Neonatal sepsis is associated with most cases ofmortality in the neonatal intensive care unit. Major challengesin detecting sepsis using suitable biomarkers has lead people tolook for alternative approaches in the form of Machine Learningtechniques. In this project, Random Forest classification wasperformed on a sepsis data set provided by Karolinska Hospital.We particularly focused on tackling class imbalance in the datausing sampling and cost-sensitive techniques. We compare theclassification performances of Random Forests in six differentsetups; four using oversampling and undersampling techniques;one using cost-sensitive learning and one basic Random Forest.The performance with the oversampling techniques were betterand could identify more sepsis patients than the other setups.The overall performances were also good, making the methodspotentially useful in practice. / Neonatal sepsis är orsaken till majoriteten av mortaliteten i neonatal intensivvården. Svårigheten i att detektera sepsis med hjälp av biomarkörer har lett många att leta efter alternativa metoder. Maskininlärningstekniker är en sådan alternativ metod som har i senaste tider ökat i användning inom vård och andra sektorer. I detta project användes Random Forest klassifikations algoritmen på en sepsis datamängd given av Karolinska Sjukhuset. Vi fokuserade på att hantera klassimbalansen i datan genom att använda olika provtagningsoch kostnadskänsliga metoder. Vi jämförde klassificeringsprestanda för Random Forest med sex olika inställningar; fyra av de använde provtagingsmetoderna; en av de använde en kostnadskänslig metod och en var en vanlig Random Forest. Det visade sig att modellens prestanda ökade som mest med översamplings metoderna. Den generella klassificeringsprestandan var också bra, vilket gör Random Forests tillsammans med ingsmetoderna potentiellt användbar i praktiken. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

<p> The overall theme of my dissertation is applying frontier econometric models to interesting economic problems. The first chapter analyzes how individual consumption responds to permanent and transitory income shocks is limited by model misspecification and availability of data. The misspecification arises from ignoring unemployment risk while estimating income shocks. I employ the Heckman two step regression model to consistently estimate income shocks. Moreover, to deal with data sparsity, I propose identifying the partial consumption insurance and income and consumption volatility heterogeneities at the household level using Least Absolute Shrinkage and Selection Operator (LASSO). Using PSID data, I estimate partial consumption insurance against permanent shock of 63% and 49% for white and black household heads, respectively; the white and black household heads self-insure against 100% and 90% of the transitory income shocks, respectively. Moreover, I find income and consumption volatilities and partial consumption insurance parameters vary across time. In the second chapter I recast smooth structural break test proposed by Chen and Hong (2012), in a predictive regression setting. The regressors are characterized using the local to non-stationarity framework. I conduct a Monte Carlo experiment to evaluate the finite sample performance of the test statistic and examine an empirical example to demonstrate its practical application. The Monte Carlo simulations show that the test statistic has better power and size compared to the popular SupF and LM. Empirically, compared to SupF and LM, the test statistic rejects the null hypothesis of no structural break more frequently when there actually is a structural break present in the data. The third chapter is a collaboration with James Reeder III. We study the effects of using promotions to drive public policy diffusion in regions with polarized political beliefs. We estimate a model that allows for heterogeneous effects at the county-level based upon state-level promotional offerings to drive vaccine adoption during COVID-19. Central to our empirical application is accounting for the endogenous action of state-level agents in generating promotional schemes. To address this challenge, we synthesize various sources of data at the county-level and leverage advances in both the Bass Diffusion model and 10 machine learning. Studying the vaccine rates at the county-level within the United States, we find evidence that the use of promotions actually reduced the overall rates of adoption in obtaining vaccination, a stark difference from other studies examining more localized vaccine rates. The negative average effect is driven primarily by the large number of counties that are described as republican leaning based upon their voting record in the 2020 election. Even directly accounting for the population’s vaccine hesitancy, this result still stands. Thus, our analysis suggests that in the polarized setting of the United States electorate, more localized policies on contentious topics may yield better outcomes than broad, state-level dictates. </p>

Soil characterization provides the basic information necessary for understanding the physical, chemical, and biological properties of soils. Knowledge about soils can in turn be used to inform management practices, optimize agricultural operations, and ensure the continuation of ecosystem services provided by soils. However, current analytical standards for identifying each distinct property are costly and time-consuming. The optimization of laboratory grade technology for wide scale use is demonstrated by advances in a proximal soil sensing technique known as portable X-ray fluorescence spectrometry (pXRF). pXRF analyzers use high energy Xrays that interact with a sample to cause characteristic reflorescence that can be distinguished by the analyzer for its energy and intensity to determine the chemical composition of the sample. While pXRF only measures total elemental abundance, the concentrations of certain elements have been used as a proxy to develop models capable of predicting soil characteristics. This study aimed to evaluate existing models and model building techniques for predicting soil pH, texture, cation exchange capacity (CEC), soil organic carbon (SOC), total nitrogen (TN), and C:N ratio from pXRF spectra and assess their fittingness for California soils by comparing predictions to results from laboratory methods. Multiple linear regression (MLR) and random forest (RF) models were created for each property using a training subset of data and evaluated by R2 , RMSE, RPD and RPIQ on an unseen test set. The California soils sample set was comprised of 480 soil samples from across the state that were subject to laboratory and pXRF analysis in GeoChem mode. Results showed that existing data models applied to the CA soils dataset lacked predictive ability. In comparison, data models generated using MLR with 10-fold cross validation for variable selection improved predictions, while algorithmic modeling produced the best estimates for all properties besides pH. The best models produced for each property gave RMSE values of 0.489 for pH, 10.8 for sand %, 6.06 for clay % (together predicting the correct texture class 74% of the time), 6.79 for CEC (cmolc/kg soil), 1.01 for SOC %, 0.062 for TN %, and 7.02 for C:N ratio. Where R2 and RMSE were observed to fluctuate inconsistently with a change in the random train/test splits, RPD and RPIQ were more stable, which may indicate a more useful representation of out of sample applicability. RF modeling for TN content provided the best predictive model overall (R2 = 0.782, RMSE = 0.062, RPD = 2.041, and RPIQ = 2.96). RF models for CEC and TN % achieved RPD values >2, indicating stable predictive models (Cheng et al., 2021). Lower RPD values between 1.75 and 2 and RPIQ >2 were also found for MLR models of CEC, and TN %, as well as RF models for SOC. Better estimates for chemical properties (CEC, N, SOC) when compared to physical properties (texture), may be attributable to a correlation between elemental signatures and organic matter. All models were improved with the addition of categorical variables (land-use and sample set) but came at a great statistical cost (9 extra predictors). Separating models by land type and lab characterization method revealed some improvements within land types, but these effects could not be fully untangled from sample set. Thus, the consortia of characterizing bodies for ‘true’ lab data may have been a drawback in model performance, by confounding inter-lab errors with predictive errors. Future studies using pXRF analysis for soil property estimation should investigate how predictive v models are affected by characterizing method and lab body. While statewide models for California soils provided what may be an acceptable level of error for some applications, models calibrated for a specific site using consistent lab characterization methods likely provide a higher degree of accuracy for indirect measurements of some key soil properties.

Background. This research centers on tackling the serious global problem of trafficaccidents. With more than a million deaths each year and numerous injuries, it’svital to predict and prevent these accidents. By combining satellite images and dataon accidents, this study uses a mix of advanced learning methods to build a modelthat can foresee accidents. This model aims to improve how accurately we predictaccidents and understand what causes them. Ultimately, this could lead to betterroad safety, smoother maintenance, and even benefits for self-driving cars and insurance. Objective.The objective of this thesis is to create a predictive model that improvesthe accuracy of traffic accident severity forecasts by integrating satellite imagery andhistorical accident data and comparing this model with stand-alone data models.Through this hybrid approach, the aim is to enhance prediction precision and gaindeeper insights into the underlying factors contributing to accidents, thereby potentially aiding in the reduction of accidents and their resulting impact. Method.The proposed method involves doing a literature review to find currentimage recognition models and then experimentation by training a Logistic Regression, Random Forest, SVM classifier, VGG19, and the hybrid model using the CNNand VGG19 and then comparing their performance using metrics mentioned in thethesis work. Results.The performance of the proposed method is evaluated using various metrics, including precision, recall, F1 score, and confusion matrix, on a large datasetof labeled images. The results indicate that a high accuracy of 81.7% is achieved indetecting traffic accident severity through our proposed approach where the modelbuilt on individual structural data and image data got an accuracy of 58.4% and72.5%. The potential utilization of our proposed method can detect safe and dangerous locations for accidents. Conclusion.The predictive modeling of Traffic accidents are performed using thethree different types of datasets which are structural data, satellite images, and acombination of both. The finalized architectures are an SVM classifier, VGG19, anda hybrid input model using CNN and VGG19. These models are compared in orderto find the best-performing approach. The results indicate that our hybrid modelhas the best accuracy with 81.7% indicating a strong performance by the model.

The co-evolution of life and the environment stands as a cornerstone in Earth's 4.5-billion-year history. Environmental fluctuations have wielded substantial influence over biological evolution, while life forms have, in turn, reshaped Earth's surface and climate. This dissertation centers on a critical period in Earth's history—the Neoproterozoic Era—when profound environmental shifts potentially catalyzed pivotal eukaryotic evolutionary events. By delving deeper into Neoproterozoic paleoenvironments, I aim at a clearer understanding of life-environment co-evolution in this crucial era. The first chapter focuses on an important juncture—the transition from prokaryote to eukaryote dominance in marine ecosystems during the Tonian Period (1000 Ma to 720 Ma). To assess whether the availability of nitrate, an important macro-nutrient, played a critical role in this evolutionary event, nitrogen isotope compositions (δ¹⁵N) of marine carbonates from the early Tonian (ca. 1000 Ma to ca. 800 Ma) Huaibei Group in North China were measured. The data indicate nitrate limitation in early Neoproterozoic oceans. Further, a compilation of Proterozoic sedimentary δ¹⁵N data, together with box model simulations, suggest a ~50% increase in marine nitrate availability at ~800 Ma. Limited nitrate availability in early Neoproterozoic oceans may have delayed the ecological rise of eukaryotes until ~800 Ma when increased nitrate supply, together with other environmental and ecological factors, may have contributed to the transition from prokaryote-dominant to eukaryote-dominant marine ecosystems. Recognizing the spatial and temporal variations in Neoproterozoic oceanic environments, the second chapter lays the groundwork for a robust stratigraphic framework for the early Tonian Period. Employing the dynamic time warping algorithm, I constructed a global stratigraphic framework for the early Tonian Period using δ¹³C_carb data from the North China, São Francisco, and Congo cratons. This exercise confirms the generally narrow range of δ¹³C_carb fluctuations in the early Tonian, but also confirms the presence of a negative δ¹³C_carb excursion of notable magnitude (~9 ‰) at ca. 920 Ma in multiple records, suggesting that it was global in scope. This negative excursion, known as the Majiatun excursion, is likely the oldest negative excursion in the Neoproterozoic Era and marks the onset of the dynamic Neoproterozoic carbon cycle. Shifting focus to the late Neoproterozoic, the third chapter delves into the origins of Neoproterozoic superheavy pyrite, whose bulk-sample δ³⁴S values are greater than those of contemporaneous seawater sulfate and whose