Example Projects
MOISSCode ships with five example protocols in the examples/ directory. Three of these are comprehensive, production-style projects that demonstrate real-world clinical workflows across different medical domains.
Quick Start
Run any example with:
moiss run examples/icu_admission.moiss -v
Or via Python directly:
python -m moisscode.cli run examples/icu_admission.moiss -v
1. ICU Admission Bundle
File: examples/icu_admission.moiss
Domain: Critical Care
Events generated: 103
A comprehensive ICU admission workup covering everything from initial severity scoring to nutrition planning and multi-drug therapy.
Modules Used
| Module | Purpose |
|---|---|
med.scores | SOFA + qSOFA severity scoring |
med.lab | 8 individual lab results + eGFR (CKD-EPI 2021) |
med.nutrition | BMI, Harris-Benedict, ICU caloric targets, TPN, IV fluids |
med.pk | 3-drug interaction check + dose validation |
med.db | Patient and lab persistence |
KAE | Biomarker tracking (HR, lactate, SpO2) |
Process Flow
graph TD
A[Patient Admission] --> B[Severity Scoring]
B --> C[SOFA + qSOFA]
C --> D{SOFA >= 6?}
D -->|Yes| E[CRITICAL Alert]
D -->|No| F[Continue]
E --> F
F --> G[KAE Biomarker Tracking]
G --> H[Lab Interpretation]
H --> I[8 labs: WBC, Hgb, Plt, Na, K, Cr, Glucose, Lactate]
I --> J[Renal Function: eGFR]
J --> K{eGFR < 60?}
K -->|Yes| L[Dose Adjustment Warning]
K -->|No| M[Continue]
L --> M
M --> N[Nutrition Assessment]
N --> O[BMI + Harris-Benedict + ICU targets + TPN + Fluids]
O --> P[PK Drug Workflow]
P --> Q[Interaction Check x3 drugs]
Q --> R[Administer: Norepinephrine + Vancomycin + Meropenem]
R --> S[Database Persistence]
S --> T[Clinical Assessment: Sepsis]
T --> U[Summary Alerts]
Code
protocol ICU_Admission {
input: Patient p;
// -- Severity Scoring --
let sofa = med.scores.sofa(p);
let qsofa = med.scores.qsofa(p);
if sofa >= 6 {
alert "SOFA >= 6 -> High mortality risk" severity: critical;
}
// -- Vital Sign Tracking via KAE --
track p.hr using KAE;
track p.lactate using KAE;
track p.spo2 using KAE;
// -- Lab Interpretation --
let wbc = med.lab.interpret("WBC", 18.5);
let hgb = med.lab.interpret("Hgb", 8.2);
let plt = med.lab.interpret("Plt", 95);
let sodium = med.lab.interpret("Na", 131);
let potassium = med.lab.interpret("K", 5.8);
let creatinine = med.lab.interpret("Cr", 2.1);
let glucose = med.lab.interpret("Glucose", 220);
let lactate_lab = med.lab.interpret("Lactate", 3.2);
// -- Renal Function (CKD-EPI 2021) --
let gfr_result = med.lab.gfr(2.1, 55, "M");
// -- Nutrition Assessment --
let bmi = med.nutrition.bmi(70, 170);
let caloric_need = med.nutrition.harris_benedict(70, 170, 55, "M");
let icu_targets = med.nutrition.icu_caloric_target(70, "acute");
let fluids = med.nutrition.maintenance_fluids(70);
let tpn = med.nutrition.tpn_calculate(70, 1400, 84);
// -- Multi-Drug PK Workflow --
let drugs = ["Vancomycin", "Meropenem", "Norepinephrine"];
for drug in drugs {
med.pk.check_interactions(drug);
}
administer Norepinephrine dose: 0.1 mcg/kg/min;
administer Vancomycin dose: 1000 mg;
administer Meropenem dose: 1000 mg;
// -- Persistence + Assessment --
med.db.save_patient("ICU-001", "Admission Patient", 55, 70.0, "M");
assess p for sepsis;
}
Sample Output (abbreviated)
[1] LOG: [Import] med.lab
[2] LOG: [Import] med.nutrition
[3] LOG: [Import] med.pk
[7] LOG: [Protocol] Executing: ICU_Admission
[9] LOG: [Let] sofa = 1
[12] LOG: [Let] qsofa = 3
[14] LOG: [Track] p.hr = 110 (KAE: pos=91.67, vel=21.6471)
[15] LOG: [Track] p.lactate = 3.2 (KAE: pos=2.67, vel=0.6306)
[16] LOG: [Track] p.spo2 = 94 (KAE: pos=78.33, vel=18.5098)
...
[PK] Interaction check: Vancomycin ? No known interactions
[PK] Interaction check: Meropenem ? No known interactions
[PK] Administer Norepinephrine 0.1 mcg/kg/min | MOISS: PROPHYLACTIC
[PK] Administer Vancomycin 1000 mg | MOISS: FUTILE
[PK] Administer Meropenem 1000 mg | MOISS: FUTILE
[DB] Patient ICU-001 saved
...
