Towards Unsupervised Detection of Process Models in Healthcare.

Process mining techniques can play a significant role in understanding healthcare processes by supporting analysis of patient records in electronic health record systems. Healthcare processes are complex and patterns of care may vary considerably within similar cohorts of patients. Process mining often creates "spaghetti" models and require significant domain expert input to refine. Machine learning approaches such as Hidden Markov Models (HMM) may assist this refinement process. HMMs have been advocated for patient pathways clustering purposes; however these models can also be utilized for detecting hidden processes to help event abstraction. We explore use of an unsupervised method for detecting hidden healthcare sub-processes using HMMs, in particular the Viterbi algorithm. We describe an approach to enrich the event log with HMM-derived states and remodeling the healthcare processes as state transitions using a process mining tool. Our method is applied to event data for 'Altered Mental Status' patients that was extracted from a US hospital database (MIMIC-III). The results are promising and show a successful reduction of model complexity and detection of several hidden processes unsupervised by a domain expert.

In Vitro Engineering of High Modulus Cartilage-Like Constructs.

To date, the outcomes of cartilage repair have been inconsistent and have frequently yielded mechanically inferior fibrocartilage, thereby increasing the chances of damage recurrence. Implantation of constructs with biochemical composition and mechanical properties comparable to natural cartilage could be advantageous for long-term repair. This study attempted to create such constructs, in vitro, using tissue engineering principles. Bovine synoviocytes were seeded on nonwoven polyethylene terephthalate fiber scaffolds and cultured in chondrogenic medium for 4 weeks, after which uniaxial compressive loading was applied using an in-house bioreactor for 1 h per day, at a frequency of 1 Hz, for a further 84 days. The initial loading conditions, determined from the mechanical properties of the immature constructs after 4 weeks in chondrogenic culture, were strains ranging between 13% and 23%. After 56 days (sustained at 84 days) of loading, the constructs were stained homogenously with Alcian blue and for type-II collagen. Dynamic compressive moduli were comparable to the high end values for native cartilage and proportional to Alcian blue staining intensity. We suggest that these high moduli values were attributable to the bioreactor setup, which caused the loading regime to change as the constructs developed, that is, the applied stress and strain increased with construct thickness and stiffness, providing continued sufficient cell stimulation as further matrix was deposited. Constructs containing cartilage-like matrix with response to load similar to that of native cartilage could produce long-term effective cartilage repair when implanted.

A Nondestructive Method to Distinguish the Internal Constituent Architecture of the Intervertebral Discs Using 9.4 Tesla Magnetic Resonance Imaging.

STUDY DESIGN: An in vitro study of the intervertebral disc (IVD) structure using 9.4T magnetic resonance imaging (MRI). OBJECTIVE: Investigate the potential of ultrahigh-field strength MRI for higher quality 3-dimensional (3D) volumetric MRI datasets of the IVD to better distinguish structural details. SUMMARY OF BACKGROUND DATA: MRI has the advantages of being nondestructive and 3D in comparison to most techniques used to obtain the structural details of biological tissues, however, its poor image quality at higher resolution is a limiting factor. Ultrahigh-field MRI could improve the imaging of biological tissues but the current understanding of its application for spinal tissue is limited. METHODS: 2 ovine spinal segments (C7-T1, T2-T3) containing the IVD were separately imaged using 2 sequences; 3D spin echo (multislice-multiecho) pulse sequence for the C7-T1 sample and 3D gradient echo (fast-low-angle-shot) pulse sequence for the T2-T3 sample. The C7-T1 sample was subsequently decalcified and imaged again using the same scanning parameters. Histological sections obtained from the decalcified sample were stained followed by digital scanning. Observations from corresponding MRI slices and histological sections were compared as a method of confirmation of morphology captured under MRI. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and relative-contrast values were calculated for quantitative evaluation of image quality. RESULTS: Measurements from histology sections and corresponding MRI slices matched well. Both sequences revealed finer details of the IVD structure. Under the spin echo sequence, the annulus lamellae architecture was distinguishable and the SNR and CNR values were higher. The relative contrast was considerably higher between high (nucleus) and low (bone) signal constituents, but between the nucleus and the annulus the relative contrast was low. Under the gradient echo sequence, although the relative contrasts between constituents were poor, the fiber orientation was clearly manifested. CONCLUSION: The obtained positive results demonstrate the potential of ultrahigh-field strength MRI to nondestructively capture the IVD structure. LEVEL OF EVIDENCE: N/A.

