title: Automatic Differential Diagnosis using Transformer-Based Multi-Label
    Sequence Classification
summary: As the field of artificial intelligence progresses, assistive technologies
  are becoming more widely used across all industries. The healthcare industry is
  no different, with numerous studies being done to develop assistive tools for
  healthcare professionals. Automatic diagnostic systems are one such beneficial
  tool that can assist with a variety of tasks, including collecting patient
  information, analyzing test results, and diagnosing patients. However, the idea
  of developing systems that can provide a differential diagnosis has been
  largely overlooked in most of these research studies. In this study, we propose
  a transformer-based approach for providing differential diagnoses based on a
  patient's age, sex, medical history, and symptoms. We use the DDXPlus dataset,
  which provides differential diagnosis information for patients based on 49
  disease types. Firstly, we propose a method to process the tabular patient data
  from the dataset and engineer them into patient reports to make them suitable
  for our research. In addition, we introduce two data modification modules to
  diversify the training data and consequently improve the robustness of the
  models. We approach the task as a multi-label classification problem and
  conduct extensive experiments using four transformer models. All the models
  displayed promising results by achieving over 97% F1 score on the held-out test
  set. Moreover, we design additional behavioral tests to get a broader
  understanding of the models. In particular, for one of our test cases, we
  prepared a custom test set of 100 samples with the assistance of a doctor. The
  results on the custom set showed that our proposed data modification modules
  improved the model's generalization capabilities. We hope our findings will
  provide future researchers with valuable insights and inspire them to develop
  reliable systems for automatic differential diagnosis.
paper_authors._id: 66d03fe85685fc01abd65168
paper_authors.name: Abu Adnan Sadi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-02T09:23:02.086Z
paper_authors._id: 62ffac65311cad266f9b34f9
paper_authors.avatarUrl: /avatars/efacfa91f067acb20e372d9207448bcb.svg
paper_authors.fullname: Abu Adnan Sadi
paper_authors.type: user
paper_authors.user: AdnanSadi
paper_authors._id: 66d03fe85685fc01abd65169
paper_authors.name: Mohammad Ashrafuzzaman Khan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-04T07:25:27.415Z
paper_authors._id: 62fd0a8403f86646220655b8
paper_authors.avatarUrl: /avatars/392731415f3772946c22f13ceee6ef47.svg
paper_authors.fullname: Mohammad Ashrafuzzaman Khan
paper_authors.type: user
paper_authors.user: m-ak
paper_authors._id: 66d03fe85685fc01abd6516a
paper_authors.name: Lubaba Binte Saber
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-03T07:48:37.634Z
paper_authors._id: 66d6103604841918b64e22d0
paper_authors.avatarUrl: /avatars/e917765f6c024743b68907f940a3ffcf.svg
paper_authors.fullname: Lubaba Binte Saber 
paper_authors.type: user
paper_authors.user: Lubaba26
paper_title: Automatic Differential Diagnosis using Transformer-Based Multi-Label
    Sequence Classification
paper_summary: As the field of artificial intelligence progresses, assistive technologies
  are becoming more widely used across all industries. The healthcare industry is
  no different, with numerous studies being done to develop assistive tools for
  healthcare professionals. Automatic diagnostic systems are one such beneficial
  tool that can assist with a variety of tasks, including collecting patient
  information, analyzing test results, and diagnosing patients. However, the idea
  of developing systems that can provide a differential diagnosis has been
  largely overlooked in most of these research studies. In this study, we propose
  a transformer-based approach for providing differential diagnoses based on a
  patient's age, sex, medical history, and symptoms. We use the DDXPlus dataset,
  which provides differential diagnosis information for patients based on 49
  disease types. Firstly, we propose a method to process the tabular patient data
  from the dataset and engineer them into patient reports to make them suitable
  for our research. In addition, we introduce two data modification modules to
  diversify the training data and consequently improve the robustness of the
  models. We approach the task as a multi-label classification problem and
  conduct extensive experiments using four transformer models. All the models
  displayed promising results by achieving over 97% F1 score on the held-out test
  set. Moreover, we design additional behavioral tests to get a broader
  understanding of the models. In particular, for one of our test cases, we
  prepared a custom test set of 100 samples with the assistance of a doctor. The
  results on the custom set showed that our proposed data modification modules
  improved the model's generalization capabilities. We hope our findings will
  provide future researchers with valuable insights and inspire them to develop
  reliable systems for automatic differential diagnosis.
paper_ai_keywords: transformer-based approach differential diagnoses patient data patient reports multi-label classification transformer models F1 score generalization capabilities