Diagnosis code assignment is a field that looks at automatically assigning diagnosis codes to free-text clinical notes. Assigning a diagnosis code to clinical notes manually needs expertise and time. Being able to do this automatically makes getting structured data from free-text clinical notes in Electronic Health Records easier. Furthermore, it can also be used as decision support for clinicians where they can input their notes and get back diagnosis codes as a second opinion. This project investigates the effects of using the hierarchies the diagnosis codes are structured in when training the diagnosis code assignment models compared to models trained with a standard loss function, binary cross-entropy. This has been done by using the hierarchy of two systems of diagnosis codes, ICD-9 and SNOMED CT, where one hierarchy is more detailed than the other. The results showed that hierarchical training increased the recall of the models regardless of what hierarchy was used. The more detailed hierarchy, SNOMED CT, increased the recall more than what the use of the less detailed ICD-9 hierarchy did. However, when using the more detailed SNOMED CT hierarchy the precision of the models decreased while the differences in precision when using the ICD-9 hierarchy was not statistically significant. The increase in recall did not make up for the decrease in precision when training with the SNOMED CT hierarchy when looking at the F1-score that is the harmonic mean of the two metrics. The conclusions from these results are that using a more detailed hierarchy increased the recall of the model more than when using a less detailed hierarchy. However, the overall performance measured in F1-score decreased when using a more detailed hierarchy since the other metric, precision, decreased by more than what recall increased. The use of a less detailed hierarchy maintained its precision giving an increase in overall performance. / Diagnoskodstilldeling är ett fält som undersöker hur man automatiskt kan tilldela diagnoskoder till fri-text läkaranteckningar. En manuell tildeling kräver expertis och mycket tid. Förmågan att göra detta automatiskt förenklar utvinning av strukturerad data från fri-text läkaranteckningar i elektroniska patientjournaler. Det kan även användas som ett hjälpverktyg för läkare där de kan skriva in sina läkaranteckningar och få tillbaka diagnoskoder som en andra åsikt. Detta arbete undersöker effekterna av att ta användning av hierarkierna diagnoskoderna är strukturerade i när man tränar modeller för diagnoskodstilldelning jämfört med att träna modellerna med en vanlig loss-funktion. Det här kommer att göras genom att använda hierarkierna av två diagnoskod-system, SNOMED CT och ICD-9, där en av hierarkierna är mer detaljerad. Resultaten visade att hierarkisk träning ökade recall för modellerna med båda hierarkierna. Den mer detaljerade hierarkien, SNOMED CT, gav en högre ökning än vad träningen med ICD-9 gjorde. Trots detta minskade precision av modellen när man den tränades med SNOMED CT hierarkin medan skillnaderna i precision när man tränade hierarkiskt med ICD-9 jämfört med vanligt inte var statistiskt signifikanta. Ökningen i recall kompenserade inte för minskningen i precision när modellen tränades med SNOMED CT hierarkien som man kan see på F1-score vilket är det harmoniska medelvärdet av de recall och precision. Slutsatserna man kan dra från de här resultaten är att en mer detaljerad hierarki kommer att öka recall mer än en mindre detaljerad hierarki ökar recall. Trots detta kommer den totala prestandan, som mäts av F1-score, försämras med en mer detaljerad hierarki eftersom att recall minskar mer än vad precision ökar. En mindre detaljerad hierarki i träning kommer bibehålla precision så att dens totala prestandan förbättras.

No description available.

Explainability is a key component in building trust for computer calculated predictions when they are applied to areas with influence over individual people. This bachelor thesis project report focuses on the explanation regarding the decision making process of the machine learning method Logistic Regression when predicting mortality. The aim is to present theoretical information about the predictive model as well as an explainable interpretation when applied on the clinical MIMIC-III database. The project found that there was a significant difference between particular features considering the impact of each individual feature on the classification. The feature that showed the greatest impact was the Glasgow Coma Scale value, which could be proven through the fact that a good classifier could be constructed with only that and one other feature. An important conclusion from this study is that a great focus should be enforced early in the implementation process when the features are selected. In this specific case, when medical artificial intelligence is implemented, medical expertise is desired in order to make a good feature selection. / Förklarbarhet är en viktig komponent för att skapa förtroende för datorframtagna prognoser när de appliceras på områden som påverkar individuella personer. Denna kandidatexamensarbetesrapport fokuserar på förklarandet av beslutsprocessen hos maskininlärningsmetoden Logistic Regression när dödlighet ska förutsägas. Målet är att presentera information om den förutsägande modellen samt en förklarbar tolkning av resultaten när modellen appliceras på den kliniska databasen MIMIC-III. Projektet fann att det fanns signifikanta skillnader mellan särskilda egenskaper med hänsyn till den påverkan varje enskild egenskap har på klassificeringen. Den egenskapen som visade ha störst inverkan var Glascow Coma Scale värdet, vilket kunde visas via det faktum att en god klassificerare kunde konstrueras med endast den och en annan egenskap. En viktig slutsats av denna studie är att stort fokus bör läggas tidigt i implementationsprocessen då egenskaperna väljs. I detta specifika fall, då medicinsk artificiell intelligens implementeras, krävs medicinsk expertis för att göra ett gott egenskapsurval. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Chronic Kidney Disease (CKD) is a common and dangerous health condition that requires early detection and treatment to be effective. Current diagnostic methods are time-consuming and expensive. In this research, we hope to construct a predictive model for CKD utilizing a combination of time series and static variables for early detection of CKD. In this study, we investigate the influence of multimodal approach by combining the predictions from multiple models that utilize different modalities. The ROCKET method is utilized for classification using time series features, whilst the Random Forest approach is employed for static data. XGBoost has been utilized to gain information about feature importance among labs and demographics-comorbidities data. In this study, we use the MIMIC-III database, adopting various strategies to handle data and class imbalance, such as stratification, balancing techniques, and backwards and forward fill for missing value imputation. The evaluation metrics for CKD and non-CKD class labels include precision, recall, F1, and accuracy. Our findings show that aggregating time series data produce contrasting results for labs compared to vitals data. We also addressed the significance of the different demographic, comorbidities and lab events features. The findings indicate that a multimodal approach did not show significant advantages over individual models when the individual models performed suboptimal. The study also found that Ethnicity is more significant than age and gender in predicting CKD. Furthermore, the study revealed some significant features from lab events and comorbidities. The study also provides some recommendations for future work to explore the potential of a multimodal approach further.

The discharge summary is an important document, summarizing a patient’s medical information during their hospital stay. It is crucial for communication between clinicians and primary care physicians. Creating a discharge sum- mary is a necessary task. However, it is time-consuming for physicians. Using technology to automatically generate discharge summaries can be helpful for physicians and assist them in concentrating more on the patients than writing clinical summarization notes and discharge summaries. This master’s thesis aims to contribute to the research of building a transformer-based model for an automated discharge summary with a pre-trained DistilBART language model. This study plans to answer this main research question: How e↵ective is the pre-trained DistilBART language model in predicting an automated discharge summary for multiple clinical texts? The research strategy used in this study is experimental. the dataset is MIMIC- III. To evaluate the e↵ectiveness of the model, ROUGE scores are selected. The result of this model is compared with the result of the baseline BART model, which is implemented on the same dataset in the other recent research. This study regards multiple document summarization as the process of combining multiple inputs into a single input, which is then summarized. The findings indicate an improvement in ROUGE-2 and ROUGE-Lsum in the DistilBART model in comparison with the baseline BART model. However, one important limitation was computational resource constraint. The study also provides eth- ical considerations and some recommendations for future works.