[93] LOG: [Assess] Evaluating p for sepsis...
[94] LOG: [Assess] Result: score=3, scoring=qSOFA, risk=HIGH
2. Antibiotic Stewardship Advisor
File: examples/abx_stewardship.moiss
Domain: Infectious Disease
Events generated: 113
An intelligent antimicrobial selection workflow that identifies organisms, checks susceptibility, evaluates pharmacogenomics, and selects therapy based on patient severity.
Modules Used
| Module | Purpose |
|---|---|
med.micro | Organism ID, susceptibility (MIC), Gram stain DDx, empiric therapy |
med.lab | Renal function (eGFR) for dose adjustment |
med.pk | Drug interaction screening |
med.genomics | CYP450 metabolism, drug-gene checks, dosing guidance |
med.research | HIPAA de-identification for research export |
med.db | Patient and lab persistence |
Process Flow
graph TD
A[Patient Evaluation] --> B[qSOFA Severity]
B --> C{qSOFA >= 2?}
C -->|Yes| D[Urgent: Broad Spectrum]
C -->|No| E[Targeted: Narrow Spectrum]
A --> F[Organism Identification]
F --> G[E. coli + S. aureus + P. aeruginosa]
G --> H[Gram Stain DDx]
H --> I[Susceptibility Testing]
I --> J[5 MIC Breakpoint Tests]
A --> K[Empiric Therapy Lookup]
K --> L[UTI + Pneumonia + Sepsis]
A --> M[Renal Function]
M --> N{eGFR < 30?}
N -->|Yes| O[Dose Adjustment Warning]
N -->|No| P[Standard Dosing]
A --> Q[Pharmacogenomics]
Q --> R[CYP2C19 Check: Voriconazole]
R --> S[Dosing Guidance]
S --> T[Multi-Drug Interaction Screen]
D --> U[Administer Meropenem + Vancomycin]
E --> V[Administer Ceftriaxone]
U --> W[De-identify for Research]
V --> W
W --> X[Final Assessment]
Code
protocol AntibioticStewardship {
input: Patient p;
let score = med.scores.qsofa(p);
// -- Organism Identification --
let ecoli_profile = med.micro.identify("e_coli");
let staph_profile = med.micro.identify("s_aureus");
let pseudo_profile = med.micro.identify("p_aeruginosa");
let gram_neg_rods = med.micro.gram_stain_ddx("negative", "rod");
// -- Susceptibility Testing --
let ecoli_cipro = med.micro.susceptibility("e_coli", "Ciprofloxacin", 0.25);
let ecoli_ceftri = med.micro.susceptibility("e_coli", "Ceftriaxone", 0.5);
let staph_vanco = med.micro.susceptibility("s_aureus", "Vancomycin", 1.0);
// -- Empiric Therapy --
let sepsis_empiric = med.micro.empiric_therapy("sepsis");
// -- Pharmacogenomics --
let cyp_voriconazole = med.genomics.drug_gene_check("Voriconazole");
let dosing = med.genomics.dosing_guidance("CYP2C19", "Voriconazole", "*1/*2");
let genomic_interactions = med.genomics.interaction_check(
["Voriconazole", "Omeprazole", "Clopidogrel"]
);
// -- Therapy Selection --
if score >= 2 {
administer Meropenem dose: 1000 mg;
administer Vancomycin dose: 1000 mg;
} else {
administer Ceftriaxone dose: 1000 mg;
}
// -- Research Export --
med.research.deidentify(p);
assess p for sepsis;
}
Sample Output (abbreviated)
[7] LOG: [Protocol] Executing: AntibioticStewardship
[9] LOG: [Let] score = 3
...
[17] LOG: [Let] ecoli_profile = {type: MICRO_ID, organism: Escherichia coli,
gram: negative, shape: rod, ...}
[22] LOG: [Let] gram_neg_rods = {type: GRAM_DDX, differentials:
[Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, ...]}
...
[30] LOG: [Let] ecoli_cipro = {interpretation: SUSCEPTIBLE, mic: 0.25,
breakpoint: 1.0}
...
[45] LOG: [Let] cyp_voriconazole = {gene: CYP2C19, impact: MAJOR,
recommendation: Consider dose adjustment}
...
[PK] Administer Meropenem 1000 mg | MOISS: FUTILE
[PK] Administer Vancomycin 1000 mg | MOISS: FUTILE
...
[103] LOG: [Assess] Result: score=3, scoring=qSOFA, risk=HIGH
3. Outbreak Response Toolkit
File: examples/outbreak_response.moiss
Domain: Public Health & Epidemiology
Events generated: 106
Population-level epidemic modeling combined with patient-level triage. Models disease transmission with SIR and SEIR compartmental models, calculates public health metrics, and triages individual patients.
