Covid-19 on daily basis. [ABSTRACT FROM AUTHOR]