Using Topological Data Analysis for diagnosis pulmonary embolism

Pulmonary Embolism (PE) is a common and potentially lethal condition. Most patients die within the first few hours from the event. Despite diagnostic advances, delays and underdiagnosis in PE are common.To increase the diagnostic performance in PE, current diagnostic work-up of patients with suspected acute pulmonary embolism usually starts with the assessment of clinical pretest probability using plasma d-Dimer measurement and clinical prediction rules. The most validated and widely used clinical decision rules are the Wells and Geneva Revised scores. We aimed to develop a new clinical prediction rule (CPR) for PE based on topological data analysis and artificial neural network. Filter or wrapper methods for features reduction cannot be applied to our dataset: the application of these algorithms can only be performed on datasets without missing data. Instead, we applied Topological data analysis (TDA) to overcome the hurdle of processing datasets with null values missing data. A topological network was developed using the Iris software (Ayasdi, Inc., Palo Alto). The PE patient topology identified two ares in the pathological group and hence two distinct clusters of PE patient populations. Additionally, the topological netowrk detected several sub-groups among healthy patients that likely are affected with non-PE diseases. TDA was further utilized to identify key features which are best associated as diagnostic factors for PE and used this information to define the input space for a back-propagation artificial neural network (BP-ANN). It is shown that the area under curve (AUC) of BP-ANN is greater than the AUCs of the scores (Wells and revised Geneva) used among physicians. The results demonstrate topological data analysis and the BP-ANN, when used in combination, can produce better predictive models than Wells or revised Geneva scores system for the analyzed cohortComment: 18 pages, 5 figures, 6 tables. arXiv admin note: text overlap with arXiv:cs/0308031 by other authors without attributio

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed to identify, adopting topological data analysis, the risk factors for therapeutic failure (in-hospital death or intensive care unit transfer), the in-hospital occurrence of stroke/TIA and major bleeding in a cohort of critically ill patients with pre-existing atrial fibrillation admitted to a stepdown unit; to engineer newer prediction models based on machine learning in the same cohort. We selected all medical patients admitted for critical illness and a history of pre-existing atrial fibrillation in the timeframe 01/01/2002-03/08/2007. All data regarding patients' medical history, comorbidities, drugs adopted, vital parameters and outcomes (therapeutic failure, stroke/TIA and major bleeding) were acquired from electronic medical records. Risk factors for each outcome were analyzed adopting topological data analysis. Machine learning was used to generate three different predictive models. We were able to identify specific risk factors and to engineer dedicated clinical prediction models for therapeutic failure (AUC: 0.974, 95%CI: 0.934-0.975), stroke/TIA (AUC: 0.931, 95%CI: 0.896-0.940; Brier score: 0.13) and major bleeding (AUC: 0.930:0.911-0.939; Brier score: 0.09) in critically-ill patients, which were able to predict accurately their respective clinical outcomes. Topological data analysis and machine learning techniques represent a concrete viewpoint for the physician to predict the risk at the patients' level, aiding the selection of the best therapeutic strategy in critically ill patients affected by pre-existing atrial fibrillation

A neural network-based retinal imaging interface for optic disc localization in ophthalmic analyses

An automatic detection of the position of Optic Disc (OD) is a fundamental step in the analysis of retinal images, due to the fact that precise determinations about the localization of Optic Nerve and OD in retinal images reveals necessary to examine the severity of some diseases. Changes in the OD often indicate a pathologic progression. On the basis of multiple pixel determinations of a multiple Processor on captured fundus images, in this work a NN-based Positioning Interface, constituted by a Retinal Imaging System, a Neural Validity Classifier and a Positioning Processor for an accurate localization of the Reference point of OD, is presented. More in detail, the locations of multiple candidates are accurately validated by synthesizing a Neural Network behaving as a Classifier of Validity for regular/abnormal candidate reference points. Then, a Positioning Processor, which considers only validated midpoints, adopts the most suitable point as the Reference point of the OD for subsequent ophthalmic analyses. Simulation results are reported on selected fundus oculi images

Adalimumab as a potential cause of drug-induced thrombocytopaenic microangiopathy

We report the case of a 63-year-old male patient admitted to our emergency department for dyspnoea, peripheral oedema, severe diarrhoea and asthenia. History revealed Crohn’s disease (CD) submitted to several intestinal surgical resections in the previous years. He recently started a treatment with adalimumab for the control of CD. Laboratory tests at the admission revealed severe haemolytic anaemia and thrombocytopaenia. Haptoglobin levels were low, schistocyte count was markedly increased. In the suspect of thrombotic microangiopathy, he was admitted to our internal medicine department where we urgently started plasma exchange (PEX). We observed normal ADAMTS-13 activity in absence of Shiga toxin or enterotoxic Escherichiacoli at stool tests. Despite a diagnosis of atypical haemolytic–uraemic syndrome, we observed full platelet count recovery and schistocytes normalisation after the fourth PEX. We then put a diagnosis of adalimumab-induced thrombocytopaenic microangiopathy. Adalimumab was withdrawn. We did not observe relapses in the following 3 months.</jats:p