Deep Learning in the Prediction of Clinically Significant Outcomes in Stroke and General Medicine Patients

Background The need for novel strategies to improve outcome prediction and the categorisation of unstructured medical data will increase as the demands on hospitals, associated with the increasing age and complexity of admitted patients, continues to rise. Stroke is a highly specialised field, in which key performance indicators and discharge planning have an important role. General medicine is a field that encompasses a wide variety of multisystem and undifferentiated illnesses. It is possible that machine learning, in particular deep learning, may be able to assist with the prediction of clinically significant outcomes both in areas with highly specialised assessment and treatment considerations (such as stroke), as well as fields with a diverse mix of medical conditions and comorbidities (such as general medicine). Method This thesis comprised of studies using machine learning to predict clinically significant outcomes in stroke and general medicine inpatients. Initially a systematic review was conducted to evaluate the existing literature regarding the prediction of one such clinically significant outcome, length of stay, in medical inpatients. Derivation and validation studies were conducted to develop models for stroke inpatients to aid with the prediction of discharge independence, survival to discharge, discharge destination and length of stay. Stroke key performance indicator-automated extraction and clinical coding categorisation were undertaken in studies employing techniques including natural language processing. Natural language processing was applied to general medicine free-text data in pilot, derivation, and validation studies in the prediction of outcomes including discharge timing. Results The systematic review identified a particular lack of prospective validation studies for machine learning models developed to aid with length of stay prediction in medical inpatients. The stroke model derivation, prospective and external validation studies demonstrated the successful use of machine learning models in the prediction of outcomes relevant to discharge planning for stroke patients. For example, an area under the receiver operator curve (AUC) of 0.85 and 0.87 was achieved for the prediction of independence at the time of discharge in the prospective and external validation datasets respectively. The automated collection of stroke key performance indicators and the application of natural language processing to stroke clinical coding also demonstrated performance as high as an AUC of 0.95-1.00 in key performance indicator classification tasks. The general medicine pilot, derivation, prospective and external validation studies demonstrated the development and success of artificial neural networks in the prediction of discharge within the next 48 hours (AUC 0.78 and 0.74 in the prospective and external validation datasets respectively). Conclusions Machine learning models (including deep learning) can successfully predict clinically significant outcomes in stroke and general medicine patients.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202

Pharmacokinetics and pharmacodynamics utilizing unbound target tissue exposure as part of a disposition-based rationale for lead optimization of benzoxaboroles in the treatment of Stage 2 Human African Trypanosomiasis

This review presents a progression strategy for the discovery of new anti-parasitic drugs that uses in vitro susceptibility, time-kill and reversibility measures to define the therapeutically relevant exposure required in target tissues of animal infection models. The strategy is exemplified by the discovery of SCYX-7158 as a potential oral treatment for stage 2 (CNS) Human African Trypanosomiasis (HAT). A critique of current treatments for stage 2 HAT is included to provide context for the challenges of achieving target tissue disposition and the need for establishing pharmacokinetic-pharmacodynamic (PK-PD) measures early in the discovery paradigm. The strategy comprises 3 stages. Initially, compounds demonstrating promising in vitro activity and selectivity for the target organism over mammalian cells are advanced to in vitro metabolic stability, barrier permeability and tissue binding assays to establish that they will likely achieve and maintain therapeutic concentrations during in-life efficacy studies. Secondly, in vitro time-kill and reversibility kinetics are employed to correlate exposure (based on unbound concentrations) with in vitro activity, and to identify pharmacodynamic measures that would best predict efficacy. Lastly, this information is used to design dosing regimens for pivotal pharmacokinetic-pharmacodyamic studies in animal infection model

SCYX-7158, an Orally-Active Benzoxaborole for the Treatment of Stage 2 Human African Trypanosomiasis

Human African trypanosomiasis (HAT) is caused by infection with the parasite Trypanosoma brucei and is an important public health problem in sub-Saharan Africa. New, safe, and effective drugs are urgently needed to treat HAT, particularly stage 2 disease where the parasite infects the brain. Existing therapies for HAT have poor safety profiles, difficult treatment regimens, limited effectiveness, and a high cost of goods. Through an integrated drug discovery project, we have discovered and optimized a novel class of boron-containing small molecules, benzoxaboroles, to deliver SCYX-7158, an orally active preclinical drug candidate. SCYX-7158 cured mice infected with T. brucei, both in the blood and in the brain. Extensive pharmacokinetic characterization of SCYX-7158 in rodents and non-human primates supports the potential of this drug candidate for progression to IND-enabling studies in advance of clinical trials for stage 2 HAT

The role of qualitative research in adding value to a randomised controlled trial: lessons from a pilot study of a guided e-learning intervention for managers to improve employee wellbeing and reduce sickness absence

The GEM study was funded by the National Institute for Health Research Public Health Research Programme (project number 10/3007/06)