origins remain controversial. Two supervised machine learning algorithms were trained on a large LA-ICP-MS pyrite trace element database to distinguish pyrite of different origins. The analysis validates that two models built on the co-behavior of 12 trace elements (Co, Ni, Cu, Zn, As, Mo, Ag, Sb, Te, Au, Tl, and Pb) can be used to accurately predict pyrite origins. This novel approach was then used to identify the origins of pyrite from two Neoproterozoic sedimentary successions in South China. The first set of samples contains isotopically superheavy pyrite from the Cryogenian Tiesi'ao and Datangpo formations. The second set of samples contains pyritic rims from the Ediacaran Doushantuo Formation; these pyrite rims are associated with fossiliferous chert nodules and do not have superheavy sulfur isotopes. For the superheavy pyrite, the models consistently show high confidence levels in identifying its genesis type, and three out of four samples were inferred to be of sedimentary origins. For the pyritic nodule rims, the models suggest that early diagenetic pyrite was subsequently altered by hydrothermal fluids and therefore shows mixed signals. The third chapter highlights the importance of pyrite trace elements in deciphering and distinguishing the origins of pyrite in sedimentary strata. / Doctor of Philosophy / Understanding how life and the environment have shaped our planet's story over 4.5 billion years is like piecing together an intricate puzzle. On the one hand, changes in the environment kickstarted big shifts in how life evolved. On the other hand, living creatures have also left their mark on Earth's landscapes and climate. This dissertation focuses on unraveling the mysterious Neoproterozoic Era (1 billion to 538 million years ago), a time when Earth saw some of its most dramatic changes. A significant aspect of my investigation delves into the evolutionary dynamics within ancient marine ecosystems. Specifically, I'm exploring a critical juncture when organisms with more complex cellular structures, known as eukaryotes, became ecologically more important than prokaryotic life forms in many aspects of Earth systems. By examining ancient rock formations from China, I have found evidence suggesting that nitrate, a vital nutrient, was scarce in the Neoproterozoic oceans. However, around 800 million years ago, there appears to have been a significant surge in nitrate availability. This surge potentially catalyzed a pivotal phase in evolution, possibly driving the shift from prokaryote to eukaryote dominance in these ancient waters. Second, there is a challenge to delineate a robust timeline for the early Neoproterozoic Era. Imagine trying to piece together a story from a time when there were no calendars or clear dates. Employing advanced statistical methods and comparing chemical signals preserved in carbonate rocks from disparate global locations, I endeavor to craft a coherent timeline for this crucial period. Within this timeline, a noteworthy anomaly in the carbon cycle emerged around 920 million years ago known as the Majiatun excursion. This anomaly represents a significant shift in the Neoproterozoic carbon cycle. Furthermore, my investigation plunges into the geochemistry of sulfur, an important element in shaping ancient marine environments. Certain sedimentary rocks harbor anomalous sulfur isotope signatures in the mineral pyrite (also known as fool's gold), hinting at dramatic environmental transformations during the late Neoproterozoic. Employing advanced analytical techniques and machine learning methodologies, I seek to discern the origins and implications of these anomalous sulfur isotope signals found in pyrite, unraveling their significance in reconstructing the environmental dynamics of ancient oceans.

Clinical trials are experiments or observations on a patient’s responses of different medical treatments to cure diseases. Such trials are heavily regulated and must achieve a certain quality standard of the trial and clinical adherence is a determining factor on the success of a study. However, it has historically been difficult to systematically follow and understand patient adherence to medical ordinations, predominately due to lack of proper tools. One new type of tools is a digital pillbox that can be used to supply pills to participants in clinical trials. This paper examines implementing two supervised machine learning models to detect if an object (a pill) is found in an encapsulated compartment (pillbox) based on electromagnetic radiation data from a proximity sensor. Support Vector Machine (SVM) and Random Forest (RF) were evaluated on a data set of N=1,485 observations, consisting of five classes: four different pills and ‘no pill’. RF performs best with accuracy of 98.0% and weighted average precision of 98.0%. SVM had 97.3% accuracy and 97.6% weighted average precision. Best performance was achieved at N=1,000 for RF and 1,100 for SVM. The conclusion was that a high accuracy and precision can be achieved using either RF or SVM. The classification model strengthens the value proposition of a digital pillbox and can improve clinical trials to achieve better data quality. However, for the model to contribute actual economical value, digital pillboxes must be a common practice in clinical trials. / Kliniska studier är experiment eller observationer av en patients reaktion på olika typer av medicinsk vård för behandling sjukdomar. Sådana studier är tungt reglerade och behöver uppnå en viss kvalitésstandard och klinisk följsamhet är en avgörande faktor för en studies framgång. Trots det har det historiskt varit svårt att systematiskt mäta och förstå en patients följsamhet av en medicinsk ordination, primärt på grund av brist av användbara verktyg. En ny typ av verktyg är en digital  pillerbox som försörjer piller till deltagare i kliniska studier. Denna studie undersöker implementation av två bevakade maskininlärningsmodeller för detektion om ett objekt (ett piller) befinner sig i ett slutet fack baserad på elektromagnetisk strålning från en närhetssensor. Support Vector Machine (SVM) och Random Forest (RF) utvärderades på ett dataset av N=1 485 observationer utgjort av fem klasser: fyra piller och ’inget piller’. RF presterar bäst med 98,0% i träffsäkerhet och 98,0% i viktad medelprecision. SVM fick 97,3% träffsäkerhet och 97,6% viktad medelprecision. Bäst prestation uppnåddes vid N=1 000 för RF och N=1 100 för SVM. Slutsatsen var att en hög träffsäkerhet och precision kan uppnås genom antingen RF eller SVM. Klassificeringsmodellen förstärker en digital pillerbox värdeerbjudande och kan hjälpa kliniska studier att uppnå högre datakvalité. Däremot, för klassificeringsmodellen ska bidra med faktiskt ekonomiskt värde, behöver digitala pillerboxar vara en vedertagen praxis.

The strive for cost reduction of services and repairs combined with a desire for increased vehicle reliability has led to the development of predictive maintenance programs. In maintenance plans, accurate forecasts and predictions regarding which components in a vehicle is in risk of a breakdown is bene_cial to obtain since this enables components to be predictively exchanged or serviced before they break down and cause unnecessary downtime. Previous works in data driven predictive maintenance models typically utilize customer and operational data to predict component wear trough regressive or classi_er models. In this thesis the possibilities and bene_ts associated with utilizing vehicle repair and service history data for trucks in a predictive model is investigated. The repair and service data is a time series of irregularly sampled visits to a service centre and is used in conjunction with operational data and chassis con_guration data collected by a truck manufacturer. To tackle the problem a Random Forest, a Neural Network as well as a Recurrent Neural Network model was tested on the various datasets. The Recurrent Neural Network model made it possible to utilize the entire vehicle repair time series data whereas the Random Forest model used a condensed form of the repair data. The Recurrent model proved to perform signi_cantly better than the Neural Network model trained on operational data however it was not proven signi_cantly better than a Random Forest model trained on the condensed form of repair data. A conclusion that can be drawn is that repair history data can increase the performance of a predictive model, however it is unclear if the time sequence plays a part or if a list of previously exchanged parts works equally well. / Strävan efter att reducera kostnader av reparationer och service samt att öka fordons pålitlighet har lett till utvecklingen av prediktiva underhållsprogram. Träffsäkra förutsägeleser och prediktioner kring vilka delar som riskerar att fallera möjliggör prediktiva utbytelser eller service av delar innan de går sönder. Tidigare arbeten i prediktivt underhåll använder sig vanligen av kunddata och operationell data för att generera en prediktion genom regressions eller klassificeringsmetoder. I det här examensarbetet utforskas möjligheterna och fördelarna med att använda verkstadsdata från lastbilar i en prediktiv modell. Verkstadsdatan består av en oregelbundet genererad tidsserie av besök till en serviceanläggning och används i kombination med operationell data samt chassiutförandedata. För att angripa problemet användes en Random Forest, en Neuronnäts samt en Recurrent (Återkommande) Neuronnätsmodell på de olika datakällorna. Recurrent Neuronnätsmodellen möjliggjorde användandet av kompletta tidserieverkstadsdatan och denna modell visade sig ge bäst resultat men kunde inte påvisas  vara signifikant bättre än en Random Forest modell som tränades på en komprimerad variant av verkstadsdatan.  En slutsats som kan dras av arbetet är att verkstadsdatan kan öka prestandan i en prediktiv model men att det är oklart om det är tidssekvensen av datat som ger ökningen eller om det fungerar lika bra med en lista över tidigare utbytta delar.

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