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- Jonell, Patrik, et al.
(författare)
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Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia : Clinical Feasibility and Preliminary Results
- 2021
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Ingår i: Frontiers in Computer Science. - : Frontiers Media SA. - 2624-9898. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- Non-invasive automatic screening for Alzheimer's disease has the potential to improve diagnostic accuracy while lowering healthcare costs. Previous research has shown that patterns in speech, language, gaze, and drawing can help detect early signs of cognitive decline. In this paper, we describe a highly multimodal system for unobtrusively capturing data during real clinical interviews conducted as part of cognitive assessments for Alzheimer's disease. The system uses nine different sensor devices (smartphones, a tablet, an eye tracker, a microphone array, and a wristband) to record interaction data during a specialist's first clinical interview with a patient, and is currently in use at Karolinska University Hospital in Stockholm, Sweden. Furthermore, complementary information in the form of brain imaging, psychological tests, speech therapist assessment, and clinical meta-data is also available for each patient. We detail our data-collection and analysis procedure and present preliminary findings that relate measures extracted from the multimodal recordings to clinical assessments and established biomarkers, based on data from 25 patients gathered thus far. Our findings demonstrate feasibility for our proposed methodology and indicate that the collected data can be used to improve clinical assessments of early dementia.
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- Mikheeva, Olga, et al.
(författare)
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Aligned Multi-Task Gaussian Process
- 2022
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Ingår i: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022. - : ML Research Press. ; , s. 2970-2988
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Konferensbidrag (refereegranskat)abstract
- Multi-task learning requires accurate identification of the correlations between tasks. In real-world time-series, tasks are rarely perfectly temporally aligned; traditional multitask models do not account for this and subsequent errors in correlation estimation will result in poor predictive performance and uncertainty quantification. We introduce a method that automatically accounts for temporal misalignment in a unified generative model that improves predictive performance. Our method uses Gaussian processes (GPs) to model the correlations both within and between the tasks. Building on the previous work by Kazlauskaite et al. (2019), we include a separate monotonic warp of the input data to model temporal misalignment. In contrast to previous work, we formulate a lower bound that accounts for uncertainty in both the estimates of the warping process and the underlying functions. Also, our new take on a monotonic stochastic process, with efficient path-wise sampling for the warp functions, allows us to perform full Bayesian inference in the model rather than MAP estimates. Missing data experiments, on synthetic and real time-series, demonstrate the advantages of accounting for misalignments (vs standard unaligned method) as well as modelling the uncertainty in the warping process (vs baseline MAP alignment approach).
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- Mikheeva, Olga, et al.
(författare)
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Perceptual facial expression representation
- 2018
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Ingår i: Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538623350 ; , s. 179-186
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Konferensbidrag (refereegranskat)abstract
- Dissimilarity measures are often used as a proxy or a handle to reason about data. This can be problematic, as the data representation is often a consequence of the capturing process or how the data is visualized, rather than a reflection of the semantics that we want to extract. Facial expressions are a subtle and essential part of human communication but they are challenging to extract from current representations. In this paper we present a method that is capable of learning semantic representations of faces in a data driven manner. Our approach uses sparse human supervision which our method grounds in the data. We provide experimental justification of our approach showing that our representation improves the performance for emotion classification.
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- Monk, Bradley J., et al.
(författare)
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A Randomized, Phase III Trial to Evaluate Rucaparib Monotherapy as Maintenance Treatment in Patients With Newly Diagnosed Ovarian Cancer (ATHENA-MONO/GOG-3020/ENGOT-ov45)
- 2022
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Ingår i: Journal of Clinical Oncology. - : Lippincott, Williams & Wilkins. - 0732-183X .- 1527-7755. ; 40:34, s. 3952-3964
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSEATHENA (ClinicalTrials.gov identifier: ) was designed to evaluate rucaparib first-line maintenance treatment in a broad patient population, including those without BRCA1 or BRCA2 (BRCA) mutations or other evidence of homologous recombination deficiency (HRD), or high-risk clinical characteristics such as residual disease. We report the results from the ATHENA-MONO comparison of rucaparib versus placebo.METHODSPatients with stage III-IV high-grade ovarian cancer undergoing surgical cytoreduction (R0/complete resection permitted) and responding to first-line platinum-doublet chemotherapy were randomly assigned 4:1 to oral rucaparib 600 mg twice a day or placebo. Stratification factors were HRD test status, residual disease after chemotherapy, and timing of surgery. The primary end point of investigator-assessed progression-free survival was assessed in a step-down procedure, first in the HRD population (BRCA-mutant or BRCA wild-type/loss of heterozygosity high tumor), and then in the intent-to-treat population.RESULTSAs of March 23, 2022 (data cutoff), 427 and 111 patients were randomly assigned to rucaparib or placebo, respectively (HRD population: 185 v 49). Median progression-free survival (95% CI) was 28.7 months (23.0 to not reached) with rucaparib versus 11.3 months (9.1 to 22.1) with placebo in the HRD population (log-rank P = .0004; hazard ratio [HR], 0.47; 95% CI, 0.31 to 0.72); 20.2 months (15.2 to 24.7) versus 9.2 months (8.3 to 12.2) in the intent-to-treat population (log-rank P < .0001; HR, 0.52; 95% CI, 0.40 to 0.68); and 12.1 months (11.1 to 17.7) versus 9.1 months (4.0 to 12.2) in the HRD-negative population (HR, 0.65; 95% CI, 0.45 to 0.95). The most common grade & GE; 3 treatment-emergent adverse events were anemia (rucaparib, 28.7% v placebo, 0%) and neutropenia (14.6% v 0.9%).CONCLUSIONRucaparib monotherapy is effective as first-line maintenance, conferring significant benefit versus placebo in patients with advanced ovarian cancer with and without HRD.
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