# An Order-Sensitive Hierarchical Neural Model for Early Lung Cancer Detection Using Dutch Primary Care Notes and Structured Data

## TL;DR

This study developed models to detect lung cancer early using patient notes and clinical data, showing promising results for identifying high-risk patients.

## Contribution

A novel hierarchical neural model that uses text order and combines it with clinical data for early lung cancer detection.

## Key findings

- The combined model achieved an AUROC of 0.91 and a PPV of 0.034 in detecting lung cancer.
- Including clinical variables slightly improved model performance compared to text-only models.
- The model requires additional testing for 29 high-risk patients to detect one cancer case.

## Abstract

The research aims to improve early detection of lung cancer by developing better prediction models using free-text notes from doctor consultations, which capture the context and order of words and sentences. The authors created two models: one using only text and another combining text with clinical data. These models were tested on a large dataset of patients, with the combined model performing slightly better, accurately identifying high-risk patients who might need further testing. The findings show that these models could help doctors detect lung cancer earlier. The models should be validated in other populations before being adopted into clinical practice.

Background: Improving prediction models to timely detect lung cancer is paramount. Our aim is to develop and validate prediction models for early detection of lung cancer in primary care, based on free-text consultation notes, that exploit the order and context among words and sentences. Methods: Data of all patients enlisted in 49 general practices between 2002 and 2021 were assessed, and we included those older than 30 years with at least one free-text note. We developed two models using a hierarchical architecture that relies on attention and bidirectional long short-term memory networks. One model used only text, while the other combined text with clinical variables. The models were trained on data excluding the five months leading up to the diagnosis, using target replication and a tuning set, and were tested on a separate dataset for discrimination, PPV, and calibration. Results: A total of 250,021 patients were enlisted, with 1507 having a lung cancer diagnosis. Included in the analysis were 183,012 patients, of which 712 had the diagnosis. From the two models, the combined model showed slightly better performance, achieving an AUROC on the test set of 0.91, an AUPRC of 0.05, and a PPV of 0.034 (0.024, 0.043), and showed good calibration. To early detect one cancer patient, 29 high-risk patients would require additional diagnostic testing. Conclusions: Our models showed excellent discrimination by leveraging the word and sentence structure. Including clinical variables in addition to text slightly improved performance. The number needed to treat holds promise for clinical practice. Investigating external validation and model suitability in clinical practice is warranted.