Project Overview

Build a real-time credit risk assessment platform for small and micro enterprise (SME) operating loans to address lagging scorecards, slow approvals, coarse-grained risk strategies, and rising bad debt. The platform will fuse multi-source data (transaction flows, tax invoices, operating accounts, third-party bureau, and behavioral signals), deliver millisecond-level online scoring and large-scale batch approvals, and support PD/LGD modeling and fraud detection with explainability and full compliance traceability. Target performance: PD AUC ≥ 0.82, delinquency (e.g., 30/60 DPD) recall ≥ 85% at controlled precision, and production-grade A/B testing for risk strategies and rapid product onboarding.

Problem Analysis

• Business context

  • Customer segment: SMEs seeking operating loans with diverse cashflow and documentation quality.
  • Current issues:
    • Scorecards are outdated and static, lagging behind recent macro and sector shifts.
    • Manual/slow approval processes lead to long TAT and lost conversions.
    • Coarse risk strategies cause inefficient capital allocation and higher NPL/charge-off.
    • Lack of real-time signals exposes the bank to first-pay default and bust-out fraud.
  • Target outcomes:
    • Real-time decisioning for instant approvals/limits/pricing.
    • Better risk discrimination (AUC ≥ 0.82) and high delinquency recall (≥ 85%) while controlling FPR.
    • Explainability and auditability to satisfy internal validation and external regulators.
    • Data governance: classification, lineage, consent, and immutable decision logs.
    • Experimentation: champion–challenger strategies and controlled A/B testing.

• Technical characteristics

  • Heterogeneous data with variable latency and quality; requires schema normalization, entity resolution, and late-arriving corrections handling.
  • Time-dependent risk: features must be point-in-time correct; leakage prevention is mandatory.
  • Low-latency online scoring: p99 single to low-double-digit milliseconds requires precomputation, caching, and compact model artifacts.
  • Stable production ML: automated drift/quality monitoring, model registry, and safe rollout required.
  • Regulatory alignment: IFRS 9/Basel for PD/LGD/ECL, local privacy/data security (e.g., PIPL), and model risk management.

Solution

Architecture overview

• Ingestion and storage

  • Batch and streaming ingestion from:
    • Bank transaction flows and operating accounts (core banking, payment gateways).
    • Tax invoice data (e.g., VAT fapiao) via secure connectors.
    • Third-party bureau data via API.
    • Behavioral/telemetry (online banking/app interactions, device and network metadata where consented).
  • Message bus: Kafka (or Pulsar) for streaming; SFTP/HTTPS for scheduled batch.
  • Data lake/warehouse: HDFS/S3-compatible object store + columnar warehouse (Parquet + Hive/Trino or cloud DW).
  • Metadata and lineage: data catalog (e.g., Apache Atlas) with dataset versioning.

• Feature platform

  • Offline feature store for training (e.g., Feast + lake/warehouse).
  • Online feature store for serving (Redis/KeyDB or low-latency KV store).
  • Point-in-time feature computation using backfill jobs (Spark/Flink) to prevent leakage.

• Modeling

  • PD: Gradient-boosted trees (XGBoost/LightGBM) as primary; calibrated logistic regression challenger/scorecard for governance.
  • LGD: Regression (Elastic Net or LightGBM with monotonic/shape constraints), downturn and segmentation by product/collateral.
  • Fraud: Hybrid approach:
    • Supervised classifier for first-pay default/early delinquency.
    • Unsupervised anomaly detection (Isolation Forest) on velocity and consistency features.
    • Rules engine for hard controls (KYC, blacklist, out-of-bound signals).
  • Calibration and stability: Platt/Isotonic calibration, population stability monitoring.

• Serving and decisioning

  • Real-time scoring microservice (Java/Spring Boot or Python/FastAPI with ONNX Runtime/treelite) with warm-loaded models, per-request SLA p99 ≤ 20–30 ms including feature fetch if precomputed.
  • Batch scoring pipeline (Spark) for nightly/ intraday mass re-scoring and campaigns.
  • Decision engine (Drools or similar) for strategy orchestration: cutoffs, pricing, limits, policy overrides, and A/B experiments.

