Data

The data page contains the shared Pydantic value objects used by suites, backends, metrics, and benchmark runs.

PatientRecord and SuiteConfig validate inputs at construction time. This is especially useful for public suite APIs because invalid synthetic counts, invalid test splits, and malformed record shapes fail early.

Enums And Records

base

krisis/data/base.py

Abstract base classes for the Krisis data layer. All domain-specific data modules (CKD, Hypertension, Diabetes) inherit from these contracts.

FeatureSet

Bases: str, Enum

Controls which feature set a suite exposes to the benchmark.

FULL — all features in the raw dataset, including weak signals and clinically ambiguous markers. Use this to stress-test models on messy, real-world input.

REDUCED — the validated subset derived from feature selection (e.g. Pearson correlation). Tighter signal, less noise. Use this for focused clinical reasoning evaluation.

Task

Bases: str, Enum

The clinical reasoning task the benchmark will evaluate.

DETECTION — binary: condition present vs. not present. The simplest task. Most models handle this reasonably.

STAGING — multiclass: assign the correct disease stage (e.g. CKD Stage 1–5). Harder. Requires understanding of clinical thresholds.

PROGRESSION — temporal: given a patient trajectory across multiple time points, predict direction of disease progression. The hardest task. Requires longitudinal reasoning.

PatientRecord

Bases: BaseModel

A single patient record passed to a model backend for evaluation.

features — the clinical markers for this patient as a dict e.g. {"sc": 1.2, "hemo": 10.4, "htn": "yes", ...}

label — the ground truth label for this record. For DETECTION: 0 or 1 For STAGING: integer stage (1–5) For PROGRESSION: direction string ("stable", "worsening", "improving") or next-stage integer

metadata — optional dict for anything that shouldn't be passed to the model but is useful for result analysis e.g. {"egfr": 42.3, "ckd_stage": 3, "sex": "female"}

SuiteConfig

Bases: BaseModel

Configuration passed to a BaseDataSuite at instantiation.

features — FULL or REDUCED feature set task — DETECTION, STAGING, or PROGRESSION seed — random seed for reproducibility across all stochastic operations (imputation, generation, splits) n_synthetic — number of synthetic patient records to generate. Set to 0 to use only real records from the source dataset. test_size — fraction of records held out for evaluation (0.0–1.0)