Prediction of Bladder Cancer Treatment Side Effects Using an Ontology-Based Reasoning for Enhanced Patient Health Safety

Chamseddine Barki,Hanene Boussi Rahmouni,Salam Labidi

doi:10.3390/informatics8030055

Chamseddine Barki, Hanene Boussi Rahmouni + Show 1 more

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https://doi.org/10.3390/informatics8030055

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Abstract

Predicting potential cancer treatment side effects at time of prescription could decrease potential health risks and achieve better patient satisfaction. This paper presents a new approach, founded on evidence-based medical knowledge, using as much information and proof as possible to help a computer program to predict bladder cancer treatment side effects and support the oncologist’s decision. This will help in deciding treatment options for patients with bladder malignancies. Bladder cancer knowledge is complex and requires simplification before any attempt to represent it in a formal or computerized manner. In this work we rely on the capabilities of OWL ontologies to seamlessly capture and conceptualize the required knowledge about this type of cancer and the underlying patient treatment process. Our ontology allows case-based reasoning to effectively predict treatment side effects for a given set of contextual information related to a specific medical case. The ontology is enriched with proofs and evidence collected from online biomedical research databases using “web crawlers”. We have exclusively designed the crawler algorithm to search for the required knowledge based on a set of specified keywords. Results from the study presented 80.3% of real reported bladder cancer treatment side-effects prediction and were close to really occurring adverse events recorded within the collected test samples when applying the approach. Evidence-based medicine combined with semantic knowledge-based models is prominent in generating predictions related to possible health concerns. The integration of a diversity of knowledge and evidence into one single integrated knowledge-base could dramatically enhance the process of predicting treatment risks and side effects applied to bladder cancer oncotherapy.

Highlights

Bladder cancer (BC) remains a major concern for urologists worldwide despite considerable advances in the medical field
To present the different methods and the research methodology adopted in this work, we rely mainly on a scenario describing an instance of the process a doctor adopts to make a choice of treatment protocol for a patient suffering from BC
These describe the composition of the ontology in terms of various structured components including: There are -6 super-classes targeting the BC, treatments, procedures, risks, and evidence, each of which contains a large number of hierarchical subclasses linked to instances describing concrete objects about

Summary

Introduction

Bladder cancer (BC) remains a major concern for urologists worldwide despite considerable advances in the medical field. BC has a standardized overall age-specific mortality rate estimated at 4.7 per 100,000 [1] It is of a particular importance in the field of urological carcinology in predicting treatment side effects (SEs), due to its frequency, its anatomopathological polymorphism, the difficulty of precise staging and the great prognostic uncertainty. Following a patient’s discovery of BC, the care team develops a personalized treatment plan This is based on the patient’s health and specific information about cancer. Immunotherapy, Chemotherapy and Radiation therapy are common practices for the treatment of BC [4] These procedures have many side and unwanted effects including mouth sores, tiredness, changes in kidney or liver function, diarrhea, dry mouth, changes in fingernails or toenails, changes in mineral levels in the blood, loss of appetite, loss of taste, anemia, dry skin, dry eyes, and hair loss, along with redness, swelling, peeling or tenderness on the hands/feet, constipation, belly pain, nausea, and muscle pain.

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