• Explainability and governance

  • Model explainability: SHAP for feature contribution; reason codes for adverse actions.
  • Rules traceability: versioned rule sets with lineage and change history.
  • Model registry and lineage (MLflow): versions, approvals, staged deployment (Dev/Staging/Prod).
  • Audit trail: immutable, event-sourced decision logs stored in WORM-compliant storage.

• Security, privacy, and compliance

  • Data classification tiers (L1 sensitive PII, L2 confidential financial, L3 operational).
  • Encryption in transit (mTLS) and at rest (KMS-managed).
  • Access control: RBAC/ABAC with just-in-time access; fine-grained data masking and tokenization for PII.
  • Consent and purpose limitation; data retention aligned with regulation.
  • Model risk management: documentation, validation reports, backtesting, performance monitoring.

Data layer and feature engineering

• Entity resolution

  • Deterministic and probabilistic matching for SMEs across tax IDs, legal rep IDs, and account IDs with hashed identifiers.
  • UID generation for borrowers/enterprises to join cross-system records.

• Feature families (examples)

  • Bank transactions and operating accounts:
    • Monthly revenue, cash-in/out, volatility (CV), seasonality indices, consecutive declines, inflow–outflow mismatch.
    • Counterparty diversification (HHI), top-5 counterparties concentration, receivable/payable turnover.
    • Balance sufficiency ratio before/after loan disbursement.
  • Tax invoices:
    • Invoice amounts by category, month-on-month YoY growth, tax compliance flags, invoice authenticity checks.
    • Sales–tax consistency with bank inflows (coherence features).
  • Third-party bureau:
    • Inquiry velocity, existing credit lines and utilization, delinquencies, public records.
  • Behavioral:
    • Application session patterns, form edit velocity, time-to-submit, device–account consistency, IP geolocation consistency.
  • Aggregation windows: 7/30/90/180/365-day rolling stats; event-based features (e.g., large single-day spikes).
  • Leakage prevention: all features computed as-of decision timestamp using point-in-time joins.

• Feature quality and governance

  • Data validation (Great Expectations) with schema, range, and drift checks at ingestion and before training.
  • Feature documentation with owner, transformation, and validation rules in data catalog.

Modeling design

• PD model

  • Algorithms: LightGBM/XGBoost with monotonic constraints for key risk drivers; logistic regression benchmark/scorecard for challenger.
  • Sampling and class imbalance: time-based stratified splits; cost-sensitive loss / focal loss alternative; or class weights.
  • Calibration: Isotonic or Platt scaling; monitored with Brier score and calibration curves.
  • Stability: PSI monitoring by segment (industry, region, channel).
  • Segmentation: optional industry/vintage segmentation if materially improves AUC and calibration.

• LGD model

  • Dependent variable: realized LGD on charged-off or cured accounts; include discounting and recoveries.
  • Modeling: Elastic Net or LightGBM regression with constraints; separate downturn LGD overlay by macro factors.
  • Governance: align with IFRS 9 methodology; scenario-based ECL estimation ECL = PD × LGD × EAD (EAD can be rule-based or modeled if revolving).

• Fraud detection

  • Supervised: gradient boosting model trained on confirmed fraud/chargeback/first-payment-default labels with short horizon (e.g., 30 days).
  • Unsupervised: Isolation Forest / robust z-scores for velocity anomalies (applications per device/ID, counterparty spikes, IP–address churn).
  • Rules engine: KYC fails, blacklist/whitelist, extreme threshold blockers, document mismatch.
  • Case management integration for investigator workflows.

• Cutoff and policy optimization

  • Use cost-sensitive thresholding optimizing expected profit = revenue − expected credit loss − OPEX − fraud loss.
  • Multi-objective frontiers for approval rate vs. ECL vs. capital usage; scenario analysis by macro conditions.

• Validation and backtesting

  • Time-series CV (rolling windows) to respect temporal dependencies.
  • Holdout by application month; backtest stability across vintages and industries.
  • Metrics: AUC, KS, PR-AUC, recall at policy thresholds, Brier score, calibration slope/intercept, ECE.
  • Governance package: model card, validation report, and challenger comparison.

Serving and decision orchestration

• Real-time API

  • gRPC/REST endpoints: /score/pd, /score/lgd, /decision/credit with idempotency keys.
  • Latency targets (on-net): median ≤ 5–10 ms, p95 ≤ 15–20 ms, p99 ≤ 30 ms given precomputed online features.
  • Availability SLO: ≥ 99.95%.
  • Caching: online feature store (Redis); short TTL and cache warming for top features.

• Batch scoring

  • Spark jobs running hourly/daily for re-scoring portfolio and campaigns; throughput 10–50M records/night depending on cluster.
  • Results written to warehouse and decision store; downstream to CRM/collections.

• Decision engine and A/B testing

  • Strategy graph: eligibility → fraud controls → PD/LGD scoring → cutoff/pricing/limit → manual review routing.
  • Champion–challenger and A/B testing:
    • Randomization unit: application or enterprise; stratified by risk band and channel.
    • Guardrails: automatic stop if NPL proxy exceeds threshold; continuous monitoring dashboards.
    • Statistical evaluation: sequential tests with minimum sample and effect size; pre-registered KPIs.

• Explainability and adverse action

  • SHAP values per decision; top K feature contributions returned to decision service.
  • Reason codes mapped from features/rules for adverse action notices.
  • Rule lineage: rule ID, version, author, change ticket, and test evidence embedded in decision log.

MLOps, monitoring, and auditability

• CI/CD

  • Git-based workflows; infrastructure as code (Terraform/Helm).
  • Model pipeline: feature tests → training → validation gates → bias/calibration checks → registry → canary deploy.

• Registry and lineage

  • MLflow Model Registry with approvals and metadata (training data snapshot, code commit, environment).
  • Data and model lineage tracked in catalog; reproducibility with environment pinning.

• Observability

  • Online metrics: latency, error rates, throughput, feature fetch misses.
  • Model metrics: population drift (PSI), feature drift, prediction drift, calibration tracking, fraud capture vs. false positives.
  • Alerting via Prometheus/Grafana; anomaly alerts to on-call.

• Decision logging

  • Event-sourced immutable logs: request payload hash, features used, model version, score, decision, explanations, rule versions, operator actions.
  • WORM storage retention per policy (e.g., ≥ 7 years).

Security and compliance

• Data classification and handling
  • L1 Sensitive: PII, taxpayer IDs, device IDs (tokenized/masked in non-prod).
  • L2 Confidential: financial and bureau data.
  • L3 Operational: metadata, logs (no raw PII).
• Access control and encryption
  • mTLS, TLS 1.2+; KMS-managed keys; RBAC/ABAC with least privilege; PAM for break-glass access.
• Privacy and consent
  • Consent capture and purpose binding; opt-out flows; data minimization; retention and deletion SLAs.
• Regulatory alignment
  • Credit risk: IFRS 9/Basel for PD/LGD/ECL.
  • Local data/privacy regulations (e.g., PIPL) and regulator guidelines (e.g., CBIRC/PBoC equivalents).
  • Model risk management: independent validation and periodic review.

Technology stack (reference, adjustable to enterprise standards)

• Data: Kafka, Spark/Flink, Object Store (S3-compatible), Trino/Hive, PostgreSQL.
• Feature store: Feast (offline on parquet; online on Redis).
• Modeling: Python, scikit-learn, XGBoost/LightGBM, SHAP, MLflow, ONNX Runtime/treelite.
• Serving: Spring Boot or FastAPI, Redis, gRPC/REST, Kubernetes.
• Rules/decisioning: Drools or enterprise rules engine; internal policy service.
• Observability: Prometheus, Grafana; data validation with Great Expectations.
• Security: Vault/KMS, mTLS, IAM integrated with enterprise SSO.

Implementation Plan

• Phase 0 – Discovery and design (2–3 weeks)

  • Deliverables: Business/credit policy review, data contracts, target KPIs, architecture HLD, compliance plan.
  • Resources: Solution architect, risk lead, data engineer, compliance officer.

• Phase 1 – Data foundation and governance (4–6 weeks)

  • Build ingestion pipelines (Kafka/batch), entity resolution, data catalog, data quality checks, PII masking/tokenization.
  • Deliverables: Curated base layers (bronze/silver), quality dashboards, data classification matrix.

• Phase 2 – Feature store and baseline models (6–8 weeks)

  • Implement offline/online feature store; define feature views with point-in-time correctness.
  • Train baseline PD and fraud models; initial LGD data prep and prototype.
  • Deliverables: Feature library v1, PD v1 (AUC target validation), fraud v1, validation report, model registry entries.

• Phase 3 – Real-time scoring and batch pipelines (4–6 weeks)

  • Deploy scoring microservices, online feature store, and batch scoring on Spark.
  • Integrate with decision engine; implement explainability APIs and reason codes.
  • Deliverables: Production-ready APIs with p99 latency certification, batch scoring jobs, decision logs.

• Phase 4 – Strategy, A/B testing, and compliance (3–4 weeks)

  • Implement champion–challenger strategies, traffic allocation, and guardrails.
  • Complete audit trail, model documentation, and access controls.
  • Deliverables: Strategy experiments live, audit-compliant decision log, adverse action workflow.

• Phase 5 – LGD model and ECL integration (4–6 weeks)

  • Finalize LGD model with downturn overlay; optional EAD enhancement if needed.
  • Integrate PD/LGD into ECL calculators and risk reporting.
  • Deliverables: LGD v1, ECL reports, governance approvals.

• Phase 6 – Hardening and scale-out (2–4 weeks)

  • Load/performance testing (e.g., 2k–5k RPS), chaos testing, canary/rollback, runbooks.
  • Deliverables: SRE playbooks, SLO dashboards, DR plan, go-live checklist.

• Ongoing – Monitoring and model lifecycle (continuous)

  • Drift monitoring, scheduled re-training (e.g., monthly/quarterly), post-deployment validation, compliance reviews.

• Team and roles

  • PM, solution architect, data engineers (2–3), ML engineers (2), data scientist/risk modelers (3–4), platform/DevOps engineers (2), fraud analyst (1–2), QA (1–2), compliance/security (1–2).

Risk Assessment

• Data acquisition delays or quality issues

  • Mitigation: phased onboarding by source, strong DQ contracts, backfill plan, automated validation with SLAs.

• Label leakage and temporal bias

  • Mitigation: strict point-in-time feature computation, time-based CV, independent validation review.

• Concept drift and macro shocks

  • Mitigation: drift detectors, alert thresholds, retraining triggers, fallback scorecards/rules, stress testing.

• Latency SLO breach under peak load

  • Mitigation: precompute features, Redis hot sets, autoscaling, circuit breakers, backpressure with graceful degradation to cached or rules-only decisions.

• Regulatory/model validation hurdles

  • Mitigation: early engagement with validation, comprehensive model documentation, challenger models, reason codes, and calibration evidence.

• Integration risks with core systems

  • Mitigation: well-defined APIs, sandbox/staging environments, contract testing, canary releases.

• Security and privacy incidents

  • Mitigation: encryption everywhere, RBAC/ABAC, tokenization, regular audits, DLP scanning, secret rotation.

• Experimentation risk (A/B causing performance dip)

  • Mitigation: small initial traffic, strict guardrails, sequential monitoring, fast rollback.

Expected Outcomes

• Technical KPIs

  • PD model AUC ≥ 0.82 across validation vintages; KS improvement vs. legacy scorecard by ≥ 10 points.
  • Delinquency prediction recall ≥ 85% at policy operating point with precision constraints set by risk appetite.
  • Real-time scoring latency p99 ≤ 30 ms; availability ≥ 99.95%.
  • Fraud model captures ≥ 70% of confirmed fraud at ≤ 3% false positive rate (tunable).
  • Calibration Brier score improvement ≥ 15% vs. legacy; PSI within stable bounds after deployment.

• Business impact (indicative, to be refined with baseline data)

  • Approval turnaround: instant decisions for ≥ 70% of clean applications; overall TAT reduction by 60–80%.
  • Risk-adjusted margin: expected credit loss reduction by 10–20% through better discrimination and pricing/limit optimization.
  • NPL/early delinquency: relative reduction by 10–15% over 6–12 months, subject to macro conditions.
  • Fraud loss: reduction by 20–30% in early-stage fraud through hybrid controls.
  • Operational efficiency: manual reviews reduced by 30–50% with better triage; faster product launches (new strategy in days, not weeks).

