2nd/10_Wiki/Topics/AI_and_ML/Search-Methodology.md

---
id: wiki-2026-0508-search-methodology
title: Search Methodology
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Systematic Search, Literature Search, Research Methodology]
duplicate_of: none
source_trust_level: A
confidence_score: 0.88
verification_status: applied
tags: [research, methodology, prisma, systematic-review, literature-search]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: none
---

# Search Methodology

## 매 한 줄
> **"매 reproducible literature search — define question, query strategy, screen, extract, synthesize"**. PRISMA 2020 매 standard for systematic reviews. 매 2026 update: AI-augmented (Elicit, Consensus, Undermind) + traditional database search 매 hybrid.

## 매 핵심

### 매 Research question framing
- **PICO** (clinical): Population, Intervention, Comparator, Outcome.
- **PEO** (qualitative): Population, Exposure, Outcome.
- **SPIDER** (mixed methods): Sample, Phenomenon, Design, Eval, Research-type.

### 매 PRISMA 2020 flow
1. **Identification**: 매 records from databases + registers + other.
2. **Screening**: 매 title/abstract → eligible.
3. **Eligibility**: 매 full-text review.
4. **Included**: 매 final corpus → synthesis.

### 매 Database strategy
- **Medical**: PubMed, EMBASE, Cochrane CENTRAL.
- **CS**: Google Scholar, Semantic Scholar, ACM/IEEE/arXiv.
- **Social**: Web of Science, Scopus, PsycINFO.
- 매 매 multiple databases 매 essential — 매 single source 매 missing 30-50%.

### 매 Query construction
- Boolean: AND, OR, NOT.
- 매 controlled vocabulary: MeSH, Emtree, ACM CCS.
- 매 truncation: `child*` matches child, children.
- 매 proximity: `"machine learning" NEAR/3 medicine`.

### 매 AI-augmented (2024-2026)
- **Elicit**: 매 question → relevant papers + extraction.
- **Consensus**: 매 yes/no claim verification.
- **Undermind**: 매 deep search agents.
- **OpenAlex API**: 매 250M scholarly works open.

### 매 응용
1. Systematic review / meta-analysis.
2. Tech due diligence.
3. PhD literature review.
4. Patent landscape analysis.

## 💻 패턴

### Boolean query construction
```python
from itertools import product

terms = {
    "concept_a": ["machine learning", "ML", "deep learning"],
    "concept_b": ["medical imaging", "radiology", "diagnostic imaging"],
    "concept_c": ["systematic review", "meta-analysis"],
}

def build_query(terms):
    blocks = []
    for concept, alts in terms.items():
        block = "(" + " OR ".join(f'"{t}"' for t in alts) + ")"
        blocks.append(block)
    return " AND ".join(blocks)

print(build_query(terms))
# ("machine learning" OR "ML" OR "deep learning") AND ("medical imaging" ...) AND ...
```

### PubMed E-utilities
```python
import requests

def pubmed_search(query, max_results=200):
    base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
    r = requests.get(f"{base}/esearch.fcgi", params={
        "db": "pubmed", "term": query, "retmax": max_results, "retmode": "json"
    })
    pmids = r.json()["esearchresult"]["idlist"]
    r2 = requests.get(f"{base}/esummary.fcgi", params={
        "db": "pubmed", "id": ",".join(pmids), "retmode": "json"
    })
    return r2.json()["result"]
```

### Semantic Scholar API
```python
def s2_search(query, limit=100):
    url = "https://api.semanticscholar.org/graph/v1/paper/search"
    fields = "title,abstract,authors,year,citationCount,openAccessPdf"
    r = requests.get(url, params={"query": query, "limit": limit, "fields": fields})
    return r.json()["data"]
```

### Deduplication
```python
from rapidfuzz import fuzz

def dedupe(records):
    unique = []
    seen_titles = []
    for r in records:
        title = r["title"].lower().strip()
        if any(fuzz.ratio(title, t) > 92 for t in seen_titles):
            continue
        seen_titles.append(title)
        unique.append(r)
    return unique
```

### Screening with LLM (title+abstract)
```python
from anthropic import Anthropic

client = Anthropic()

def llm_screen(record, inclusion_criteria):
    prompt = f"""Inclusion criteria: {inclusion_criteria}

Title: {record['title']}
Abstract: {record['abstract']}

Decision (INCLUDE / EXCLUDE / UNSURE) + 1-line reason:"""
    r = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=100,
        messages=[{"role": "user", "content": prompt}],
    )
    return r.content[0].text

# 매 always 매 human verify UNSURE + sample of INCLUDE/EXCLUDE.
```

### PRISMA flow tracking
```python
class PRISMA:
    def __init__(self):
        self.counts = {
            "identified_db": 0, "identified_reg": 0, "identified_other": 0,
            "duplicates": 0, "screened": 0, "excluded_screen": 0,
            "fulltext_sought": 0, "fulltext_unavailable": 0,
            "fulltext_assessed": 0, "excluded_eligibility": {},
            "included": 0,
        }
    def render(self):
        for k, v in self.counts.items():
            print(f"{k}: {v}")
```

### Forward / backward citation chasing
```python
def snowball(seed_dois, depth=1):
    frontier = set(seed_dois)
    found = set()
    for _ in range(depth):
        new = set()
        for doi in frontier:
            refs = s2_get_references(doi)
            cites = s2_get_citations(doi)
            new.update(refs + cites)
        found.update(frontier)
        frontier = new - found
    return found
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Cochrane systematic review | 매 PRISMA 2020 + 2-reviewer double screen |
| Tech scouting | 매 AI tools (Elicit, Consensus) + Semantic Scholar |
| Patent search | 매 EPO Espacenet + PatentScope |
| Quick lit review | 매 Google Scholar + snowball |
| AI-augmented full review | 매 LLM screen + 100% human verify |

**기본값**: 매 PRISMA 2020 + Boolean across ≥3 databases + LLM-assist screening + human verification.

## 🔗 Graph
- 부모: [[Research Methods]]
- Adjacent: [[Bibliometrics]] · [[Citation Analysis]]

## 🤖 LLM 활용
**언제**: 매 large-corpus screening (10k+ titles), 매 extraction template fill, 매 query expansion.
**언제 X**: 매 final inclusion decision (매 always human), 매 citation accuracy claim (매 hallucination risk).

## ❌ 안티패턴
- **Single database**: 매 30-50% missing.
- **No protocol**: 매 publication bias 매 invisible.
- **Single reviewer**: 매 ≥2 with kappa agreement.
- **LLM-only screening**: 매 hallucination + bias 매 verify 100%.
- **No PRISMA flow**: 매 unreproducible.

## 🧪 검증 / 중복
- Verified (PRISMA 2020 statement, Cochrane Handbook v6.4).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — PRISMA, Boolean, AI-augmented tools |