Browsing by Author "Lassila, Mikko"

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    Case study in the context of Pharma 4.0: binary pass / fail classification of pharmaceutical product batches based on raw material batch properties
    (2020) Lassila, Mikko; Upreti, Bikesh; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
    “Pharma 4.0” (implementation of Industry 4.0 concepts in pharmaceutical manufacturing) could solve barriers faced by pharmaceutical industry. Development of new pharmaceuticals is expensive and routine manufacturing operations typically involving batch processing (in-stead of continuous) are often inefficient. “Pharma 4.0” could make the industry more efficient through automatization of decisions with less human interventions. Nowadays regula-tors, such as FDA, also seem to encourage pharmaceutical industry to adapt emerging technology. In this bachelor’s thesis I conducted a case study where a classification algorithm was trained to predict the success of pharmaceutical product batches based on the raw material batch combinations used in manufacturing of the product batch. It was known beforehand that raw materials have a major role in the quality of the case product. The pass / fail classification was based on a single critical quality attribute of the product but could be extended. The data set was rather small (in machine learning context) consisting of slightly over 350 batches and imbalanced as majority of the batches were passed. However, training and cross-validation of the classifier with approximately 70 % of the batches and testing with rest (30 %) of the batches lead to quite good results in terms of consistency between cross-validation and testing, high precision and sufficiently high recall. The performance was especially evaluated by precision-recall curves but also ROC (Receiver operating characteristics) curves. Especially Support Vector Machine (SVM), Naive Bayes and Random Forest algorithms gave the best results with above-mentioned considerations, however, due to data set limitations final con-clusions of algorithm superiority for this purpose are not made. Instead, this study was a proof-of-concept that encourages to develop such a raw material selection tool in the compa-ny. This should be relatively straightforward as I focused here on the data available on a sin-gle data source (ERP). Therefore, suitable next step following this case study could be the implementation of the raw material selection tool for production planning purposes: when the product is manufactured, the classifier retrieves all raw material batches available for the raw materials to be used and predicts the probability of “pass” for each possible raw material combination based on the raw material batch attributes. Classifier could suggest the production planner the raw material batch combination with the highest probability of success. In long run, if the model proves useful in real use, the tool could reduce the workload of production planners and pro-cess and / or material experts they occasionally may need to consult in selection of the optimal raw material batch combination.
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    Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformer
    (IEEE, 2023) Chen, Zhaoyang; Siltala-Li, Lina; Lassila, Mikko; Malo, Pekka; Vilkkumaa, Eeva; Saaresranta, Tarja; Virkki, Arho Veli; School Common, BIZ; Department of Information and Service Management; Aalto University School of Business; Department of Information and Service Management; Turku University Hospital; University of Turku
    Background: Obstructive sleep apnea (OSA) is growing increasingly prevalent in many countries as obesity rises. Sufficient, effective treatment of OSA entails high social and financial costs for healthcare. Objective: For treatment purposes, predicting OSA patients' visit expenses for the coming year is crucial. Reliable estimates enable healthcare decision-makers to perform careful fiscal management and budget well for effective distribution of resources to hospitals. The challenges created by scarcity of high-quality patient data are exacerbated by the fact that just a third of those data from OSA patients can be used to train analytics models: only OSA patients with more than 365 days of follow-up are relevant for predicting a year's expenditures. Methods and procedures: The authors propose a translational engineering method applying two Transformer models, one for augmenting the input via data from shorter visit histories and the other predicting the costs by considering both the material thus enriched and cases with more than a year's follow-up. This method effectively adapts state-of-the-art Transformer models to create practical cost prediction solutions that can be implemented in OSA management, potentially enhancing patient care and resource allocation. Results: The two-model solution permits putting the limited body of OSA patient data to productive use. Relative to a single-Transformer solution using only a third of the high-quality patient data, the solution with two models improved the prediction performance's R2 from 88.8% to 97.5%. Even using baseline models with the model-augmented data improved the R2 considerably, from 61.6% to 81.9%. Conclusion: The proposed method makes prediction with the most of the available high-quality data by carefully exploiting details, which are not directly relevant for answering the question of the next year's likely expenditure. Clinical and Translational Impact Statement: Public Health- Lack of high-quality source data hinders data-driven analytics-based research in healthcare. The paper presents a method that couples data augmentation and prediction in cases of scant healthcare data.
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    Use of electronic health records and machine learning for sleep apnea diagnosis and treatment cost prediction
    (2021) Lassila, Mikko; Malo, Pekka; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
    Sleep apnea has recently gained wide public attention in Finland, partly due to the sudden death of a Finnish celebrity (Olli Lindholm) in 2019. However, the attention is justified, considering the status of sleep apnea as a new national disease in Finland. The health care is under great pressure due to the increased number of doctor’s referrals. Ability to predict the diagnosis and treatment costs could help the health care to make better decisions. At the same time, electronic health records (EHR) and advanced analytics have enabled new opportunities for health care. This is attractive due to potential for quality improvements (e.g., help diagnosis) and cost savings. In this thesis, we trained and validated machine learning models to predict a patient’s direct diagnosis and treatment costs from existing EHR data. In practice, we used a dataset collected from sleep apnea patients in specialized health care in a Finnish hospital district. The work consisted of extraction of the features and treatment information from the EHR database, restructuring and cleaning the data, combining with the unit cost data and training, validating and evaluation the machine learning models. As a target variable, we used net present value (NPV) of the direct costs. As feature values, we used especially initial values of some key information available from the patients such as age, body mass index and apnea-hypopnea index (AHI). The tested regression methods included a shrinkage method (Lasso) and tree-based ensemble method (Random Forest) as well as three gradient boosting based state-of-the-art methods (CatBoost, XGBoost and LightGBM). With the latter ones, the model accuracy (in cross-validation and testing) was the highest although still below moderate. Despite some lacks in the data (only specialized health care, no operation codes, inconsistency in time), the study showed that prediction of the treatment costs of OSA was possible with such an approach. Especially, after implementing suggested improvements, such models could have practical use for health care decision makers.