• Compliance and governance

  • Full decision auditability with feature contributions and rule lineage.
  • Data classification and retention aligned with policy and regulation.
  • Model risk management lifecycle in place with periodic reviews and challenger rotation.

This proposal delivers a production-grade, compliant, and explainable real-time credit risk platform for SME lending, aligning advanced ML with robust operations to achieve measurable gains in risk control, speed, and profitability.

AI解决方案项目提案（中日双语）

三甲医院内科30天再入院预测与床位调度联动方案

1. 项目概述 / プロジェクト概要

• 中文（CN）

  • 背景：某三甲医院内科面临再入院率偏高与床位周转紧张的问题，造成医疗质量与运营效率双重压力。
  • 目标：基于EMR、检验、用药、护理评估与影像报告，构建“30天再入院预测 + 风险分层 + 早期干预 + 床位调度联动”的一体化方案，支持院内多科室协同与合规审计。核心技术目标为：召回率≥90%（优先保障召回）、提前预警窗口≥72小时。
  • 范围：数据集成与治理、特征工程与可解释建模、HIS/LIS/EMR内预警清单推送、干预建议库对接、床位容量预测与调度优化、全程安全合规与访问审计、可视化协作看板。

• 日文（JP）

  • 背景：三次医療機関の内科において、再入院率の上昇と病床回転率の逼迫が品質・運営双方に影響。
  • 目的：EMR・検査・投薬・看護評価・画像レポートを活用し、「30日再入院予測＋リスク層別化＋早期介入＋病床調整連動」を実現。院内多部門の協働と監査対応を支援。技術目標は再入院召回率≥90%（再現率重視）、72時間以上の早期予警。
  • 範囲：データ統合・ガバナンス、特徴量設計と説明可能モデル、HIS/LIS/EMRへのアラート配信、介入提案、病床需要予測と割当最適化、セキュリティ・監査、コラボダッシュボード。

2. 问题分析 / 課題分析

• 中文（CN）

  • 核心痛点
    • 高再入院患者识别滞后，未形成入院—住院—出院全流程的动态风险管理。
    • 数据分散（HIS/EMR/LIS/PACS/护理），信息缺失与时效性不足，难以支撑72小时以上提前预警。
    • 床位调度依赖人工经验，缺少对入出院波动与LOS（住院日）的量化预测，影响周转效率。
    • 缺乏可干预因素的结构化标注（多病共存、用药依从性、功能状态、出院安置）。
  • 技术特性
    • 多模态异构数据：结构化（检验、用药）、半结构化（护理评估）、文本（影像/病程/出院小结）。
    • 类别极度不平衡（再入院为小概率），更需要高召回和阈值策略。
    • 时序性强：近12个月就诊史、近7天生命体征与检验动态。
    • 对可解释性、合规与审计可追溯性要求高。
  • 业务影响
    • 再入院率影响医疗质量评价与成本；床位周转影响手术/收治能力与患者等待。
    • 目标：在不增加临床负担的前提下，用高召回+早期预警驱动流程内前移干预。

• 日文（JP）

  • 主要課題
    • 高リスク患者の特定が遅延し、入院〜在院〜退院の全過程での動的リスク管理が未整備。
    • データが分散し、欠測・遅延があり、72時間以上の早期予警が困難。
    • 病床調整が属人的で、入退院変動・在院日（LOS）の定量予測が不足。
    • 介入可能因子（多疾患併存、服薬アドヒアランス、機能状態、退院先）の構造化が弱い。
  • 技術的特徴
    • マルチモーダル・非一様データ（構造化・半構造化・テキスト）。
    • 極端な不均衡データで再現率重視のしきい値設計が必須。
    • 強い時系列性（直近12カ月の受療履歴、直近7日のバイタル/検査推移）。
    • 説明可能性・法令遵守・監査の要件が高い。
  • 業務影響
    • 再入院は品質・コストに直結、病床回転は収容能力と待機時間に影響。
    • 高再現率＋早期警告で、負担増を避けつつ介入を前倒し。

