A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data

The financial risk not only affects the development of the company itself, but also affects the economic development of the whole society; therefore, the financial risk assessment of company is an important part. At present, numerous methods of financial risk assessment have been researched by scholars. However, most of the extant methods neither integrated fuzzy sets with quantitative analysis, nor took into account the historical data of the past few years

Parameters affecting tumour control and toxicity in oesophageal cancer: a multi-dimensional outcome analysis

The work contained within this thesis explores the relationship between clinical and technological parameters of radiotherapy treatment planning and patient outcome in patients who were treated for cancer of the oesophagus as part of the SCOPE 1 clinical trial. However the methods and concepts of the work could also have applications at other tumour sites. By developing ideas from previous studies, a novel method of applying a conformity index found a significant relationship between the quality of a radiotherapy plan and patient outcome in terms of overall survival. Furthermore it was found that the plan quality could be improved by utilising a relatively new method of dose delivery. This dose delivery method also allowed the improving of tumour control via dose escalation to be explored via radiobiological modelling. The results of this work showed that although the probability of controlling the tumour is increased, there is also a significantly higher risk of increased gastric toxicity for patients with lower oesophageal tumours. Interfraction gastric movement was also investigated with the end result being a recommendation for stomach movement and toxicity to be minimised by using a pre-treatment protocol. This is being taken forward in a nationwide multicentre clinical trial. Finally a texture analysis software package was used to investigate whether there was relationship between the image heterogeneity parameters of computed tomography images and patient outcome. This work could potentially aid the decision making process of radiotherapy treatment, allowing a more informed judgement to be made on the most beneficial treatment for the patient

Advances towards the use of radical radiotherapy in malignant pleural mesothelioma

Background: The complex shape of the pleural cavity and the close proximity of normal radiosensitive structures render the delivery of radical radiotherapy in malignant pleural mesothelioma (MPM) challenging. However, the advent of conformal, intensity modulated radiotherapy (IMRT), where dose is selectively delivered to the tumour whilst sparing normal tissues, can facilitate safe dose escalation. SYSTEMS-2 is the only randomised controlled trial of radiotherapy dose escalation to be attempted in MPM and is comparing the palliative efficacy of two hypofractionated radiotherapy regimes to sites of pain using conformal techniques. Although traditionally associated with unacceptable late normal tissue toxicity, the success of stereotactic radiotherapy (SABR) and the discovery that two common malignancies exhibit low α/β ratios, has enhanced the popularity of hypofractionated regimes. While the radiobiology of MPM is not well understood, its slow growth and apparent radioresistance suggests that it may exhibit a low α/β ratio and therefore that it may respond more favourably to dose hypofractionation. Aims of thesis: To investigate the possibility of further radiotherapy dose escalation in MPM, beyond that delivered in the SYSTEMS-2 study. Methods: I. Novel radiotherapy dose constraints were generated for use in the SYSTEMS-2 study and tested on five patients from the SYSTEMS study. II. Multi criteria optimisation (MCO) software was used to assess whether the original dose escalated radiotherapy plans for the Glasgow cohort of SYSTEMS-2 could be improved, without compromising target volume coverage. III. A clinically relevant 3D in vitro spheroid model was used to investigate the radiobiology of two independent MPM cell lines (H2052 and 211H). Spheroids were established and exposed to the same total dose of ionising radiation (IR) delivered in different doses per fraction. Data was used to investigate response to dose fractionation and to estimate the α/β ratio of this tumour. IV. The response of H2052 and 211H spheroids to two radiosensiting agents was investigated in combination with fractionated radiotherapy. Spheroids were incubated with increasing concentrations of either NU7441 (a DNA-PKcs inhibitor) or A1331852 (a BH3 mimetic) before being exposed to fractionated IR. The immunohistochemical (IHC) expression of DNA-PKcs and Bcl-xL was explored in diagnostic biopsies obtained from MPM patients to investigate clinical validity of the targets. V. IHC expression of nine proteins, selected for their potential to impact on radioresponse, was analysed in diagnostic tumour tissue collected from SYSTEMS and SYSTEMS-2 patients. Expression data was correlated with baseline clinical trial data in all patients, and with clinical trial outcome data from SYSTEMS patients. Results: I. Initial planning studies showed that none of the five SYSTEMS patients met all of the SYSTEMS-2 dose constraints, but the plans were deemed to be potentially clinically acceptable and the constraints were taken forward in the trial. The value of familiarity with a planning technique was evidenced by the fact that all constraints were achieved when the cases were re-planned by the same staff member in April 2019. II. MCO re-planning of dose escalated SYSTEMS-2 plans achieved clinically significant dose reductions to organs at risk (OAR) without compromising target volume coverage in 13/20 cases. Plans which did not meet OAR constraints or conform to the prescribed target volume coverage may still have been clinically acceptable. III. In vitro studies confirmed that growth of MPM spheroids can be delayed by IR. Spheroids demonstrated sensitivity to changes in dose per fraction, with the greatest volume reductions observed in hypofractionated radiotherapy regimes. This data implies that these MPM cell lines may exhibit a low α/β ratio, a suggestion which was further supported by in vitro multi-fraction IR studies. IV. Data suggest that NU7441 and A1331852 are potent radiosensitisers of MPM spheroids and that both are valid clinical targets in MPM. The supposition that a BH3 mimetic may offer tumour specific radiosensitisation, combined with the observation that A1331852 demonstrated greatest efficacy with hypofractionated IR, suggests that this agent may be clinically valuable in the radiosensitisation of MPM. V. No statistically significant correlations were found between baseline clinical characteristics and expression of the proteins of interest and no potential biomarkers of radiosensitisation were identified in the SYSTEMS cohort. Conclusions: Novel dose constraints are being used to facilitate the delivery of hypofractionated, dose escalated palliative radiotherapy in the SYSTEMS-2 study. Results from this trial may guide future dose escalation in this disease and data from MCO planning studies suggest that further dose escalation to the target volume may be feasible without breaching OAR limits. In vitro studies suggest that MPM is sensitive to IR, responds more effectively to dose hypofractionation and may have a low α/β ratio. This data may be helpful in determining dose and fractionation regimes in future MPM radiotherapy trials. Combination of BH3 mimetics with IR may provide MPM specific radiosensitisation, achieving greatest efficacy with dose hypofractionation. Ongoing IHC analysis of tumour samples from the SYSTEMS-2 study may identify a biomarker of radiotherapy response which would be helpful in guiding radiotherapy treatment decisions for future patients. In summary, this thesis has investigated ways in which radiotherapy could be delivered with radical intent in MPM. Practical aspects of radiotherapy planning and delivery have been considered and are presented in conjunction with laboratory data to demonstrate how technical advances can be combined with an appreciation of disease radiobiology to facilitate radical treatment

Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer

Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues. Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice. Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings. This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer. Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis. Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene. Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis