File(s) not publicly available
Reason: Slides not to be distributed publicly
Closing the gap in the detection and diagnosis of fungal infections in patients with blood cancers using a machine learning based platform technology
online resourceposted on 29.03.2018, 02:31 by Michelle Ananda-Rajah
Invasive fungal infections cause a life-threatening pneumonia in patients with impaired immunity. Hospitals spend millions of dollars on drugs to manage these infections but are unaware of the types of infections affecting their patients and their outcomes. Surveillance of fungal infections is not occurring in hospitals because fungi infrequently grow in the laboratory and manual surveillance is onerous. As a result, clinicians and hospitals cannot evaluate the effectiveness of preventative efforts, outbreaks may go unnoticed and tailoring therapy according to risk is hampered by the lack of large datasets for a rare disease. Variation is common in radiologist reporting affecting patient care and clinical trials. Our machine learning based platform technology incorporating natural language processing, deep learning based image recognition and the integration of clinical data in an expert system can address these performance gaps with benefits to patients, hospitals, clinicians and trial sponsors.