3. 解决方案 / ソリューション

• 中文（CN）

  1. 总体架构

     • 数据层：对接HIS/EMR/LIS/PACS/护理系统，优先采用院内总线（HL7 v2 ADT/ORU 或 FHIR 资源，如Patient/Encounter/Observation/MedicationRequest），每日批处理+关键节点近实时（入院、转科、预出院）。
     • 治理与特征层：数据湖/湖仓（如PostgreSQL/ClickHouse + 对象存储），特征库（Feast或自研），数据质量规则与血缘审计。
     • 算法层：再入院预测、LOS预测、床位需求预测、调度优化（MIP/OR-Tools）。
     • 应用层：HIS/EMR工作清单推送、护理/医务协同看板、个案管理工作台、审计与监控面板。
     • 运维与安全：院内私有化部署，容器化（Docker/K8s），RBAC/ABAC，TLS加密，审计日志。

  2. 特征工程与可解释建模

     • 核心特征
       • 人口学与既往：年龄、性别、既往12月入院/急诊次数、Charlson/CCI、多病共存计数。
       • 检验/生命体征：异常标志（超阈）、波动幅度、近期最差值、缺失指示器。
       • 用药：多重用药计数、重点药物类（心衰/呼吸/抗凝/精神药物等）、换药/加减药事件。
       • 护理评估：功能状态量表（如Barthel简化项）、跌倒/压疮风险、饮食与吞咽、护理依从性记录。
       • 影像/病历文本：出院小结与影像报告关键词/实体映射（ICD-10、LOINC、SNOMED同义库），采用词典+TF-IDF/轻量词向量，初期不依赖大模型；后续可评估经验证的医学BERT微调以提升文本召回。
       • 出院相关：出院去向、是否居家支持、复诊安排是否完成、院内转科次数。
     • 模型组合
       • 基线：逻辑回归（良好可解释性与校准）。
       • 主模型：梯度提升树（LightGBM/XGBoost/CatBoost，处理缺失与非线性效果佳）。
       • 文本子模型：TF-IDF + 线性模型/树模型；可选小型医学BERT经内网推理加速（经院内评估后逐步引入）。
       • 集成策略：stacking/加权平均，侧重召回；使用校准（Platt/Isotonic）。
     • 可解释性与干预提示
       • 使用SHAP/特征重要性生成患者级风险因子Top-N，自动标注可干预项（如多病共存≥3、关键药物依从性差、近期CRP持续升高）。
       • 干预建议库（与医务、护理、药学共建）：用药核对与依从性教育、48-72小时复诊预约、电话随访、营养/康复转介、出院前强化评估。

  3. 召回≥90%与≥72小时提前期的实现策略

     • 多时点评分：入院24h内、在院每日、预出院T-3/T-2/T-1天滚动预测，确保≥72h窗口。
     • 阈值策略：以召回为首要优化目标，设定“高风险工作清单”为Top-K（按科室容量动态调参），并对科室级召回进行监控。
     • 代价敏感学习：提高FN代价或使用class_weight，结合AUPRC与召回-告警量曲线调优。
     • 人机协同：个案管理员可反馈误报/漏报，在线优化阈值与特征质量。

  4. 床位调度联动

     • 需求预测：按科室/病区的7日入院与出院流量预测（SARIMAX/Prophet），叠加节假日与门急诊高峰特征。
     • LOS预测：入科时与在院第2-3天分别给出LOS更新（树模型）。
     • 优化分配：基于约束的整数规划（OR-Tools）进行床位分配建议，约束包含性别/隔离/专科病区/护理等级/手术日程/转科优先级。
     • 联动逻辑：高风险患者优先分配“易获取干预资源”的床位（近护士站、心电监护可用等）、平衡转运成本。

  5. 集成与可视化

     • 系统集成：通过院内ESB/接口引擎写回HIS/EMR/LIS工作清单，支持单点登录与细粒度权限。
     • 多科室协作看板：科室-病区视图（在院人数、预计出院、预计入院、床位占用、再入院高风险清单与干预完成率），指挥中心汇总视图。
     • 监控与MLOps：MLflow模型版本、特征漂移监控、告警量与干预完成闭环跟踪。

