Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?

Background: Rare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all individuals affected by (different) RD during the time before diagnosis is established.

Objective: We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD.

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Background: Diagnosing a rare metabolic disease challenges physicians and affected individuals and their families. To support the diagnostic pathway, a diagnostic tool was developed using the experiences of the affected individuals gained in interviews.

Methods: 17 interviews with parents or individuals with a selected rare metabolic disease (Mucopolysaccharidosis (MPS), M.

Diagnostic needs for rare diseases and shared prediagnostic phenomena: Results of a German-wide expert Delphi survey.

Background: Worldwide approximately 7,000 rare diseases have been identified. Accordingly, 4 million individuals live with a rare disease in Germany. The mean time to diagnosis is about 6 years and patients receive several incorrect diagnoses during this time.

Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial.

Background: Diagnosis of neuromuscular diseases in primary care is often challenging. Rare diseases such as Pompe disease are easily overlooked by the general practitioner. We therefore aimed to develop a diagnostic support tool using patient-oriented questions and combined data mining algorithms recognizing answer patterns in individuals with selected neuromuscular diseases.