Patient Deterioration Prediction
“Which inpatients will deteriorate in the next 12 hours?”
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A real-world example
Which inpatients will deteriorate in the next 12 hours?
Failure to rescue costs U.S. hospitals $1.1B annually. Traditional early warning scores (NEWS, MEWS) generate false alarm rates above 90%, causing alert fatigue. A 600-bed hospital averages 15 unexpected ICU transfers per month; each costs $31,000 more than a planned transfer. Catching deterioration 6 hours earlier would save $5.6M per year and 40+ lives.
How KumoRFM solves this
Graph-learned clinical intelligence across your entire patient network
Kumo builds a temporal graph from vitals streams, lab results, medication administrations, and nursing assessments. It learns that a specific pattern of trending MAP decline combined with a new vasopressor order and a nursing note sentiment shift precedes deterioration 8 hours before traditional scores trigger. The model weighs cross-patient signals: when multiple patients on the same unit show subtle vital changes simultaneously, it detects environmental or staffing-related risk factors.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
PATIENTS
| patient_id | age | unit | admit_date |
|---|---|---|---|
| P6001 | 71 | Med-Surg 4A | 2025-03-01 |
| P6002 | 55 | Med-Surg 4A | 2025-03-02 |
| P6003 | 83 | Telemetry 3B | 2025-02-28 |
VITALS
| vitals_id | patient_id | timestamp | hr | bp_sys | spo2 | temp |
|---|---|---|---|---|---|---|
| VT01 | P6001 | 2025-03-03 06:00 | 88 | 132 | 96 | 37.1 |
| VT02 | P6001 | 2025-03-03 10:00 | 102 | 108 | 93 | 38.2 |
| VT03 | P6002 | 2025-03-03 08:00 | 72 | 128 | 98 | 36.8 |
LABS
| lab_id | patient_id | test_name | value | timestamp |
|---|---|---|---|---|
| LB01 | P6001 | Lactate | 3.8 | 2025-03-03 07:00 |
| LB02 | P6001 | WBC | 16.4 | 2025-03-03 07:00 |
| LB03 | P6002 | Lactate | 1.1 | 2025-03-03 06:00 |
MEDICATIONS
| med_id | patient_id | drug | route | admin_time |
|---|---|---|---|---|
| MD01 | P6001 | Norepinephrine | IV | 2025-03-03 09:30 |
| MD02 | P6002 | Metoprolol | PO | 2025-03-03 08:00 |
| MD03 | P6003 | Heparin drip | IV | 2025-03-03 06:00 |
NURSING_NOTES
| note_id | patient_id | timestamp | assessment |
|---|---|---|---|
| NN01 | P6001 | 2025-03-03 08:00 | Pt appears more confused, family concerned |
| NN02 | P6002 | 2025-03-03 08:30 | Resting comfortably, no complaints |
| NN03 | P6003 | 2025-03-03 07:00 | Steady, ambulating in hallway |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(VITALS.RAPID_RESPONSE, 0, 12, hours) FOR EACH PATIENTS.PATIENT_ID WHERE PATIENTS.UNIT != 'ICU'
Prediction output
Every entity gets a score, updated continuously
| PATIENT_ID | UNIT | TIMESTAMP | DETERIORATION_PROB |
|---|---|---|---|
| P6001 | Med-Surg 4A | 2025-03-03 10:15 | 0.91 |
| P6002 | Med-Surg 4A | 2025-03-03 10:15 | 0.07 |
| P6003 | Telemetry 3B | 2025-03-03 10:15 | 0.15 |
Understand why
Every prediction includes feature attributions — no black boxes
Patient P6001 -- 71yo, Med-Surg 4A
Predicted: 91% deterioration probability in next 12 hours
Top contributing features
Heart rate trend (last 4h)
+14 bpm
27% attribution
Lactate level
3.8 mmol/L
24% attribution
New vasopressor initiated
Norepinephrine
21% attribution
SpO2 decline rate
-3% in 4h
16% attribution
Nursing assessment sentiment
Concern noted
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A 600-bed hospital catching inpatient deterioration 6 hours earlier prevents 40+ deaths and saves $5.6M annually in unplanned ICU transfers. Kumo fuses vitals trajectories, lab trends, medication signals, and nursing narrative patterns into a single real-time score that outperforms NEWS/MEWS with 80% fewer false alarms.
Related use cases
Explore more healthcare use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
