Dataset: 9.3K articles from Wikipedia (CC BY-SA).
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Made by DATEXIS (Data Science and Text-based Information Systems) at Beuth University of Applied Sciences Berlin

Deep Learning Technology: Sebastian Arnold, Betty van Aken, Paul Grundmann, Felix A. Gers and Alexander Löser. Learning Contextualized Document Representations for Healthcare Answer Retrieval. The Web Conference 2020 (WWW'20)

Funded by The Federal Ministry for Economic Affairs and Energy; Grant: 01MD19013D, Smart-MD Project, Digital Technologies

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Results for Query ‹ Mycobacterium fortuitum screening

Mycobacterium fortuitum – Description

Mycobacterium fortuitum – Treatment

Mycobacterium avium-intracellulare infection – Diagnosis

Extensively drug-resistant tuberculosis – Diagnosis

Mycobacterium avium-intracellulare infection – Diagnosis | HIV infection

Extensively drug-resistant tuberculosis – Prevention

Miliary tuberculosis – Diagnosis

Opportunistic infection – Veterinary treatment

Multiple drug resistance – Preventing the emergence of antimicrobial resistance

Multi-drug-resistant tuberculosis – Prevention

Multi-drug-resistant tuberculosis – Treatment

Multiple drug resistance – Antiparasitic resistance

Tuberculosis in relation to HIV – Research at molecular level

Opportunistic infection – Treatment

Prosector's wart – Diagnosis

Miliary tuberculosis – Prognosis

Tuberculosis – Diagnosis | Latent tuberculosis

Tuberculous lymphadenitis – Diagnosis

Tuberculosis – Diagnosis | Active tuberculosis

Tuberculosis in relation to HIV – Treatment

Buruli ulcer – Diagnosis

Paratuberculosis – Human risks

Prosector's wart – Treatment

Pneumonia – Diagnosis | Differential diagnosis

Pneumonia – Prognosis | Clinical prediction rules