Predicting Diseases from Patient Narratives: An Innovative Approach to Enhance Healthcare Access

Concepts de base

A novel patient-side disease prediction model, PoMP, that leverages patient narratives including textual descriptions and demographic data to enable early disease detection and facilitate seamless healthcare access.

Résumé

The paper introduces Personalized Medical Disease Prediction (PoMP), a novel approach that predicts diseases based solely on patient-provided health narratives, including textual descriptions and demographic information. This is in contrast to existing disease prediction methods that heavily rely on clinical data such as laboratory tests and medical imaging, which are typically only available after a patient consults a healthcare professional.
PoMP employs a two-tiered classification architecture. It first predicts the broad disease category and then narrows down to the specific disease within that category. This hierarchical approach leverages the inherent structure of disease classifications.
The authors collected a comprehensive dataset, Haodf, from a leading online doctor consultation platform in China. The dataset includes patient narratives across six prevalent disease categories with varying risk levels, as well as the corresponding diagnoses made by doctors.
Extensive experiments on the Haodf dataset demonstrate the effectiveness of PoMP. Compared to various pre-trained language models, PoMP achieves state-of-the-art performance in 6 out of 7 evaluation scenarios, with significant improvements in both category prediction and disease prediction. The authors also conduct an ablation study to highlight the importance of incorporating demographic information in addition to textual descriptions for accurate disease prediction.
PoMP represents a significant advancement in making disease prediction more accessible and tailored to patient needs, thereby enhancing the efficiency of healthcare communication. By empowering patients to gain a clearer understanding of their potential health conditions, PoMP can facilitate timely connections with appropriate medical specialists, reducing the time and effort spent navigating the healthcare system.

Stats

Patients with Coronary Heart Disease (CHD) have an average age of 60.5 years.
Patients with Lung Cancer have an average age of 67.2 years.
The Haodf dataset contains a total of 29,326 patient records across 6 disease categories, with an average of 481.4 tokens per patient narrative.

Citations

"PoMP enables rapid comprehension of potential health conditions for individuals and seamless connections with doctors specializing in relevant medical disciplines."
"PoMP presents a promising approach and introduces the possibilities in patient-side disease prediction."

Idées clés tirées de

Enabling Patient-side Disease Prediction via the Integration of Patient Narratives

by Zhixiang Su,... à arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02935.pdf

Questions plus approfondies

How can PoMP be extended to incorporate real-time patient data, such as wearable device measurements, to further improve disease prediction accuracy

To incorporate real-time patient data from wearable devices into PoMP for enhanced disease prediction accuracy, several steps can be taken:

Data Integration: Develop a system to seamlessly integrate wearable device measurements, such as heart rate, activity levels, and sleep patterns, into the existing patient-side narratives. This integration can provide a more comprehensive view of the patient's health status.

Feature Engineering: Extract relevant features from the wearable device data and combine them with the existing patient data. This can include creating new input embeddings that capture real-time physiological indicators.

Continuous Learning: Implement a mechanism for continuous learning where the model adapts to new data in real-time. This ensures that the predictions are updated as new information becomes available from the wearable devices.

Feedback Loop: Establish a feedback loop where the model's predictions are compared with the actual outcomes based on the wearable device data. This feedback can be used to refine the model and improve its accuracy over time.

Privacy and Security: Ensure that the wearable device data is securely transmitted and stored to protect patient privacy. Implement encryption protocols and access controls to safeguard sensitive health information.

What are the potential ethical and privacy concerns associated with patient-side disease prediction, and how can they be addressed to ensure responsible deployment of such technologies

Ethical and privacy concerns related to patient-side disease prediction include:

Data Security: Patient health data is sensitive and must be protected from unauthorized access or breaches. Robust encryption and secure storage mechanisms should be in place to safeguard patient privacy.

Informed Consent: Patients should be fully informed about how their data will be used for disease prediction and must provide explicit consent. Transparency about data usage and sharing practices is essential.

Bias and Fairness: Machine learning models used for disease prediction may inadvertently perpetuate biases present in the data. Regular bias audits and fairness assessments should be conducted to ensure equitable outcomes for all patient groups.

Data Ownership: Clarify ownership rights of the data collected for disease prediction. Patients should have control over their health information and be aware of who has access to it.

To address these concerns, healthcare providers and researchers can implement strict data governance policies, conduct regular privacy audits, provide clear communication to patients about data usage, and adhere to regulatory guidelines such as GDPR and HIPAA.

Given the hierarchical nature of disease classifications, how can PoMP's architecture be leveraged to provide personalized treatment recommendations beyond just disease prediction

To provide personalized treatment recommendations beyond disease prediction, PoMP's architecture can be leveraged in the following ways:

Treatment Pathway Prediction: Extend the model to predict the most effective treatment pathways based on the predicted disease. This can involve recommending specific medications, therapies, or lifestyle changes tailored to the patient's condition.

Risk Assessment: Utilize the hierarchical disease classification to assess the patient's risk level for different complications or comorbidities associated with the predicted disease. This information can guide personalized preventive measures.

Outcome Prediction: Predict the potential outcomes of different treatment options for the predicted disease. By analyzing historical data and treatment responses, PoMP can offer insights into the likely prognosis for the patient.

Patient Education: Develop a module within PoMP that educates patients about their predicted disease, treatment options, and potential outcomes. Empowering patients with knowledge can improve treatment adherence and health outcomes.

By expanding PoMP's capabilities to include personalized treatment recommendations, healthcare providers can offer more holistic and patient-centric care, leading to improved health outcomes and patient satisfaction.