Segmentation of 3D vasculatures for interventional radiology simulation.

Training in interventional radiology is slowly shifting towards simulation which allows the repetition of many interventions without putting the patient at risk. Accurate segmentation of anatomical structures is a prerequisite of realistic surgical simulation. Therefore, our aim is to develop a generic approach to provide fast and precise segmentation of various virtual anatomies covering a wide range of pathology, directly from patient CT/MRA images. This paper presents a segmentation framework including two segmentation methods: region model based level set segmentation and hierarchical segmentation. We compare them to an open source application ITK-SNAP which provides similar approaches. The subjective human influence such as inconsistent inter-observer errors and aliasing artifacts etc. are analysed. The proposed segmentation techniques have been successfully applied to create a database of various anatomies with different pathologies, which is used in computer-based simulation for interventional radiology training.

PlasmoPredict: a gene function prediction website for Plasmodium falciparum.

The genome sequence of the malaria parasite Plasmodium falciparum was published in 2002 and revealed that approximately 60% of its genes could not be assigned a function. Eight years later the majority of P. falciparum proteins are still of unknown function. We therefore present PlasmoPredict, an easy-to-use online gene function prediction tool that integrates a wide range of functional genomics data for P. falciparum to aid in the annotation of these genes.

Tracking with virtual slides: a tool to study diagnostic error in histopathology.

AIMS: To determine the reasons for diagnostic error by virtual slides which allow unsupervised study of diagnosis and error. METHODS AND RESULTS: Software was developed to produce visualizations of the diagnostic track followed by pathologists as they viewed virtual slides. These showed the diagnostic path in four dimensions (x, y, time and zoom), areas studied for >1000 ms, and included pathologists' comments about the areas viewed. The system was used to study two trainee and two expert pathologists diagnosing 60 Barrett's oesophageal biopsy specimens. Comparisons of the diagnostic tracks showed the reason for errors. Forty-six cases had an expert consensus diagnosis. The trainees made errors in 21 and 15 cases, respectively, of which 11 and nine were clinically significant. Errors were made across the whole spectrum of diagnoses from negative to intramucosal carcinoma. Detailed examination of the tracks showed that in all errors there was incorrect interpretation of information; in three errors there was an additional failure to identify diagnostic features. CONCLUSIONS: Tracking with virtual slides is a useful tool in studying diagnosis and error, which has the potential for use in training and assessment.

Liver segmentation using automatically defined patient specific B-spline surface models.

This paper presents a novel liver segmentation algorithm. This is a model-driven approach; however, unlike previous techniques which use a statistical model obtained from a training set, we initialize patient-specific models directly from their own pre-segmentation. As a result, the non-trivial problems such as landmark correspondences, model registration etc. can be avoided. Moreover, by dividing the liver region into three sub-regions, we convert the problem of building one complex shape model into constructing three much simpler models, which can be fitted independently, greatly improving the computation efficiency. A robust graph-based narrow band optimal surface fitting scheme is also presented. The proposed approach is evaluated on 35 CT images. Compared to contemporary approaches, our approach has no training requirement and requires significantly less processing time, with an RMS error of 2.44 +/- 0.53 mm against manual segmentation.

A 6DOF gravity compensation scheme for a phantom premium using a neural network.

Current uses of haptic hardware such as the Phantom Premium 6DOF for surgical simulators lack the desired interface transparency and could cause artefacts in the training regime of a student training on a simulator. This problem is addressed and two neural networks are used to find a mapping between handle coordinates and orientation and force output required to counteract gravitational forces. A close fit to the data is achieved for both networks (errors of 0.00149 and 0.0157 between training and predicted forces) and 3DOF gravity compensation is achieved. A 6DOF simulator is created but requires further work to improve it accuracy.