Information extraction for prognostic stage prediction from breast cancer medical records using NLP and ML.
For cancer prediction, the prognostic stage is the main factor that helps medical experts to decide the optimal treatment for a patient. Specialists study prognostic stage information from medical reports, often in an unstructured form, and take a larger review time. The main objective of this study...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 9; pp. 1751 - 1773 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2021
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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=152044043&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152044043 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2021 vid: 59 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152044043 151536287 152044043 NLM34297300 10.1007/s11517-021-02399-7 NLM34297300 152044043 ppf: 1751 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Information extraction for prognostic stage prediction from breast cancer medical records using NLP and ML. aug: au: Deshmukh, Pratiksha R. Phalnikar, Rashmi affil: School of Computer Engineering and Technology, MIT World Peace University, 411029, Pune, India sug: subj: Breast Neoplasms Diagnosis Medical Records Proteins Information Retrieval Prognosis Female Scales Female ab: For cancer prediction, the prognostic stage is the main factor that helps medical experts to decide the optimal treatment for a patient. Specialists study prognostic stage information from medical reports, often in an unstructured form, and take a larger review time. The main objective of this study is to suggest a generic clinical decision-unifying staging method to extract the most reliable prognostic stage information of breast cancer from medical records of various health institutions. Additional prognostic elements should be extracted from medical reports to identify the cancer stage for getting an exact measure of cancer and improving care quality. This study has collected 465 pathological and clinical reports of breast cancer sufferers from India's reputed medical institutions. The unstructured records were found distinct from each institute. Anatomic and biologic factors are extracted from medical records using the natural language processing, machine learning and rule-based method for prognostic stage detection. This study has extracted anatomic stage, grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) from medical reports with high accuracy and predicted prognostic stage for both regions. The prognostic stage prediction's average accuracy is found 92% and 82% in rural and urban areas, respectively. It was essential to combine biological and anatomical elements under a single prognostic staging method. A generic clinical decision-unifying staging method for prognostic stage detection with great accuracy in various institutions of different regional areas suggests that the proposed research improves the prognosis of breast cancer. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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