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Audio_recordings
Diagnoses
Medical_papers
Patients_records
Transcripts
Indexing_ES.py
README.md
Research_merging.ipynb
api_keys.py
bootstrap.min.css
config.py
diag_init_script.js
diagnosis.html
diagnosis.js
diagnosis_init.html
diagnosis_network.ipynb
getPatientRecord.py
graph_suggestion.py
index.html
main_output.py
main_search.py
mini_app.ipynb
package-lock.json
paper.html
paper_script.js
patient_records.ipynb
report.html
script.js
script.py
search_medline.py
searching_ES.py
speech2text.py

README.md

DocAssist

This is a project made for OxfordHack 2017.

DocAssist is an application facilitating consultations for doctors. Medical practitioners are faced with an increasing amount of bureaucracy, and find it hard to keep records on every consultation, to follow each patient's diagnoses, and to keep up-to-date with recent developments in medical research.

DocAssist works in four stages:

  • The entire consultation is recorded, as a dialogue between the doctor and the patient. The consultation is conveniently subdivided into six separate steps (history, examination, tests, diagnosis, treatment, and summary).
  • A speech recognition service (Microsoft Cognitive Services API) is run on the recordings, extracting meaningful data and generating automatically a record of the consultation for regulatory purposes.
  • A recommendation system, based on clusters of common diagnoses extracted from a database of patient records, presents the doctor with an ordered list of diagnoses commonly associated with the current one. For instance, gall stones are commonly associated with diabetes, and thus DocAssist might encourage the doctor to look into tests for diabetes.
  • The final diagnosis is used to match relevant papers in the medical literature. We use MedLine's API and ElasticSearch on a local database.

Instructions to run:

  • speech2text.py will launch speech recognition on the files in the Audio_recordings folder.
  • graph_suggestion.py will use the graph with its communities in Diagnoses/ to return suggestions of potential diagnoses based on the current diagnosis and the patient's history.
  • mainSearch.py will launch a medical research papers search based on a local database (with ElasticSearch) and on Medline's API.