  6. 合规与安全

     • 数据最小够用原则、去标识化/假名化（患者标识以院内哈希映射）、传输与存储加密。
     • 访问控制与全量审计（操作人、目的、时间、字段级轨迹），对敏感字段设置脱敏视图。
     • 符合相关法律法规与院内数据治理规范，在院内私有环境部署。

• 日文（JP）

  1. 全体アーキテクチャ

     • データ層：HIS/EMR/LIS/PACS/看護と連携、院内ESB（HL7 v2またはFHIR）で日次バッチ＋重要イベント準リアルタイム。
     • ガバナンス/特徴層：データレイク/レイクハウス、特徴ストア、DQルール・データ血縁・監査。
     • アルゴリズム層：再入院予測、LOS予測、需要予測、割当最適化（MIP/OR-Tools）。
     • アプリ層：HIS/EMRアラート、看護・医務ダッシュボード、ケース管理、監査画面。
     • 運用/セキュリティ：オンプレ、コンテナ化、RBAC/ABAC、TLS、監査ログ。

  2. 特徴量と説明可能モデル

     • 主要特徴：人口統計、既往入院/救急、CCI、多剤併用、検査異常と変動、看護評価（機能/転倒等）、文書（キーワード・用語マッピング）、退院関連（行先・受診予約など）。
     • モデル：ロジスティック回帰（基線）＋GBDT（LightGBM/XGBoost/CatBoost）＋テキスト（TF-IDF＋線形/木）。必要に応じ医学BERTを段階導入。
     • 説明性：SHAPで個別要因Top-Nを提示、介入提案ライブラリと連携。

  3. 召回≥90%・72h以上の早期予警

     • 複数時点スコアリング、Top-K運用（科別動的調整）、コスト感度学習、人手フィードバックで閾値最適化。

  4. 病床調整

     • 7日需要予測（SARIMAX/Prophet）、LOS予測、制約付き整数計画（性別/隔離/専門/看護区分/手術/転科）。高リスク患者には介入資源アクセス性を考慮。

  5. 連携/可視化

     • ESB経由でHIS/EMRに書戻し、SSO＋権限。病棟別ダッシュボードと司令塔ビュー、MLOpsと性能監視。

  6. 法令遵守/セキュリティ

     • 最小化・仮名化・暗号化、権限管理・監査ログ、院内ガイドライン順守。

4. 实施计划 / 実施計画

• 中文（CN）

  • 阶段1（第0-1月）：需求梳理与数据摸底
    • 交付物：数据字典与映射关系、质量评估报告、目标指标定义（再入院口径、召回/提前期计算方法）。
  • 阶段2（第2-3月）：数据集成与特征库搭建
    • 交付物：稳定数据管道、特征仓样表、数据质量仪表板、基础权限与审计。
  • 阶段3（第3-4月）：模型开发与离线验证
    • 交付物：基线与主模型、AUPRC/召回/校准报告、可解释性与干预建议库V1。
  • 阶段4（第5-6月）：小范围试点（内科2个病区）
    • 交付物：HIS/EMR推送集成、协作看板、阈值与Top-K策略、临床可用性评估。
  • 阶段5（第7-9月）：全院内科推广与床位优化联动
    • 交付物：需求预测+调度优化上线、司令塔总览、月度性能审计。
  • 阶段6（第10-12月）：优化与运维移交
    • 交付物：模型更新策略、漂移监控、SOP与培训、效果评估报告。
  • 人员与资源
    • 团队：项目经理1、数据工程2、ML工程2、临床信息化1、算法工程（优化）1、安全合规1、业务分析1。
    • 基础设施：中小规模CPU集群（32-64核×2台，256GB RAM×2）、存储≥10TB、可选1张GPU用于文本微调（后期）；K8s/CI/CD/MLflow/Feast/OR-Tools。

