ARXIV:2602.21165 · HEALTHCARE NLP · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

Q: What products could be built from this research?

PVminer can be productized as a software tool for healthcare systems to analyze patient-generated data, aiding in patient-centric analysis and care improvement strategies.

Q: What are the practical use cases?

Develop an API enabling healthcare providers to integrate patient voice analysis into existing e-health solutions, aiding improved patient-provider communication and decision-making processes.

Q: What industries could this research disrupt?

PVminer could replace manual, labor-intensive qualitative analysis of patient communications, offering scalable and precise automated insights aligned with patient and provider needs.

arXiv

PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care. Traditional qualitative coding frameworks are labor intensive and do not scale to large volumes of patient-authored messages across health systems.

METHOD

Full abstract

Patient-generated text such as secure messages, surveys, and interviews contains rich expressions of the patient voice (PV), reflecting communicative behaviors and social determinants of health (SDoH). Traditional qualitative coding frameworks are labor intensive and do not scale to large volumes of patient-authored messages across health systems. Existing machine learning (ML) and natural language processing (NLP) approaches provide partial solutions but often treat patient-centered communication (PCC) and SDoH as separate tasks or rely on models not well suited to patient-facing language. We introduce PVminer, a domain-adapted NLP framework for structuring patient voice in secure patient-provider communication. PVminer formulates PV detection as a multi-label, multi-class prediction task integrating patient-specific BERT encoders (PV-BERT-base and PV-BERT-large), unsupervised topic modeling for thematic augmentation (PV-Topic-BERT), and fine-tuned classifiers for Code, Subcode, and Combo-level labels. Topic representations are incorporated during fine-tuning and inference to enrich semantic inputs. PVminer achieves strong performance across hierarchical tasks and outperforms biomedical and clinical pre-trained baselines, achieving F1 scores of 82.25% (Code), 80.14% (Subcode), and up to 77.87% (Combo). An ablation study further shows that author identity and topic-based augmentation each contribute meaningful gains. Pre-trained models, source code, and documentation will be publicly released, with annotated datasets available upon request for research use.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. PVminer achieves strong performance across hierarchical tasks and outperforms biomedical and clinical pre-trained baselines, achieving F1 scores of 82.25% (Code), 80.14% (Subcode), and up…

WHY NOW

Healthcare NLP moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainPVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.21165 · HEALTHCARE NLP · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

arXiv

PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Healthcare NLP moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainPVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.21165

PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback68 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Healthcare NLP moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care. Traditional qualitative coding frameworks are labor intensive and do not scale to large volumes of patient-authored messages across health systems.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Patient-generated text such as secure messages, surveys, and interviews contains rich expressions of the patient voice (PV), reflecting communicative behaviors and social determinants of health (SDoH). Traditional qualitative coding frameworks are labor intensive and do not scale to large volumes of patient-authored messages across health systems.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. PVminer achieves strong performance across hierarchical tasks and outperforms biomedical and clinical pre-trained baselines, achieving F1 scores of 82.25% (Code), 80.14% (Subcode), and up to 77.87% (Combo).
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Healthcare NLP moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Healthcare NLP

Competitors

not indexed

Competitive landscape

PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.

Segment

Healthcare NLP

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.21165 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(68)

A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection

2024Linhai Ma, Jiasong Chen et al.

Improving adversarial robustness of deep neural networks via adaptive margin evolution

2023Linhai Ma, Liang Liang

Identifying the mechanisms of patient-centred communication in secure messages between clinicians and cancer patients

2023Aantaki Raisa, J. Alpert et al.

Towards lifting the trade-off between accuracy and adversarial robustness of deep neural networks with application on COVID 19 CT image classification and medical image segmentation

2023Linhai Ma, Liang Liang

Extracting social determinants of health events with transformer-based multitask, multilabel named entity recognition

2023Russell Richie, V. Ruiz et al.

The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health

2023Kevin Lybarger, Meliha Yetisgen-Yildiz et al.

TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter

2022Xinyang Zhang, Yury Malkov et al.

Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks

2022David Oniani, Sonish Sivarajkumar et al.

Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection

2022Linhai Ma, Liang Liang

Dependency multi-weight-view graphs for event detection with label co-occurrence

2022Yan Wang, Jian Wang et al.

Conditional Probability Joint Extraction of Nested Biomedical Events: Design of a Unified Extraction Framework Based on Neural Networks

2022Yan Wang, Jian Wang et al.

Training language models to follow instructions with human feedback

2022Long Ouyang, Jeff Wu et al.

A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification

2021Linhai Ma, Liang Liang

Patient-generated health data and electronic health record integration: a scoping review

2020V. Tiase, William Hull et al.

Improving secure messaging: A framework for support, partnership & information-giving communicating electronically (SPICE).

2020J. Alpert, Shu Wang et al.

A Review of Generalized Zero-Shot Learning Methods

2020Farhad Pourpanah, Moloud Abdar et al.

Self-Alignment Pretraining for Biomedical Entity Representations

2020Fangyu Liu, Ehsan Shareghi et al.

Using de-identified electronic health records to research mental health supported housing services: A feasibility study

2020C. Dalton-Locke, J. Thygesen et al.

Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks

2020Linhai Ma, Liang Liang

Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)

2020Morwenna Senior, M. Burghart et al.

Showing 20 of 68 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkSelf-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction

7.0

Extension

Builds On ThisSmall Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

5.0

Builds On ThisExploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction

5.0

Builds On ThisMultiSense-Pneumo: A Multimodal Learning Framework for Pneumonia Screening in Resource-Constrained Settings

6.0

Builds On ThisParameter-Efficient VLMs for Gastrointestinal Endoscopy: Medical Image Generation and Clinical Visual Question Answering

0.0

Builds On ThisA multimodal and temporal foundation model for virtual patient representations at healthcare system scale

6.0

Commercially relevant

Higher ViabilityPVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models

8.0

Higher ViabilityVIVID-Med: LLM-Supervised Structured Pretraining for Deployable Medical ViTs

8.0

Higher ViabilityThe Patient is not a Moving Document: A World Model Training Paradigm for Longitudinal EHR

8.0

Higher ViabilityAutomated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models

8.0

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.21165
Route: /paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data
Active tab: read
Artifact: pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data/paper-pack
REST build passport API/api/v1/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "50c48169-e4df-4e4c-90f5-5d71e97ef986",
  "arxiv_id": "2602.21165",
  "canonical_route": "/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data",
  "endpoints": {
    "paper_pack": "/api/v1/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data/paper-pack",
    "build_passport": "/api/v1/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

Canonical ID pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data | Route /paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.21165"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data",
  "normalized_query": "2602.21165",
  "route": "/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data",
  "paper_ref": "pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

/buildability/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data

Watchwatch

Subject: PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data

Paper ref

pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data

arXiv id

2602.21165

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

c0093f73a94ddecbf4a3dd38cde07d011893401c811237d5bc878f8cd449bd50

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data#webpage",
      "url": "https://sciencetostartup.com/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data",
      "name": "PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data",
      "description": "PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data#scholarlyArticle",
      "headline": "PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data",
      "description": "PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care.",
      "url": "https://sciencetostartup.com/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data",
      "sameAs": "https://arxiv.org/abs/2602.21165",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2602.21165"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-02-24T18:10:00.000Z",
      "author": [
        {
          "@type": "Person",
          "name": "Samah Fodeh",
          "affiliation": {
            "@type": "Organization",
            "name": "Yale School of Medicine"
          }
        },
        {
          "@type": "Person",
          "name": "Linhai Ma",
          "affiliation": {
            "@type": "Organization",
            "name": "Yale School of Medicine"
          }
        },
        {
          "@type": "Person",
          "name": "Yan Wang",
          "affiliation": {
            "@type": "Organization",
            "name": "Yale School of Medicine"
          }
        },
        {
          "@type": "Person",
          "name": "Srivani Talakokkul",
          "affiliation": {
            "@type": "Organization",
            "name": "Yale School of Medicine"
          }
        },
        {
          "@type": "Person",
          "name": "Ganesh Puthiaraju",
          "affiliation": {
            "@type": "Organization",
            "name": "Yale School of Medicine"
          }
        },
        {
          "@type": "Person",
          "name": "Afshan Khan",
          "affiliation": {
            "@type": "Organization",
            "name": "Yale School of Medicine"
          }
        },
        {
          "@type": "Person",
          "name": "Ashley Hagaman",
          "affiliation": {
            "@type": "Organization",
            "name": "Yale School of Public Health"
          }
        },
        {
          "@type": "Person",
          "name": "Sarah Lowe",
          "affiliation": {
            "@type": "Organization",
            "name": "Yale School of Public Health"
          }
        },
        {
          "@type": "Person",
          "name": "Aimee Roundtree",
          "affiliation": {
            "@type": "Organization",
            "name": "Texas State University"
          }
        }
      ],
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "0b187b470625bd49b9e3a72460ec152307ab2d28"
          },
          "url": "https://www.semanticscholar.org/paper/0b187b470625bd49b9e3a72460ec152307ab2d28"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "b57348cc65ab2f32c3892554a2f00be1b5f2c85d"
          },
          "url": "https://www.semanticscholar.org/paper/b57348cc65ab2f32c3892554a2f00be1b5f2c85d"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d889a41260ebfb98f843bce35205a8faea72e3e1"
          },
          "url": "https://www.semanticscholar.org/paper/d889a41260ebfb98f843bce35205a8faea72e3e1"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d1431f7f749cd132d9faeddf802107d5baeab6ff"
          },
          "url": "https://www.semanticscholar.org/paper/d1431f7f749cd132d9faeddf802107d5baeab6ff"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "cbcd28305b9e28bd50b0227a0eed4563055f1429"
          },
          "url": "https://www.semanticscholar.org/paper/cbcd28305b9e28bd50b0227a0eed4563055f1429"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "393791e54a6fb3fbcd72775e9cdb7c3e221b1907"
          },
          "url": "https://www.semanticscholar.org/paper/393791e54a6fb3fbcd72775e9cdb7c3e221b1907"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f5888d776f122f53292973bd3693628ebd265bc6"
          },
          "url": "https://www.semanticscholar.org/paper/f5888d776f122f53292973bd3693628ebd265bc6"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1b46e078b7303b0972599c514fa4b2a0fae7e7ff"
          },
          "url": "https://www.semanticscholar.org/paper/1b46e078b7303b0972599c514fa4b2a0fae7e7ff"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "5b92ca26cd4fbedbaa2420277ee201ad97a2108c"
          },
          "url": "https://www.semanticscholar.org/paper/5b92ca26cd4fbedbaa2420277ee201ad97a2108c"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "687fae7b8b886f04a43e2071d9a7e10b2803362d"
          },
          "url": "https://www.semanticscholar.org/paper/687fae7b8b886f04a43e2071d9a7e10b2803362d"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "2cec6c2f2208a9c0e73f3861dfe968f70a3a17bd"
          },
          "url": "https://www.semanticscholar.org/paper/2cec6c2f2208a9c0e73f3861dfe968f70a3a17bd"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d766bffc357127e0dc86dd69561d5aeb520d6f4c"
          },
          "url": "https://www.semanticscholar.org/paper/d766bffc357127e0dc86dd69561d5aeb520d6f4c"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "cfd8beedb73b54d66fdb1be832a904d1fd90e1b2"
          },
          "url": "https://www.semanticscholar.org/paper/cfd8beedb73b54d66fdb1be832a904d1fd90e1b2"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "bcbce875d3cf3244cb0f942c76931dbdc86c41bd"
          },
          "url": "https://www.semanticscholar.org/paper/bcbce875d3cf3244cb0f942c76931dbdc86c41bd"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d3532519747c314fa6e5d916579f99ccb50a0e4e"
          },
          "url": "https://www.semanticscholar.org/paper/d3532519747c314fa6e5d916579f99ccb50a0e4e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "8ce404fbf787bb71800fc945b225743b07f5f7a9"
          },
          "url": "https://www.semanticscholar.org/paper/8ce404fbf787bb71800fc945b225743b07f5f7a9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "bb3d3d4be92d741d2dc30e6cd02468f74be0d716"
          },
          "url": "https://www.semanticscholar.org/paper/bb3d3d4be92d741d2dc30e6cd02468f74be0d716"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "c7bb9316f3caae7eda7855ec8d20f0d66afd826e"
          },
          "url": "https://www.semanticscholar.org/paper/c7bb9316f3caae7eda7855ec8d20f0d66afd826e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "204e971c6478bbac37827e6a04fec59c3f5fa29e"
          },
          "url": "https://www.semanticscholar.org/paper/204e971c6478bbac37827e6a04fec59c3f5fa29e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "52940aed54c37b3f10db8ac3f6ff67ac9d793a69"
          },
          "url": "https://www.semanticscholar.org/paper/52940aed54c37b3f10db8ac3f6ff67ac9d793a69"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 7
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "Healthcare NLP"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Healthcare NLP",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "PVminer: A Domain-Specific Tool to Detect the Patient Voice ",
          "item": "https://sciencetostartup.com/paper/pvminer-a-domain-specific-tool-to-detect-the-patient-voice-in-patient-generated-data"
        }
      ]
    },
    {
      "@type": "FAQPage",
      "mainEntity": [
        {
          "@type": "Question",
          "name": "What is the startup potential of \"PVminer: A Domain-Specific Tool to Detect the Patient Voice \"?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care."
          }
        },
        {
          "@type": "Question",
          "name": "What products could be built from this research?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "PVminer can be productized as a software tool for healthcare systems to analyze patient-generated data, aiding in patient-centric analysis and care improvement strategies."
          }
        },
        {
          "@type": "Question",
          "name": "What are the practical use cases?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "Develop an API enabling healthcare providers to integrate patient voice analysis into existing e-health solutions, aiding improved patient-provider communication and decision-making processes."
          }
        },
        {
          "@type": "Question",
          "name": "What industries could this research disrupt?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "PVminer could replace manual, labor-intensive qualitative analysis of patient communications, offering scalable and precise automated insights aligned with patient and provider needs."
          }
        }
      ]
    }
  ]
}

PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(68)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(68)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline