Use of a Latent Topic Model for Characteristic Extraction from Health Checkup Questionnaire Data

Co-occurrence of symptoms after radiochemotherapy in locally advanced cervix cancer patients: a cluster analysis

BACKGROUND

State of the art combined radiochemotherapy and image-guided brachytherapy for locally advanced cervical cancer (LACC) has shown improved disease control and survival as well as a significant reduction of organ related morbidity. However, LACC cancer survivors are still experiencing a spectrum of symptoms. The aim of this study was to identify co-occurring symptoms in cervix cancer survivors by using patient-reported outcome and physician assessed morbidity.

MATERIALS AND METHOD

EMBRACE I is a multicenter prospective observational study with 1416 LACC patients (2008-2015). Information on physician-assessed morbidity and patient-reported outcome was assessed at baseline and at regular follow-ups up with the CTCAE v.3 and EORTC-C30/CX24, respectively. Patients with at least 2 years of follow-up were included and data from 3 months to 2 years was used in the analysis. Factor analysis was used on both EORTC and CTCAE data with symptoms and follow-ups as observations. The extracted factors represent clusters of symptoms. Subsequently, regression models were built to investigate associations between the symptom clusters and QOL.

RESULTS

The analysis included 742 patients. Despite the differences in the definition of physician-assessed and patient-reported symptoms, similar clusters are identified by the two assessment methods. Three main organ-related clusters are recognized for urinary, gastro-intestinal and vaginal morbidity. Furthermore, a general symptoms cluster where fatigue, pain, insomnia, neuropathy, and hot flashes have large weights is found. Lastly, a cluster with nausea, vomit and lack of appetite is also identified. The general, gastrointestinal and nausea clusters show significant associations with general QOL.

CONCLUSIONS

This analysis on both PRO and physician-assessed morbidity found a cluster associated with general symptoms and organ-related symptom clusters (urinary, gastrointestinal, vaginal). This shows that LACC survivors experience a variety of co-occurring symptoms. Our analysis also shows that the cluster of general symptoms is associated with a decrease in QOL.

Categorising patient concerns using natural language processing techniques

OBJECTIVES

Patient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.

METHODS

Patient concerns were received by Alberta Health Services between 2011 and 2018 (n=76 163), regarding 806 care facilities in 163 municipalities, including hospitals, clinics, community care centres and retirement homes, in a province of 4.4 million. Their existing framework requires manual labelling of pre-defined categories. We applied an automated latent Dirichlet allocation (LDA)-based topic modelling algorithm to identify the topics present in these concerns, and thereby produce a framework-free categorisation.

RESULTS

The LDA model produced 40 topics which, following manual interpretation by researchers, were reduced to 28 coherent topics. The most frequent topics identified were communication issues causing delays (frequency: 10.58%), community care for elderly patients (8.82%), interactions with nurses (8.80%) and emergency department care (7.52%). Many patient concerns were categorised into multiple topics. Some were more specific versions of categories from the existing framework (eg, communication issues causing delays), while others were novel (eg, smoking in inappropriate settings).

DISCUSSION

LDA-generated topics were more nuanced than the manually labelled categories. For example, LDA found that concerns with community care were related to concerns about nursing for seniors, providing opportunities for insight and action.

CONCLUSION

Our findings outline the range of concerns patients share in a large health system and demonstrate the usefulness of using LDA to identify categories of patient concerns.

A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge*. Spanish-English Cross-language Case Study

OBJECTIVES

The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space.

METHODS

The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA), and a classifier that uses a domain-specific semantic annotator (MetaMap). The corpus used for the experiments (Cross-Language UVigoMED) was purpose-built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts.

RESULTS

The performance of our approach is superior to any other state-of-the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p-values < 0.0001.

CONCLUSION

Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language-specific concept spaces, a cross-language classifier can be built, and it performs better than several state-of-the-art classifiers.

A Factor Analysis Approach for Clustering Patient Reported Outcomes

BACKGROUND

In the field of radiation oncology, the use of extensive patient reported outcomes is increasingly common to measure adverse side effects after radiotherapy in cancer patients. Factor analysis has the potential to identify an optimal number of latent factors (i.e., symptom groups). However, the ultimate goal of treatment response modeling is to understand the relationship between treatment variables such as radiation dose and symptom groups resulting from FA. Hence, it is crucial to identify clinically more relevant symptom groups and improved response variables from those symptom groups for a quantitative analysis.

OBJECTIVES

The goal of this study is to design a computational method for finding clinically relevant symptom groups from PROs and to test associations between symptom groups and radiation dose.

METHODS

We propose a novel approach where exploratory factor analysis is followed by confirmatory factor analysis to determine the relevant number of symptom groups. We also propose to use a combination of symptoms in a symptom group identified as a new response variable in linear regression analysis to investigate the relationship between the symptom group and dose-volume variables.

RESULTS

We analyzed patient-reported gastrointestinal symptom profiles from 3 datasets in prostate cancer patients treated with radiotherapy. The final structural model of each dataset was validated using the other two datasets and compared to four other existing FA methods. Our systematic EFA-CFA approach provided clinically more relevant solutions than other methods, resulting in new clinically relevant outcome variables that enabled a quantitative analysis. As a result, statistically significant correlations were found between some dose-volume variables to relevant anatomic structures and symptom groups identified by FA.

CONCLUSIONS

Our proposed method can aid in the process of understanding PROs and provide a basis for improving our understanding of radiation-induced side effects.