Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled with the clinical prioritisation criteria (CPC) of Queensland (QLD), Australia, to deliver better triaging for referrals in accordance with the CPC’s updates. The unique feature of the proposed model is its non-reliance on the past datasets for model training. Medical Natural Language Processing (NLP) was applied in the proposed approach to process the medical referrals, which are unstructured free text. The proposed multiclass classification approach achieved a Micro F1 score = 0.98. The proposed approach can help in the processing of two million referrals that the QLD health service receives annually; therefore, they can deliver better and more efficient health services.

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With the extended π-electronic delocalization, organic 4-N,N-dimethylamino-4'-N'-methyl-stilbazolium tosylate (DAST) exhibits excellent third-order saturation absorption and optical limiting properties, but rare results about its third-order nonlinear emission have been previously reported. In this work, a flexible DAST−polyvinyl alcohol composite film was prepared, and its nonlinear emission properties were systematically characterized. Results reveal that besides a distinct second harmonic generation signal originating from the non-centrosymmetric macroscopic packing of the DAST chromophores, a strong third-order two-photon excited fluorescence signal was also measured from the composite, as experimentally confirmed by a quadratic dependence of the emission intensity on the pumped intensity. These nonlinear emission behaviors reveal that ionic organic DAST molecules hold great potential applications in laser frequency conversion and optical imaging.