Modular Clinical Decision Support Networks (MoDN)-Updatable, interpretable, and portable predictions for evolving clinical environments.

Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms 'monolithic' baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data

Should we treat Blastocystis sp,? A double-blind placebo-controlled randomized pilot trial

Contexte Blastocystis sp. est un protiste distribué dans le monde entier qui colonise les intestins des humains et d'une grande variété d'animaux. On ne sait pas s'il s'agit d'un simple commensal ou d'un parasite infectieux dont l'éradication s'impose. L'objectif principal de cette étude était d'évaluer l'utilité du métronidazole chez les patients présentant des symptômes gastro­ intestinaux hébergeant uniquement Blastocystis sp. En outre, nous avons exploré si le sous­ type de Blastocystis ou l'infection parasitaire concomitante détectée par réaction en chaîne par polymérase (PCR) peut influencer le résultat du traitement. Méthodes Nous avons inclus des adultes présentant des symptômes gastro-intestinaux persistants(&gt;14 jours) qui consultaient un médecin de soins primaires et chez qui la microscopie des selles ne révélait que Blastocystis sp. Les patients éligibles ont été randomisés pour recevoir 10 jours de métronidazole ou de placebo, suivis d'un crossover si les symptômes persistaient. Le outcome primaire était la consistance normale des selles. Les outcomes secondaires étaient les changements dans les autres symptômes abdominaux (ballonnements, flatulences, douleurs abdominales, nombre de selles quotidiennes) et le bien-être général. Après la phase clinique de l'étude, les sous-types de Blastocystis ont été déterminés par séquençage PCR et les échantillons de selles ont été testés pour 11 autres protozoaires avec une PCR in-house. Résultats Nous avons identifié 581 patients ambulatoires inclure dans l'étude, dont 50 répondaient aux critères d'inclusion. Il n'y a pas eu de différence dans le outcome primaire, ni dans aucun des outcomes secondaires entre les sujets traités par métronidazole et ceux traités par placebo. Les sous-types de Blastocystis les plus fréquents étaient ST4 (11/36) et ST2 (10/36). La PCR in­ house était positive pour d'autres protozoaires chez 25% (10/40) des patients. Nous avons identifié Dientamoebafragilis chez 5, Entamoeba dispar chez 3 et Cyclospora cayetanensis chez 2 patients. Une analyse stratifiée en fonction du sous-type de Blastocystis ou de la présence d'autres protozoaires n'a montré aucune différence significative dans le résultat du traitement avec le métronidazole ou le placebo. Conclusions Chez les patients infectés par Blastocystis sp., le métronidazole, comparé au placebo, n'a pas permis d'améliorer les symptômes gastro-intestinaux, indépendamment du sous-type ou de la coinfection par d'autres protozoaires non détectée au microscope

AUC-ROC curves for diagnosis.

Mean and standard deviation of the AUC and ROC curve for diagnosis prediction computed on the test set, using bootstrapping. (TIFF)</p

Imperfectly interoperable (IIO) data sets.

From the 3,192-patient CDSS-derived data set, we create two training sets with three levels of imperfect feature overlap (60, 80 and 90%) compared with perfect interoperability (100%). In our experiments, the owner of a small ‘target’ data set (fewer patients) wants to benefit from a larger ‘source’ data set without having access to this data. The ‘source’ may lack several features that are available in the ‘target’, yielding several levels of ‘imperfect interoperability’. We construct validation sets with and without these missing features, as well as a held-out test set. The F1 scores we report in this paper are averages over five randomized folds of this data-splitting procedure.</p

Two dimensional t-SNE decomposition of the <i>state</i> vector for the patients of the training set.

The projection for each data point is overlaid with a color representing the true diagnosis/diagnoses of the patients. URTI: Upper Respiratory Tract Infection, FWS: Fever Without Source.</p

MoDN calibration curve of the predictions on the test set after having encoded all available features, when the model is additionally trained for feature decoding.

MoDN is calibrated close to the perfect calibration line. (TIFF)</p

Experimental set up for porting MoDN modules in IIO settings.

MoDN is tested in two “model porting experiments” (grey), where modules are ported from a larger source data set (A) for fine-tuning or updating on a smaller, imperfectly interoperable target data set (B). The two experiments represent either a scenario where a new user with different resources starts using a CDSS or where an existing user gains new resources and would like to merge training. Three baselines are proposed. Static (blue) where modules trained in A are directly tested in B, thus not considering additional IIO features. Local (green) where modules are only trained on the target data set B, thus without insights from the larger source data set. Global (purple) is the ideal but unlikely, scenario of when all data can be shared between A and B and the modules are trained on the union of data (A ∪ B). The modularised fine-tuning experiment, pre-trains on A and then fine-tunes all modules (for all features) on B (thus personalising the modules trained on A). The modularised update experiment, pre-trains the blue modules on A and then adds modules specific to the new IIO features (in green) which have been independently trained on B (thus preserving the validity of the modules trained on A). The colors of the MoDN modules illustrate their training on distinct data sets and their potential re-combination in the porting experiments. In particular, the modules trained on A (blue) and fine-tuned on B (green) are thus depicted in teal.</p

MoDN calibration curve of the predictions on the test set after having encoded all available features.

MoDN calibration curve of the predictions on the test set after having encoded all available features.</p

Implementation details, feature decoding, idempotence and calibration.

Implementation details, feature decoding, idempotence and calibration.</p