New functional and structural insights from updated mutational databases for complement factor H, Factor I, membrane cofactor protein and C3

aHUS (atypical haemolytic uraemic syndrome), AMD (age-related macular degeneration) and other diseases are associated with defective AP (alternative pathway) regulation. CFH (complement factor H), CFI (complement factor I), MCP (membrane cofactor protein) and C3 exhibited the most disease-associated genetic alterations in the AP. Our interactive structural database for these was updated with a total of 324 genetic alterations. A consensus structure for the SCR (short complement regulator) domain showed that the majority (37%) of SCR mutations occurred at its hypervariable loop and its four conserved Cys residues. Mapping 113 missense mutations onto the CFH structure showed that over half occurred in the C-terminal domains SCR-15 to -20. In particular, SCR-20 with the highest total of affected residues is associated with binding to C3d and heparin-like oligosaccharides. No clustering of 49 missense mutations in CFI was seen. In MCP, SCR-3 was the most affected by 23 missense mutations. In C3, the neighbouring thioester and MG (macroglobulin) domains exhibited most of 47 missense mutations. The mutations in the regulators CFH, CFI and MCP involve loss-of-function, whereas those for C3 involve gain-of-function. This combined update emphasizes the importance of the complement AP in inflammatory disease, clarifies the functionally important regions in these proteins, and will facilitate diagnosis and therapy

Artificial intelligence and clinical deterioration

Purpose of review: To provide an overview of the systems being used to identify and predict clinical deterioration in hospitalised patients, with focus on the current and future role of artificial intelligence (AI). Recent findings: There are five leading AI driven systems in this field: the Advanced Alert Monitor (AAM), the electronic Cardiac Arrest Risk Triage (eCART) score, Hospital wide Alert Via Electronic Noticeboard, the Mayo Clinic Early Warning Score, and the Rothman Index (RI). Each uses Electronic Patient Record (EPR) data and machine learning to predict adverse events. Less mature but relevant evolutions are occurring in the fields of Natural Language Processing, Time and Motion Studies, AI Sepsis and COVID-19 algorithms. Summary: Research-based AI-driven systems to predict clinical deterioration are increasingly being developed, but few are being implemented into clinical workflows. Escobar et al. (AAM) provide the current gold standard for robust model development and implementation methodology. Multiple technologies show promise, however, the pathway to meaningfully affect patient outcomes remains challenging

Magnetic resonance imaging and positron emission tomography in anti-NMDA receptor encephalitis: A systematic review.

Due to a variety of clinical manifestations anti-N-methyl-d-aspartate (NMDA) receptor encephalitis may be difficult to diagnose. Magnetic resonance imaging (MRI) may be used as a component of the workup for encephalopathy. However, the use of MRI in anti-NMDA encephalitis is complicated by wide-ranging reports regarding the frequency of normal MRI findings in this disease. Positron emission tomography (PET) is a modality of imaging that may assess functional rather than structural disturbances. Therefore, this review was conducted to summarise published studies regarding the use of MRI and PET in the diagnosis of anti-NMDA receptor encephalitis. The terms (MR OR magnetic resonance OR PET OR positron emission tomography) AND (NMDA encephalitis OR N-methyl-d-aspartate encephalitis) were used to search the databases PubMed, EMBASE and Scopus on 10/5/2017. These searches returned 1534 results. Sixty studies met the inclusion criteria. The results indicated that fewer than half of MRIs in anti-NMDA receptor encephalitis show abnormal findings. When abnormal findings are present they most commonly include T2/FLAIR medial temporal and frontal hyperintensity, and leptomeningeal contrast enhancement. Cortical grey matter changes were reported in the same number of patients as subcortical white matter changes. The only MRI finding with prognostic significance at this stage is progressive cerebellar atrophy. FDG-PET has been assessed in a few small studies and can demonstrate abnormalities in cases where MRI does not. Further research should aim for larger sample sizes and to report (and attempt to control for) the time between symptom onset and the scan being conducted, and pre-imaging treatments

Gender analysis and social change: Testing the water

Copyright © 2006 Policy and Society Associates (APSS)This paper uses preliminary findings from an ARC-funded Linkage grant to speculate on the requirements for producing gender analysis as a change process. Gender analysis, commonly associated with gender mainstreaming, is a methodology aimed at ensuring that all projects, programs and policies are gender-inclusive and gendersensitive. In the Linkage study existing models of gender analysis taken from Canada and The Netherlands are being tested for their usefulness in selected agencies in South Australia and Western Australia. The goal is to design gender analysis processes appropriate to specific Australian contexts. This paper reflects on the challenges and obstacles encountered in the project to date. It focuses in particular on the importance of creating space for extended debate and discussion of the concepts and issues relevant to gender equality and social change. The authors describe this space as “somewhere in the middle”.Carol Bacchi, Joan Eveline, Jennifer Binns, Catherine Mackenzie and Susan Harwoo

sj-docx-2-caj-10.1177_08465371241227424 – Supplemental material for Imaging Features of Invasive Fungal Rhinosinusitis: A Systematic Review

Supplemental material, sj-docx-2-caj-10.1177_08465371241227424 for Imaging Features of Invasive Fungal Rhinosinusitis: A Systematic Review by Anni Chen, James Pietris, Stephen Bacchi, WengOnn Chan, Alkis J. Psaltis, Dinesh Selva and WanYin Lim in Canadian Association of Radiologists Journal</p