| name | scientific-clinical-reporting |
| description | 臨床レポート自動生成スキル。検査結果サマリー (SOAP ノート)、バイオマーカー
プロファイルレポート、薬理ゲノミクスレポート、臨床試験要約を構造化テンプレート
(PDF/LaTeX/HTML) で出力。HL7 FHIR DiagnosticReport 形式にも対応。
|
Scientific Clinical Reporting
臨床データから構造化レポートを自動生成するパイプラインを提供する。
When to Use
- 検査結果を SOAP ノート形式でまとめるとき
- バイオマーカープロファイルレポートを作成するとき
- ファーマコゲノミクスレポート (CPIC ガイドライン準拠) が必要なとき
- 臨床試験の CSR (Clinical Study Report) サマリーを生成するとき
- HL7 FHIR DiagnosticReport 形式で出力するとき
Quick Start
1. SOAP ノート生成
import json
from datetime import datetime
def generate_soap_note(patient_data, findings, assessment, plan):
"""
SOAP ノート形式の臨床レポートを生成。
Parameters:
patient_data: dict — {"id": "...", "age": 45, "sex": "M", ...}
findings: dict — {"subjective": [...], "objective": [...]}
assessment: list — 評価・診断リスト
plan: list — 治療計画リスト
"""
soap = {
"report_type": "SOAP_Note",
"generated_at": datetime.now().isoformat(),
"patient": {
"id": patient_data.get("id", "ANON"),
"age": patient_data.get("age"),
"sex": patient_data.get("sex"),
},
"S": {
"chief_complaint": findings.get("chief_complaint", ""),
"history": findings.get("subjective", []),
},
"O": {
"vitals": findings.get("vitals", {}),
"lab_results": findings.get("lab_results", []),
"imaging": findings.get("imaging", []),
"physical_exam": findings.get("objective", []),
},
"A": {
"diagnoses": assessment,
"differential": findings.get("differential", []),
},
"P": {
"treatment": plan,
"follow_up": findings.get("follow_up", ""),
"referrals": findings.get("referrals", []),
},
}
print(f"SOAP note: patient={soap['patient']['id']}, "
f"diagnoses={len(assessment)}, plans={len(plan)}")
return soap
2. バイオマーカープロファイルレポート
import pandas as pd
def biomarker_profile_report(biomarkers_df, reference_ranges=None):
"""
バイオマーカープロファイルレポート生成。
Parameters:
biomarkers_df: DataFrame — columns: [marker, value, unit, specimen]
reference_ranges: dict — {"marker": {"low": x, "high": y, "unit": "..."}}
"""
if reference_ranges is None:
reference_ranges = {
"CEA": {"low": 0, "high": 5.0, "unit": "ng/mL"},
"AFP": {"low": 0, "high": 10.0, "unit": "ng/mL"},
"CA19-9": {"low": 0, "high": 37.0, "unit": "U/mL"},
"CA125": {"low": 0, "high": 35.0, "unit": "U/mL"},
"PSA": {"low": 0, "high": 4.0, "unit": "ng/mL"},
"HER2": {"low": 0, "high": 1, "unit": "IHC score"},
: {: , : , : },
: {: , : , : },
}
results = []
_, row biomarkers_df.iterrows():
marker = row[]
value = (row[])
ref = reference_ranges.get(marker, {})
status =
ref:
value > ref.get(, ()):
status =
value < ref.get(, ()):
status =
results.append({
: marker,
: value,
: row.get(, ref.get(, )),
: ,
: status,
})
report_df = pd.DataFrame(results)
abnormal = report_df[report_df[] != ]
report = {
: ,
: (report_df),
: (abnormal),
: report_df.to_dict(),
: (
(abnormal) >
),
}
(
)
report
3. ファーマコゲノミクスレポート
def pharmacogenomics_report(genotypes, medications):
"""
CPIC ガイドライン準拠のファーマコゲノミクスレポート。
Parameters:
genotypes: dict — {"CYP2D6": "*1/*4", "CYP2C19": "*1/*2", ...}
medications: list — ["codeine", "clopidogrel", ...]
