Clinical Decision Support

Enhance clinical decision making with facts from medical documents

Case Study:

Improve Patient Flow Forecasting
Key factors that influence a patient’s flow (How likely they are to admitted? For how long? For what?):
Volume of arrivals
  • Outpatient
  • Referrals
  • Emergency Room
  • Operation Room
Admission specialty
  • Oncology
  • Hip-replacement
  • Renal Disease
  • Cardiology, …
Timing of arrival
  • Hour of the day
  • Day of the week
  • Holidays
Seasonal variables
  • Flu season
  • Natural disasters
Patient's length of stay
  • per unit (ICU, CVICU, …)
Nurse staffing levels & skill mix:
  • Certified Nurses
  • Licensed N.P.’s
  • Unlicensed Staff
  • Unique certifications
Results
The results of an NLP pipeline are often used as features in another machine learning model, that also leverages all the available structured data signals.
John Snow Labs enabled real-time decision-making and strategic planning, by predicting:
  • Bed demand
  • Safe staffing levels
  • Hospital gridlock
8xfaster training
16xfaster prediction

The NLP Challenge

Emergency Room Triage Notes
  • states started last night, upper abd, took alka seltzer approx 0500, no relief. nausea no vomiting
  • Since yeatreday 10/10 “constant Tylenol 1 hr ago. +nausea. diaphoretic. Mid abd radiates to back
  • Generalized abd radiating to lower x 3 days accompanied by dark stools. Now with bloody stool this am. Denies dizzy, sob, fatigue. Visiting from Japan on business.”
Features

Type of Pain

Intensity of Pain

Body part of region

Symptoms

Onset of symptoms

Attempted home remedy

Why John Snow Labs?

01

Clinical Language Understanding

02

State of the Art Accuracy

03

Tuned to Emergency Room Jargon

Case Study:

Precision Medicine

Recommending the next best action requires biomarkers that are only available in pathology, radiology and sequencing.

Spark NLP can automatically extract specific tumor characteristics and ​​automate knowledge extraction from pathology reports

Why John Snow Labs?

01

Out-of-the-box Oncology Specific Models

02

Map entities to standard medical terminologies

03

Identify relationships between entities in reports

We can train your team to use our software, or build complete solutions per your exact needs.

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