Modules Used
| Module | Purpose |
|---|---|
med.epi | Disease params, R0, SIR/SEIR simulation, herd immunity, incidence, prevalence, CFR |
med.micro | Outbreak pathogen ID and susceptibility |
med.lab | Surveillance lab interpretation (WBC, CRP, Procalcitonin, Lactate) |
med.scores | Patient triage (qSOFA) |
med.db | Surveillance data persistence |
med.research | De-identification for outbreak reporting |
Process Flow
graph TD
A[Outbreak Detection] --> B[Disease Parameter Lookup]
B --> C[COVID-19 + Measles + Influenza]
A --> D[R0 Calculation]
D --> E{R0 >= 2?}
E -->|Yes| F[CRITICAL: Sustained Transmission]
E -->|No| G[Monitor]
D --> H[Herd Immunity Threshold]
A --> I[Epidemic Modeling]
I --> J[SIR Model: 100K population, 90 days]
I --> K[SEIR Model: with exposed compartment]
A --> L[Population Metrics]
L --> M[Incidence Rate]
L --> N[Prevalence]
L --> O[Case Fatality Rate]
A --> P[Pathogen ID]
P --> Q[K. pneumoniae identification]
Q --> R[Susceptibility: Meropenem + Ciprofloxacin]
A --> S[Patient Triage]
S --> T[qSOFA Scoring]
T --> U{qSOFA >= 2?}
U -->|Yes| V[RED: Administer Meropenem]
U -->|No| W[YELLOW/GREEN: Monitor]
V --> X[De-identify + Report]
W --> X
Code
protocol OutbreakSurveillance {
input: Patient p;
// -- Disease Parameters --
let covid_params = med.epi.disease_params("covid19");
let measles_params = med.epi.disease_params("measles");
// -- R0 Calculation --
let r0_estimate = med.epi.r0(0.3, 0.1);
let herd_thresh = med.epi.herd_immunity(3.0);
// -- Epidemic Modeling --
let sir_result = med.epi.sir_model(100000, 10, 0.3, 0.1, 90);
let seir_result = med.epi.seir_model(100000, 10, 0.3, 0.1, 0.2, 90);
// -- Population Metrics --
let incidence = med.epi.incidence_rate(450, 500000, 30);
let prev = med.epi.prevalence(2500, 500000);
let cfr = med.epi.cfr(12, 450);
// -- Pathogen Identification --
let pathogen = med.micro.identify("k_pneumoniae");
let kp_mero = med.micro.susceptibility("k_pneumoniae", "Meropenem", 0.25);
// -- Patient Triage --
let qsofa_score = med.scores.qsofa(p);
track p.hr using KAE;
track p.spo2 using KAE;
if qsofa_score >= 2 {
alert "Patient triaged RED" severity: critical;
administer Meropenem dose: 1000 mg;
}
med.research.deidentify(p);
assess p for sepsis;
}
Sample Output (abbreviated)
[7] LOG: [Protocol] Executing: OutbreakSurveillance
[9] LOG: [Let] covid_params = {type: EPI_DISEASE, R0: 3.0,
incubation_days: 5, infectious_days: 10, cfr: 1.0, ...}
...
[15] LOG: [Let] r0_estimate = {type: EPI_R0, R0: 3.0, status: EPIDEMIC,
herd_immunity_threshold: 0.667}
[17] LOG: [Let] herd_thresh = {threshold_percent: 66.67,
interpretation: 66.7% of population must be immune}
...
[19] LOG: [Let] sir_result = {type: EPI_SIR, population: 100000,
peak_infected: 28933, peak_day: 45, attack_rate: 94.06}
[21] LOG: [Let] seir_result = {type: EPI_SEIR, population: 100000,
peak_infected: 14960, peak_day: 52, incubation_period: 5.0}
...
[29] LOG: [Let] incidence = {incidence_per_100k_per_year: 1095.0}
[31] LOG: [Let] prev = {prevalence_percent: 0.5, prevalence_per_100k: 500.0}
[33] LOG: [Let] cfr = {cfr_percent: 2.667}
...
[95] LOG: [Assess] Result: score=3, scoring=qSOFA, risk=HIGH
4. Diabetes CGM Dashboard
Domain: Endocrinology / Diabetes Management
A comprehensive diabetes management protocol using med.glucose, med.lab, med.scores, med.icd, and med.pk.
Read the full step-by-step walkthrough
5. Drug Discovery Screening Pipeline
Domain: Medicinal Chemistry / Pharmaceutical R&D
An end-to-end compound screening and clinical trial design workflow using med.chem, med.pk, med.research, and med.lab.
Read the full step-by-step walkthrough
Legacy Examples
Two simpler examples are also included for quick reference:
examples/sepsis_workup.moiss- Basic sepsis screening with custom types and loopsexamples/pk_workflow.moiss- PK calculations, MOISS classification, and billingexamples/language_features.moiss- Syntax showcase (variables, conditionals, alerts)
See Also
- Getting Started — installation and first protocol
- Language Guide — full syntax reference
- Diabetes CGM Dashboard — step-by-step diabetes walkthrough
- Drug Discovery Pipeline — step-by-step drug screening walkthrough