"""
cpic_phenotypes = {
"CYP2D6": {
"*1/*1": "Normal Metabolizer",
"*1/*4": "Intermediate Metabolizer",
"*4/*4": "Poor Metabolizer",
"*1/*2xN": "Ultrarapid Metabolizer",
},
"CYP2C19": {
"*1/*1": "Normal Metabolizer",
"*1/*2": "Intermediate Metabolizer",
"*2/*2": "Poor Metabolizer",
"*1/*17": "Rapid Metabolizer",
"*17/*17": "Ultrarapid Metabolizer",
},
}
drug_gene_map = {
"codeine": {"gene": "CYP2D6", "action": {
"Poor Metabolizer": "AVOID — use alternative analgesic",
"Ultrarapid Metabolizer": "AVOID — toxicity risk",
"Intermediate Metabolizer": "Use with caution, consider alternative",
}},
"clopidogrel": {"gene": "CYP2C19", "action": {
"Poor Metabolizer": "Use alternative antiplatelet (e.g., prasugrel)",
: ,
}},
}
recommendations = []
drug medications:
entry = drug_gene_map.get(drug, {})
gene = entry.get(, )
genotype = genotypes.get(gene, )
phenotype_map = cpic_phenotypes.get(gene, {})
phenotype = phenotype_map.get(genotype, )
action = entry.get(, {}).get(phenotype, )
recommendations.append({
: drug,
: gene,
: genotype,
: phenotype,
: action,
: drug drug_gene_map ,
})
report = {
: ,
: genotypes,
: medications,
: recommendations,
}
(
)
report
4. 構造化レポート出力 (LaTeX/HTML)
def export_clinical_report(report, output_format="html",
output_path="reports/clinical_report"):
"""
臨床レポートを LaTeX/HTML/FHIR JSON 形式で出力。
Parameters:
report: dict — SOAP, Biomarker, PGx レポート
output_format: "html", "latex", "fhir_json"
output_path: str — 出力先パス (拡張子なし)
"""
import os
os.makedirs(os.path.dirname(output_path), exist_ok=True)
report_type = report.get("report_type", "Clinical")
if output_format == "html":
filepath = f"{output_path}.html"
html_parts = [
"<!DOCTYPE html><html><head>",
f"<title>{report_type} Report</title>",
"<style>body{font-family:Arial;margin:2em;}"
"table{border-collapse:collapse;width:100%;}"
"td,th{border:1px solid #ddd;padding:8px;}</style>",
"</head><body>",
f"<h1>{report_type} Report</h1>",
]
if report_type == "SOAP_Note":
for section in ["S", "O", "A", "P"]:
html_parts.append(f"<h2>{section}</h2>")
html_parts.append(f"<pre>{json.dumps(report.get(section, {}), indent=2, ensure_ascii=False)}</pre>")
elif report_type == "Biomarker_Profile":
html_parts.append("<table><tr><th>Marker</th><th>Value</th>"
"<th>Reference</th><th>Status</th></tr>")
r report.get(, []):
status_color = r[] !=
html_parts.append(
)
html_parts.append()
html_parts.append()
(filepath, ) f:
f.write(.join(html_parts))
output_format == :
filepath =
fhir = {
: ,
: ,
: [{: [{: ,
: }]}],
: {: report_type},
: report.get(, datetime.now().isoformat()),
: [],
}
(filepath, ) f:
json.dump(fhir, f, indent=)
output_format == :
filepath =
(filepath, ) f:
f.write()
f.write()
f.write()
f.write()
f.write()
()
filepath
References
Output Files
| ファイル | 形式 |
|---|
reports/soap_note.json | JSON |
reports/biomarker_profile.json | JSON |
reports/pgx_report.json | JSON |
reports/clinical_report.html | HTML |
reports/clinical_report.tex | LaTeX |
reports/clinical_report.fhir.json | FHIR JSON |
利用可能ツール
本スキルは ToolUniverse ツールに直接依存しない。
参照スキル
| スキル | 関連 |
|---|
scientific-variant-interpretation | バリアント解釈レポート |
scientific-variant-effect-prediction | バリアント病原性スコア |
scientific-pharmacogenomics | PGx ガイドライン |
scientific-precision-oncology | 精密腫瘍学レポート |
scientific-disease-research | 疾患情報統合 |
依存パッケージ
pandas, json (stdlib), datetime (stdlib)
