Anomaly detection in clinical time-series holds significant potential in identifying suspicious patterns in different biological parameters. In this paper, we propose a targeted method that incorporates the clinical domain knowledge into LLMs to improve their ability to detect anomalies. We introduce the Metabolism Pathway-driven Prompting (MPP) method, which integrates the information about metabolic pathways to better capture the structural and temporal changes in biological samples. We applied our method for doping detection in sports, focusing on steroid metabolism, and evaluated using real-world data from athletes. The results show that our method improves anomaly detection performance by leveraging metabolic context, providing a more nuanced and accurate prediction of suspicious samples in athletes' profiles.

In this paper, we perform an empirical analysis on T-LSTM Auto-encoder - a model that can analyze a large dataset of irregularly sampled time series and project them into an embedded space. In particular, with three different synthetic datasets, we show that both memory unit and hidden unit of the last step in the encoder should be used as representation for a longitudinal profile. In addition, we perform a cross-validation to determine the dimension of the embedded representation - an important hyper-parameter of the model - when apply T-LSTM Auto-encoder into the real-world clinical datasets of patients having Chronic Kidney Disease (CKD). The analysis of the decoder outputs from the model shows that they not only capture well the long-term trends in the original data but also reduce the noise or fluctuation in the input data. Finally, we demonstrate that we can use the embedded representations of CKD patients learnt from T-LSTM Auto-encoder to identify interesting and unusual longitudinal profiles in CKD datasets.

While studying response trajectory, often the population of interest may be diverse enough to exist distinct subgroups within it and the longitudinal change in response may not be uniform in these subgroups. That is, the timeslope and/or influence of covariates in longitudinal profile may vary among these different subgroups. For example, Raudenbush (2001) used depression as an example to argue that it is incorrect to assume that all the people in a given population would be experiencing either increasing or decreasing levels of depression. In such cases, traditional linear mixed effects model (assuming common parametric form for covariates and time) is not directly applicable for the entire population as a group-averaged trajectory can mask important subgroup differences. Our aim is to identify and characterize longitudinally homogeneous subgroups based on the combination of baseline covariates in the most parsimonious way. This goal can be achieved via constructing regression tree for longitudinal data using baseline covariates as partitioning variables. We have proposed LongCART algorithm to construct regression tree for the longitudinal data. In each node, the proposed LongCART algorithm determines the need for further splitting (i.e. whether parameter(s) of longitudinal profile is influenced by any baseline attributes) via parameter instability tests and thus the decision of further splitting is type-I error controlled. We have obtained the asymptotic results for the proposed instability test and examined finite sample behavior of the whole algorithm through simulation studies. Finally, we have applied the LongCART algorithm to study the longitudinal changes in choline level among HIV patients.