Using Natural Language Processing Techniques to Detect Adverse Events From Progress Notes Due to Chemotherapy

Abstract

Objective: In recent years, natural language processing (NLP) techniques have progressed, and their application in the medical field has been tested. However, the use of NLP to detect symptoms from medical progress notes written in Japanese, remains limited. We aimed to detect 2 gastrointestinal symptoms that interfere with the continuation of chemotherapy—nausea/vomiting and diarrhea—from progress notes using NLP, and then to analyze factors affecting NLP. Materials and methods: In this study, 200 patients were randomly selected from 5277 patients who received intravenous injections of cytotoxic anticancer drugs at Kagawa University Hospital, Japan, between January 2011 and December 2018. We aimed to detect the first occurrence of nausea/vomiting (Group A) and diarrhea (Group B) using NLP. The NLP performance was evaluated by the concordance with a review of the physicians’ progress notes used as the gold standard. Results: Both groups showed high concordance: 83.5% (95% confidence interval [CI] 74.1-90.1) in Group A and 97.7% (95% CI 91.3-99.9) in Group B. However, the concordance was significantly better in Group B ( P = .0027). There were significantly more misdetection cases in Group A than in Group B (15.3% in Group A; 1.2% in Group B, P = .0012) due to negative findings or past history. Conclusion: We detected occurrences of nausea/vomiting and diarrhea accurately using NLP. However, there were more misdetection cases in Group A due to negative findings or past history, which may have been influenced by the physicians’ more frequent documentation of nausea/vomiting.

Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?

“…In the studies of summarization of medical documents, it is common to retrieve key information such as disease, examination result, or medication from EHRs [11]- [14]. Other researchs more similar to our study targeted to help physicians get the point of medical documents quickly by generating a few key sentences [15]- [18].…”

Exploring optimal granularity for extractive summarization of unstructured health records: Analysis of the largest multi-institutional archive of health records in Japan

“…However, they are not summarization in a narrow sense, that distills important information from the input. Several works targeted acquiring key information such as disease, examination result, or medication from EHRs [6,8,28,29], while these studies collected fragmented information and did not try to generate contextualized passage. There are a line of researches that targeted to help physicians get the point quickly by generating a few key sentences [7,[30][31][32].…”

Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care

“…A Deep Neural Network model was trained on the EHR data of patients from previous years, to predict the mortality of patients within the next 3–12 month period [ 43 ]. Another study used the information on symptom burden of free-text notes in the EHR [ 44 ]. Here, natural language processing (NLP) was able to identify hospitalized cancer patients with uncontrolled symptoms (pain, dyspnea, or nausea/vomiting) in the EHR.…”

“…The accuracy was between 61% and 80% with low sensitivity for nausea/vomiting (21%) and dyspnea (22%). For this reason, this model also has to be further developed before it can be used to trigger early access to PC [ 44 ]. However, despite these existing success stories, specific screening tools or CDSS of patients in need for palliative care in early, intermediate, and late stages are missing because time-specific screening parameters and a reasonable amount of underlying data are not yet available to build such tools.…”