DBbun/1M_Insomnia_Nature_SR_2018_v1.0

Patient Cohort — 1,000,000 Rows inspired by the published Harvard-Merck study: Kartoun U, Aggarwal R, Beam AL, Pai JK, Chatterjee AK, Fitzgerald TP, Kohane IS, Shaw SY. Development of an Algorithm to Identify Patients with Physician-Documented Insomnia. Sci Rep. 2018 May 18;8(1):7862. doi: 10.1038/s41598-018-25312-z. PMID: 29777125; PMCID: PMC5959894.

Created by DBbun LLC. © DBbun LLC. Commercial use requires a license.
For licensing inquiries, contact DBbun LLC.

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Overview

This package contains a large, practice-ready patient table with 1,000,000 rows designed for education, demonstrations, and method development in machine learning and analytics.

• Grain: one row = one patient.
• Target column: insomnia_class (0/1) for training and evaluation in binary classification tasks.
• Scope: demographics, utilization-style counts, comorbidity indicators, and outcome label.
• Important: This dataset is not for clinical use.

This document focuses on how to use the dataset effectively. It does not describe its provenance or internal construction.

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Files

• patients.csv — the main table with 1,000,000 patients.

You may also produce your own analysis outputs (e.g., metrics CSVs, plots) alongside the data.

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Table schema

Each row is one patient. Columns cover demographics, counts, comorbidities, and the label.

Column	Type	Description	Typical Values / Range	Missing?
patient_id	string	Unique ID	P000001 …	No
age	float	Age in years	≥ 18	Rare
sex	category	Administrative sex	Female, Male	No
bmi	float	Body mass index	~10–70	Yes
ethnicity	category	Race/ethnicity bucket	Caucasian, African American, Hispanic, Asian, Other, Unknown	Yes
smoking_status	category	Tobacco status	Current, Past, Never, Unknown	Yes
emr_fact_count	int	Total EMR facts (activity proxy)	0–several thousand	Yes
sleep_disorder_note_count	int	Sleep-related note count	0+	No
insomnia_billing_code_count	int	Insomnia diagnosis code count	0+	No
anx_depr_billing_code_count	int	Anxiety/depression code count	0+	No
psych_note_count	int	Psychiatry-related note count	0+	No
insomnia_rx_count	int	Insomnia-related prescription count	0+	No
joint_disorder_billing_code_count	int	Musculoskeletal/joint code count	0+	No
Comorbidity flags	int (0/1)	Indicator variables (see list below)	{0,1}	No
insomnia_probability	float	Scoring column in (0,1) useful for ranking	[0,1]	No
insomnia_class	int (0/1)	Label for supervised learning	{0,1}	No

Comorbidity flag columns (0/1):
hypertension, lipid_metabolism_disorder, diabetes, gastrointestinal_disorder, anxiety_or_depression, psychiatric_disorder, pneumonia, obesity, congestive_heart_failure, coronary_artery_disease, asthma, copd, cerebrovascular_disease, afib_or_flutter, cancer, peripheral_vascular_disease, osteoporosis, ckd_or_esrd, renal_failure.

Some columns intentionally include missing values to mirror common data challenges.

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Loading at scale (Python / pandas)

import pandas as pd

dtypes = {
    "patient_id": "string",
    "age": "float32",
    "bmi": "float32",
    "emr_fact_count": "int32",
    "sleep_disorder_note_count": "int16",
    "insomnia_billing_code_count": "int16",
    "anx_depr_billing_code_count": "int16",
    "psych_note_count": "int16",
    "insomnia_rx_count": "int16",
    "joint_disorder_billing_code_count": "int16",
    # binary flags as compact integers
    "hypertension": "uint8", "lipid_metabolism_disorder": "uint8", "diabetes": "uint8",
    "gastrointestinal_disorder": "uint8", "anxiety_or_depression": "uint8",
    "psychiatric_disorder": "uint8", "pneumonia": "uint8", "obesity": "uint8",
    "congestive_heart_failure": "uint8", "coronary_artery_disease": "uint8",
    "asthma": "uint8", "copd": "uint8", "cerebrovascular_disease": "uint8",
    "afib_or_flutter": "uint8", "cancer": "uint8", "peripheral_vascular_disease": "uint8",
    "osteoporosis": "uint8", "ckd_or_esrd": "uint8", "renal_failure": "uint8",
    "insomnia_probability": "float32",
    "insomnia_class": "uint8"
}

cat_cols = ["sex", "ethnicity", "smoking_status"]

# Option A: read all at once
df = pd.read_csv("patients.csv", dtype=dtypes)
for c in cat_cols:
    df[c] = df[c].astype("category")

# Option B: chunked reading to limit peak memory
chunks = pd.read_csv("patients.csv", dtype=dtypes, chunksize=100_000)
df = pd.concat((chunk.assign(**{c: chunk[c].astype("category") for c in cat_cols}) for chunk in chunks), ignore_index=True)

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Quick EDA (Exploratory Data Analysis)

print(df.shape)
print("Label prevalence:", df["insomnia_class"].mean())

# Missingness overview
missing = df.isna().mean().sort_values(ascending=False).head(20)
print(missing)

# Example correlation matrix for numeric counts
key = ["sleep_disorder_note_count","insomnia_billing_code_count","anx_depr_billing_code_count",
       "psych_note_count","insomnia_rx_count","joint_disorder_billing_code_count","emr_fact_count"]
print(df[key].corr(method="spearman"))

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Recommended preprocessing (for ML)

• Imputation: median (numeric), most-frequent (categorical).
• Encoding: one-hot for sex, ethnicity, smoking_status.
• Scaling: standardize numeric features for linear models (trees are scale-agnostic).
• Outliers: consider clipping or robust scalers for skewed counts.
• Splitting: stratify by insomnia_class for train/test.

Example (scikit-learn pipeline):

from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

y = df["insomnia_class"].astype(int)
X = df.drop(columns=["insomnia_class"])

cat_cols = X.select_dtypes(include=["category","object"]).columns.tolist()
num_cols = X.select_dtypes(include=[np.number]).columns.tolist()

preprocess = ColumnTransformer([
    ("num", Pipeline([("imp", SimpleImputer(strategy="median")),
                      ("scaler", StandardScaler())]), num_cols),
    ("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
                      ("ohe", OneHotEncoder(handle_unknown="ignore"))]), cat_cols),
])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y, random_state=7)

clf = Pipeline([("prep", preprocess), ("lr", LogisticRegression(max_iter=500))]).fit(X_train, y_train)
print("ROC-AUC:", roc_auc_score(y_test, clf.predict_proba(X_test)[:,1]))

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Slices & reporting

When reporting results, consider per-group summaries for fairness diagnostics:

for col in ["sex","ethnicity"]:
    print("\nSlice:", col)
    print(df.groupby(col)["insomnia_class"].mean())

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Caveats

• This dataset is designed for education and reproducible demonstrations.
• It must not be used for clinical decision-making or any activity involving real individuals.
• Demographic proportions and feature distributions are provided as-is and should not be interpreted as representing any specific population or institution.

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Attribution & License

© DBbun LLC. All rights reserved.
Any commercial use requires a license from DBbun LLC.