Length of Stay Prediction
“How many days will this patient stay?”
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A real-world example
How many days will this patient stay?
Inaccurate length-of-stay estimates cascade into bed shortages, surgical cancellations, and staff misalignment. A 500-bed hospital where average LOS is off by 1.5 days loses $4.2M annually in underutilized capacity and overtime staffing. Traditional severity scores (APR-DRG) ignore the temporal progression of labs and the network effects of shared care teams.
How KumoRFM solves this
Graph-learned clinical intelligence across your entire patient network
Kumo models the temporal trajectory of lab results, procedure sequences, and medication adjustments as a dynamic graph. It learns that patients with specific lab trend patterns (declining creatinine + stable WBC) under certain care teams discharge predictably faster. The relational structure captures how staffing patterns, bed assignment history, and concurrent patient load affect individual LOS.
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 | gender | comorbidity_count |
|---|---|---|---|
| P3001 | 65 | F | 3 |
| P3002 | 78 | M | 5 |
| P3003 | 42 | F | 1 |
ADMISSIONS
| admission_id | patient_id | admit_date | department | drg_code |
|---|---|---|---|---|
| ADM01 | P3001 | 2025-02-28 | Cardiology | 291 |
| ADM02 | P3002 | 2025-03-01 | Pulmonology | 190 |
| ADM03 | P3003 | 2025-03-02 | Orthopedics | 470 |
PROCEDURES
| procedure_id | admission_id | cpt_code | performed_date |
|---|---|---|---|
| PR01 | ADM01 | 33533 | 2025-02-28 |
| PR02 | ADM02 | 31624 | 2025-03-02 |
| PR03 | ADM03 | 27447 | 2025-03-02 |
LAB_RESULTS
| lab_id | patient_id | test_name | value | collected_date |
|---|---|---|---|---|
| L001 | P3001 | Troponin | 0.42 | 2025-02-28 |
| L002 | P3002 | WBC | 14.2 | 2025-03-01 |
| L003 | P3003 | Hemoglobin | 11.8 | 2025-03-02 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(LAB_RESULTS.*, 0, 30, days) FOR EACH ADMISSIONS.ADMISSION_ID -- Regression target: days from admit to discharge
Prediction output
Every entity gets a score, updated continuously
| ADMISSION_ID | PATIENT_ID | ADMIT_DATE | PREDICTED_LOS_DAYS |
|---|---|---|---|
| ADM01 | P3001 | 2025-02-28 | 6.2 |
| ADM02 | P3002 | 2025-03-01 | 11.4 |
| ADM03 | P3003 | 2025-03-02 | 3.1 |
Understand why
Every prediction includes feature attributions — no black boxes
Admission ADM02 -- 78yo Male, Pulmonology
Predicted: 11.4 days predicted length of stay
Top contributing features
Comorbidity count
5 conditions
28% attribution
WBC trend (first 24h)
Rising (+2.1)
23% attribution
DRG historical median LOS
8.5 days
19% attribution
Prior admissions (last 6 months)
3 stays
16% attribution
Department weekend staffing ratio
0.6x
14% 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 500-bed hospital improving LOS prediction accuracy by 1.5 days saves $4.2M annually through better bed utilization, fewer surgical cancellations, and optimized staffing. Kumo captures lab trajectories and care-team dynamics that severity scores ignore.
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.
