FHIR-DHP: A standardized clinical data harmonisation pipeline for scalable AI application deployment

Background Increasing digitalisation in the medical domain gives rise to large amounts of healthcare data which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to non-standardised data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the healthcare system. Despite the existence of standardised data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remains limited.Objective We developed a data harmonisation pipeline (DHP) for clinical data sets relying on the common FHIR data standard.Methods We validated the performance and usability of our FHIR-DHP with data from the MIMIC IV database including > 40,000 patients admitted to an intensive care unit.Results We present the FHIR-DHP workflow in respect of transformation of “raw” hospital records into a harmonised, AI-friendly data representation. The pipeline consists of five key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonised data into the patient-model database and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records.Conclusions Our approach enables scalable and needs-driven data modelling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step towards increasing cooperation, interoperability and quality of patient care in the clinical routine and for medical research

Toxic effects of phenothiazines on the eye

Publications about the retinotoxic action of phenothiazine derivatives led the author to undertake an ophthalmological investigation in two psychiatric hospitals in The Netherlands. The pharmacological actions of phenothiazine preparations are listed and a survey of the phenothiazine derivatives which are at present in use is given. Some retinotoxic substances are discussed and a survey is given of the literature on the ocular complications of phenothiazine therapy. The eyes of 561 patients were examined. of whom 541 are included in this study. 343 of these patients(63.4 %) were found to have retinopathy. The correlation between the retinopathy and the total dose of phenothiazine preparations taken. and between the retinopathy and the duration of treatment. was highly significant. The correlation between the retinopathy and the average daily dose taken was significant. The retinopathy was associated with a reduced standing potential of the eye. as determined by electro-oculography. It was possibly responsible for diminished visual acuity in some cases, and for an abnormally large proportion of protans in the group of patients with colour defects. It was not possible to ascribe a more severe retinotoxic action to one or more specific phenothiazine derivatives than to others. In the author's opinion regular examination of the eyes of patients who are being treated with phenothiazine preparations in high dosage and for for a long period of time is indicated

Learning invariances with stationary subspace analysis

Recently, a novel subspace decomposition method, termed 'Stationary Subspace Analysis' (SSA), has been proposed by Bünau et al. [10]. SSA aims to find a linear projection to a lower dimensional subspace such that the distribution of the projected data does not change over successive epochs or sub-datasets. We show that by modifying the loss function and the optimization procedure we can obtain an algorithm that is both faster and more accurate. We discuss the problem of indeterminacies and provide a lower bound on the number of epochs that is needed. Finally, we show in an experiment with simulated image patches, that SSA can be used favourably in invariance learning

Direct density ratio estimation with dimensionality reduction

Methods for directly estimating the ratio of two probability density functions without going through density estimation have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, conditional density estimation, feature selection, and independent component analysis. However, even the state-of-the-art density ratio estimation methods still perform rather poorly in high-dimensional problems. In this paper, we propose a new density ratio estimation method which incorporates dimensionality reduction into a density ratio estimation procedure. Our key idea is to identify a low-dimensional subspace in which the two densities corresponding to the denominator and the numerator in the density ratio are significantly different. Then the density ratio is estimated only within this low-dimensional subspace. Through numerical examples, we illustrate the effectiveness of the proposed method