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
Some medical practitioners are open to a patient's personal research, as this can open lines of communication between doctors and patients, and prove valuable in eliciting more complete or pertinent information from the patient about their present condition.
Other doctors express concern about patients who self-diagnose on the basis of information obtained from the Internet when the patient demonstrates an incomplete or distorted understanding of other diagnostic possibilities and medical likelihoods. A patient who exaggerates one set of symptoms in support of their self-diagnosis while minimizing or suppressing contrary symptoms can impair rather than enhance a doctor's ability to reach a correct diagnosis.
Cyberchondria, otherwise known as 'compucondria', is the unfounded escalation of concerns about common symptomology based on review of search results and literature online. Articles in popular media position cyberchondria anywhere from temporary neurotic excess to adjunct hypochondria. Cyberchondria is a growing concern among many healthcare practitioners as patients can now research any and all symptoms of a rare disease, illness or condition, and manifest a state of medical anxiety.