AimThe role of machine learning on clinical documentation for predictive outcomes remains undefined. We aimed to compare three neural networks on inpatient providers’ notes to predict mortality in neonatal hypoxic‐ischaemic encephalopathy (HIE).MethodsUsing Children's Hospitals Neonatal Database, non‐anomalous neonates with HIE treated with therapeutic hypothermia were identified at a single‐centre. Data were linked with the initial seven days of documentation. Exposures were derived using the databases and applying convolutional and two recurrent neural networks. The primary outcome was mortality. The predictive accuracy and performance measures for models were determined.ResultsThe cohort included 52 eligible infants. Most infants survived (n = 36, 69%) and 23 had severe HIE (44%). Neural networks performed above baseline and differed in their median accuracy for predicting mortality (P = .0001): recurrent models with long short‐term memory 69% (25th, 75th percentile 65, 73%) and gated‐recurrent model units 65% (62, 69%) and convolutional 72% (64, 96%). Convolutional networks’ median specificity was 81% (72, 97%).ConclusionThe neural network models demonstrated fundamental validity in predicting mortality using inpatient provider documentation. Convolutional models had high specificity for (excluding) mortality in neonatal HIE. These findings provide a platform for future model training and ultimately tool development to assist clinicians in patient assessments and risk stratifications.

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Specials medications are personalized formulations manufactured on demand for patients with unique prescription requirements and constitute an essential component of patient treatment. Specials are becoming increasingly in demand due to the need for personalized and precision medicine. The timely provision of optimal personalized medicine, however, is challenging, subject to strict regulatory processes, and is expert intensive. In this paper, we propose a new medical formulation engine (MFE) that performs semantic search across multiple disparate formulations archives to enable data driven formulation intelligence. We develop a new platform for medical formulations recognition (MFR) that curates a new dataset comprising formulations and non-formulations (clinical) text and uses a novel pipeline encompassing deep feature extraction and one-class support vector machine learning. The proposed MFR framework demonstrates promising performance and can be used as a benchmark for future research in formulations recognition.