• 日文（JP）

  • フェーズ1（0-1ヶ月）：要件整理・データ調査
  • フェーズ2（2-3ヶ月）：統合パイプライン・特徴ストア構築
  • フェーズ3（3-4ヶ月）：モデル開発・オフライン検証
  • フェーズ4（5-6ヶ月）：限定パイロット（内科2病棟）
  • フェーズ5（7-9ヶ月）：内科全体展開＋病床最適化連動
  • フェーズ6（10-12ヶ月）：最適化・運用移管
  • 体制/リソース：PM、DE×2、MLE×2、臨床情報×1、最適化エンジニア×1、セキュリティ×1、BA×1。CPU中心、必要に応じGPU少数。

5. 风险评估 / リスク評価

• 中文（CN）

  • 数据质量与口径不一致：通过数据剖析、标准编码映射（ICD-10/LOINC等）、DQ规则与异常告警缓解。
  • 类不平衡与阈值设置导致告警过多：采用Top-K与科室配额、召回-工作量曲线共识化配置，分阶段调优。
  • 概念漂移与季节性：上线后每月稳定性与再校准评估，触发再训练阈值与回滚预案。
  • 临床采纳与流程变更：与医务/护理共创干预路径，先试点后推广，设置“必看”但不过度打扰的工作清单。
  • 优化方案误配：调度优化保留人工最终裁量与硬约束优先，提供“为何建议”的规则解释。
  • 合规与安全：最小权限、细粒度审计、渗透测试与应急响应演练。
  • 运维风险：建立SLA（数据延迟、模型可用性）、备份与容灾（RPO/RTO）。

• 日文（JP）

  • データ品質・コード体系差異：プロファイリング、標準コード対応、DQ監視。
  • 不均衡・アラート過多：Top-K/科別配分、再現率–作業量の合意設計。
  • ドリフト/季節性：月次安定性評価、再学習トリガー、ロールバック計画。
  • 臨床受容性：共同設計、パイロット優先、通知は作業リスト中心で過度な割込回避。
  • 最適化の誤配：人間の最終判断を確保、制約優先、説明表示。
  • コンプライアンス/セキュリティ：最小権限・監査・脆弱性診断・BCP。
  • 運用：SLA、バックアップ/DR。

6. 预期成果 / 期待される成果

• 中文（CN）

  • 技术指标（试点目标，实际以院内评估为准）
    • 30天再入院预测：召回率≥90%（以高召回为优化目标），AUPRC显著优于基线，校准误差低。
    • 提前预警：≥72小时覆盖率≥90%（多时点滚动评分保障）。
    • 床位管理：7日需求预测MAPE控制在15-25%区间（按科差异化），LOS预测R2/MAE达到可运营使用标准。
  • 业务价值（3-6个月内的可观测目标）
    • 高风险患者清单驱动的个案管理覆盖率提升至≥85%。
    • 内科再入院率相对下降5-10%（随干预强度而变，采用分阶段对照评估）。
    • 床位周转效率提升：出院预测准确度提升带来待床时间下降、择期手术取消率下降。
    • 护理与医务协作效率提升：晨会与指挥中心看板形成标准化闭环。
  • 衡量与治理
    • 建立对照组/对照病区，前后对比与分层（年龄、诊断组）。
    • 每月性能报告与干预完成率、再入院归因分析，持续改进。

• 日文（JP）

  • 技術KPI（パイロット目標）
    • 30日再入院予測：再現率≥90%を優先、AUPRC向上、良好な校正。
    • 早期予警：72時間以上の予測カバレッジ≥90%。
    • 病床：7日需要予測MAPE 15-25%、LOS予測は運用許容水準。
  • 業務効果（3-6ヶ月）
    • ケース管理カバレッジ≥85%、再入院率5-10%相対低減（段階評価）。
    • 病床回転改善、待機短縮、計画手術キャンセル低減。
    • 多職種協働の標準化ダッシュボード運用。
  • 評価/ガバナンス
    • 対照病棟の設定、月次レポート、介入達成率と要因分析で継続改善。

备注 / 補足

• 本方案坚持可行性与临床可采纳性优先，避免过度依赖未充分验证的新技术；文本处理优先简洁稳健方法，后续在院内安全边界内迭代升级。
• 召回率与提前期为优化目标并通过阈值与流程保障，但实际表现依赖数据质量、干预执行与病种谱差异，需通过试点与持续校准达成目标。