AI and standards for healthcare systems: FHIR, imaging, clinical decision support.
On 21 April 2020 NVIDIA and King's College London presented MONAI, a PyTorch library that gathers in one place the transforms, networks and metrics that every medical imaging lab had until now rewritten on its own.
Read moreTechnical notes on applying machine learning to paediatric short bowel syndrome cohorts: the shape of longitudinal clinical data, target definition, and the constraint of small sample size.
Read moreThe state of convolutional networks for skin-lesion classification in mid-2019: where the public benchmarks hold, and where integration into a laboratory workflow remains the open problem.
Read moreFHIR Release 4 (4.0.0, December 2018) is the first version with Normative content: the REST API, XML/JSON formats, base datatypes and a few clinical resources leave Trial Use with a backward-compatibility commitment. A technical note on the boundary of the Normative set and what changes for those building clinical systems.
Read moreThe nnU-Net preprint (arXiv 1809.10486) argues that, for medical image segmentation, a well-configured standard U-Net beats architectural variants. Notes on the data fingerprint, the three configurations and the provisional Medical Segmentation Decathlon results.
Read moreBy 2018, models on clinical records and diagnostic images beat the published baselines, but the constraint that decides everything is the data: scarcity, quality, treatment regime, and GDPR.
Read moreFrom 25 May 2018 processing health data starts from a prohibition and ten exhaustive derogations. The Article 9(2) derogation you invoke decides who reads the data, where it may travel, and when a clinical dataset must stay separate from a research one.
Read moreControlled clinical terminologies are not interchangeable: SNOMED CT describes the clinical picture, LOINC names observations and reports, ICD classifies for statistics and reimbursement. Boundaries, licensing and Italian adoption as of 2017.
Read moreSleep stages are derived from physiological signals read according to codified rules. Scoring standards, data exchange formats, actigraphy and the EU regulatory path for sleep-analysis software as of early 2017.
Read moreAutomatic sleep staging between the historical actigraphy rules and time-series classifiers on polysomnographic signals, constrained by class imbalance and clinical validation.
Read moreA technical read of the Medical Imaging Interaction Toolkit at release 2016.03: the ITK + VTK stack, DataStorage as the shared data model, BlueBerry sitting on the CTK Plugin Framework and CppMicroServices, and the limits that remain on the regulatory side.
Read moreThe technical rules for Italy's Electronic Health Record set out a federated architecture built on signed CDA R2 documents and IHE XDS.b/XCA profiles, with INI as an interoperability gateway between regional records.
Read moreA stable Reference Model underneath, clinician-governed archetypes in ADL on top. How openEHR keeps the software model separate from clinical concepts, and what that costs and yields for interoperability and maintenance.
Read moreFHIR DSTU 1 (v0.0.82) exposes clinical entities as REST resources in XML or JSON. Reading notes on its design choices against HL7 v2 and CDA R2.
Read moreA technical reading of DICOM in 2013: the object model, SOP Classes, DIMSE services, transfer syntaxes, and the move under way towards the REST services WADO-RS, QIDO-RS and STOW-RS. What you build with the Open Source toolkits.
Read moreDecree-law 179/2012 gives Italy's Electronic Health Record a national statutory basis while deferring its operational content to a Prime Ministerial decree that has yet to be issued. What Article 12 establishes, how it rests on the Digital Administration Code, and which technical questions remain open.
Read moreHow an HL7 v2 message is built: pipe-delimited encoding, segments, message types, MLLP transport, and why the variability between implementations is the real cost of hospital integration.
Read moreOpenCDS exposes clinical logic as an HL7 DSS web service that takes a virtual Medical Record and returns structured recommendations, with Drools as the execution engine. A technical note on the architecture, the authoring bottleneck and the limits as of 2012.
Read moreNotes on the JAMIA paper describing cTAKES (Savova et al., 2010): its UIMA annotator architecture, the UMLS dictionary lookup, NegEx-based negation, and the component figures measured on the Mayo corpus.
Read moreHow the National Library of Medicine's MetaMap links English biomedical text to Concept Unique Identifiers in the UMLS Metathesaurus: the lexical-variant pipeline, and the practical limits around ambiguity, language and coverage.
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