Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Aiding Remote Diagnosis with Text Mining
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0001-5118-4856
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0003-1126-3781
2018 (English)In: Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018, 2018Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Along with the increase of digital healthcare providers, theinterest in diagnostic aids for remote diagnosis has increasedas well. As patients write about their symptoms themselves,we have access to a type of data which previously was rarelyrecorded, and which has not been filtered by a healthcareprofessional. Knowledge of similar patients and similarsymptoms is beneficial for doctors to arrive at a diagnosis.Therefore, the remote diagnostic process could be aided bypresenting patient cases together with information aboutsimilar patients and their self-reported symptom descriptions.Apart from online diagnosis, such an aid could bebeneficial in many healthcare settings, such as long-distancevisits and knowledge gain from patient diaries.In this paper, we present the impact of aiding remote diagnosisby presenting clusters of similar symptoms, usingsymptom descriptions collected from a virtual visit applicationby the Swedish telemedicine provider KRY. Symptomdescriptions were represented using the bag-of-words modeland were then clustered using the k-means algorithm. Anexperiment was then conducted with 13 doctors, where patientcases were presented together with the most representativewords of the associated cluster, to measure howtheir work was impacted. Results indicated that it was usefulin more complicated cases, but also that future experimentswill require further instructions on how the information is tobe interpreted.

Place, publisher, year, edition, pages
2018.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-259567Scopus ID: 2-s2.0-85071902648OAI: oai:DiVA.org:kth-259567DiVA, id: diva2:1352181
Conference
The Thirty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS-31)
Note

QC 20190930

Available from: 2019-09-17 Created: 2019-09-17 Last updated: 2019-09-30Bibliographically approved
In thesis
1. Creating Knowledge with Data Science for Design in Systems
Open this publication in new window or tab >>Creating Knowledge with Data Science for Design in Systems
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Designing in large-scale engineering systems is a difficult cognitive task undertaken by experts. Knowledge of experts continually changes as they are confronted with similar by different problems in designing in such systems. However, it is also important that designers are presented information that is representative of the system,and that they are cognizant of activities on a system scale so they can create diverse choices for designs in early phase of design process.Data Science has been proven to be effective at informing people for decisions at immediate horizons. But the use of data science to drive long terms designs where experts have to make the right series of decisions i.e. designs is yet unknown. The use of data science is to inform decision makers of activities at system scale.In this thesis, I have looked at how data science can be used to create knowledge in designers for designing in large scale systems. I have also investigated further questions regarding imitation of expertise using AI, and in generating similar knowledge by creating diverse options in design.The results point out that data science can indeed inform designers, change their designs and hence create knowledge. They also point out that design cognition in experts can be partly imitated in data science itself, through careful modeling of the ill-defined problem in design. This therefore points to a promising future direction where data can be used as an interface between human thinking and machine learning, by translation of conceptual forms such as differential diagnoses and cognitive artefacts using data.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2019. p. 33
Series
TRITA-CBH-FOU ; 46
Keywords
Data Science, Design Cognition, Transport, Healthcare, Artificial Intelligence, Imitation, Design Space Exploration
National Category
Computer Systems Health Sciences Transport Systems and Logistics
Research subject
Technology and Health
Identifiers
urn:nbn:se:kth:diva-259566 (URN)978-91-7873-299-9 (ISBN)
Public defence
2019-10-11, T2, Hälsovägen 11, 141 57, Huddinge, 09:30 (English)
Opponent
Supervisors
Note

QC 2019-09-20

Available from: 2019-09-20 Created: 2019-09-18 Last updated: 2019-10-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Scopus

Authority records BETA

Shreenath, Vinutha MagalMeijer, Sebastiaan

Search in DiVA

By author/editor
Shreenath, Vinutha MagalMeijer, Sebastiaan
By organisation
Health Informatics and Logistics
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 70 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf