Anatomic stage extraction from medical reports of breast Cancer patients using natural language processing

Deconstruction of physical fitness assessment system and medical rehabilitation countermeasures for physically disabled teenagers with natural language processing technology

Disability is a component of social relations and the existence of social reality. An effective solution to the problem of persons with disabilities is a measure of human civilization and social progress. The strength of the implementation of disability policy is the key to the realization of the original intention of “effectively solving the problem of disabled persons and promoting their development”. The purpose of this paper is to study the physical fitness evaluation system and medical rehabilitation strategies of physically handicapped adolescents with natural language processing technology. It mainly adopts literature research, interview and comparative analysis. Taking the rehabilitation policy for the disabled as the research starting point, the current situation of mental health of physically disabled adolescents will be examined, and the influence of physical exercise attitude and exercise level on mental health will be explored. And by comparing the difference in physical exercise level and mental health of physically handicapped youth and able-bodied youth, the effect of physical exercise on mental health of physically handicapped youth is further explained. This paper selects a total of 760 physically disabled students and able-bodied students from a secondary school as subjects, and uses research methods such as questionnaire survey and computer test to investigate the current situation of mental health of physically disabled adolescents and the relationship between physical exercise and mental health. The experimental results show that the spatiotemporal judgment experiment uses pixel difference as an indicator, and the judgment error of limb residual limbs is significantly larger than that of healthy limbs (p < 0.01). In the span of spatial location memory, the grades of the limb-restrained students were significantly lower than that of the able-bodied students (p < 0.05).

Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing

PURPOSE

The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements.

METHODS

Published literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards.

RESULTS

A total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists.

CONCLUSION

We conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.

Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model

BACKGROUND

Cancer staging information is an essential component of cancer research. However, the information is primarily stored as either a full or semistructured free-text clinical document which is limiting the data use. By transforming the cancer-specific data to the Observational Medical Outcome Partnership Common Data Model (OMOP CDM), the information can contribute to establish multicenter observational cancer studies. To the best of our knowledge, there have been no studies on OMOP CDM transformation and natural language processing (NLP) for thyroid cancer to date.

OBJECTIVE

We aimed to demonstrate the applicability of the OMOP CDM oncology extension module for thyroid cancer diagnosis and cancer stage information by processing free-text medical reports.

METHODS

Thyroid cancer diagnosis and stage-related modifiers were extracted with rule-based NLP from 63,795 thyroid cancer pathology reports and 56,239 Iodine whole-body scan reports from three medical institutions in the Observational Health Data Sciences and Informatics data network. The data were converted into the OMOP CDM v6.0 according to the OMOP CDM oncology extension module. The cancer staging group was derived and populated using the transformed CDM data.

RESULTS

The extracted thyroid cancer data were completely converted into the OMOP CDM. The distributions of histopathological types of thyroid cancer were approximately 95.3 to 98.8% of papillary carcinoma, 0.9 to 3.7% of follicular carcinoma, 0.04 to 0.54% of adenocarcinoma, 0.17 to 0.81% of medullary carcinoma, and 0 to 0.3% of anaplastic carcinoma. Regarding cancer staging, stage-I thyroid cancer accounted for 55 to 64% of the cases, while stage III accounted for 24 to 26% of the cases. Stage-II and -IV thyroid cancers were detected at a low rate of 2 to 6%.

CONCLUSION

As a first study on OMOP CDM transformation and NLP for thyroid cancer, this study will help other institutions to standardize thyroid cancer-specific data for retrospective observational research and participate in multicenter studies.

Multi-modal Broad Learning System for Medical Image and Text-based Classification

Automatic classification of medical images plays an essential role in computer-aided diagnosis. However, the medical images arise from the small number of available data and the improvement of existing data-enhancement methods are limited. In order to fulfil this demand, a Multi-Modal Broad Learning System (M²-BLS) is proposed, which has two subnetworks for simultaneous learning of both medical images and the corresponding radiology reports. M²-BLS provides two advantages: i) our M²-BLS has closed-form solution and avoids iterative training, once the image feature is available; ii) benefit from the simultaneous learning of both image and text data, our M²-BLS achieves high accuracy for medical classification. Experimental results on the publicly available datasets IU X-RAY and PEIR GROSS_895 show that our M²-BLS highly improves the classification performance, compared to SOTA deep models that learn single-type of data information only.