Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records.
Context: Clinicians document cancer patients' symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woe...
| Publicado en: | Journal of Pain & Symptom Management Vol. 55; no. 6; pp. 1492 - 1500 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jun2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129566017&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129566017 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08853924 4CB jtl: Journal of Pain & Symptom Management issn: 08853924 maglogo: N pubinfo: dt: Jun2018 vid: 55 iid: 6 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 129566017 129566017 NLM29496537 129566017 10.1016/j.jpainsymman.2018.02.016 NLM29496537 129566017 ppf: 1492 ppct: 8 formats: tig: atl: Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records. aug: au: Forsyth, Alexander W. Barzilay, Regina Hughes, Kevin S. Lui, Dickson Lorenz, Karl A. Enzinger, Andrea Tulsky, James A. Lindvall, Charlotta affil: Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts sug: subj: Palliative Care Breast Neoplasms Machine Learning Methods Electronic Health Records Natural Language Processing Human Cancer Patients Medical Records Patient-Reported Outcomes Academic Medical Centers Models, Statistical Descriptive Statistics Outcome Assessment ab: Context: Clinicians document cancer patients' symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped.Objectives: To create machine learning algorithms capable of extracting patient-reported symptoms from free-text electronic health record notes.Methods: The data set included 103,564 sentences obtained from the electronic clinical notes of 2695 breast cancer patients receiving paclitaxel-containing chemotherapy at two academic cancer centers between May 1996 and May 2015. We manually annotated 10,000 sentences and trained a conditional random field model to predict words indicating an active symptom (positive label), absence of a symptom (negative label), or no symptom at all (neutral label). Sentences labeled by human coder were divided into training, validation, and test data sets. Final model performance was determined on 20% test data unused in model development or tuning.Results: The final model achieved precision of 0.82, 0.86, and 0.99 and recall of 0.56, 0.69, and 1.00 for positive, negative, and neutral symptom labels, respectively. The most common positive symptoms were pain, fatigue, and nausea. Machine-based labeling of 103,564 sentences took two minutes.Conclusion: We demonstrate the potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy. Although our initial model requires further optimization to improve the performance, further model building may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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