| 1. |
- Eichhorst, B., et al.
(författare)
-
First-Line Venetoclax Combinations in Chronic Lymphocytic Leukemia.
- 2023
-
Ingår i: New England Journal of Medicine. - : MASSACHUSETTS MEDICAL SOC. - 0028-4793 .- 1533-4406. ; 388:19, s. 1739-1754
-
Tidskriftsartikel (refereegranskat)abstract
- Background Randomized trials of venetoclax plus anti-CD20 antibodies as first-line treatment in fit patients (i.e., those with a low burden of coexisting conditions) with advanced chronic lymphocytic leukemia (CLL) have been lacking. Methods In a phase 3, open-label trial, we randomly assigned, in a 1:1:1:1 ratio, fit patients with CLL who did not have TP53 aberrations to receive six cycles of chemoimmunotherapy (fludarabine-cyclophosphamide-rituximab or bendamustine-rituximab) or 12 cycles of venetoclax-rituximab, venetoclax-obinutuzumab, or venetoclax-obinutuzumab-ibrutinib. Ibrutinib was discontinued after two consecutive measurements of undetectable minimal residual disease or could be extended. The primary end points were undetectable minimal residual disease (sensitivity,
|
|
| 2. |
- Grua, Eoin Martino, et al.
(författare)
-
An evaluation of the effectiveness of personalization and self-adaptation for e-Health apps
- 2022
-
Ingår i: Information and Software Technology. - : Elsevier BV. - 0950-5849. ; 146
-
Tidskriftsartikel (refereegranskat)abstract
- Context: There are many e-Health mobile apps on the apps store, from apps to improve a user's lifestyle to mental coaching. Whilst these apps might consider user context when they give their interventions, prompts, and encouragements, they still tend to be rigid e.g., not using user context and experience to tailor themselves to the user. Objective: To better engage and tailor to the user, we have previously proposed a Reference Architecture for enabling self-adaptation and AI personalization in e-Health mobile apps. In this work we evaluate the end users’ perception, usability, performance impact, and energy consumption contributed by this Reference Architecture. Method: We do so by implementing a Reference Architecture compliant app and conducting two experiments: a user study and a measurement-based experiment. Results: Although limited in the number of participants, the results of our user study show that usability of the Reference Architecture compliant app is similar to the control app. Users’ perception was found to be positively influenced by the compliant app when compared to the control group. Results of our measurement-based experiment showed some differences in performance and energy consumption measurements between the two apps. The differences are, however, deemed minimal. Conclusions: Our experiments show promising results for an app implemented following our proposed Reference Architecture. This is preliminary evidence that the use of personalization and self-adaptation techniques can be beneficial within the domain of e-Health apps.
|
|
| 3. |
- van Breda, Ward, et al.
(författare)
-
A feature representation learning method for temporal datasets
- 2016
-
Ingår i: PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI). - : IEEE. - 9781509042401
-
Konferensbidrag (refereegranskat)abstract
- Predictive modeling of future health states can greatly contribute to more effective health care. Healthcare professionals can for example act in a more proactive way or predictions can drive more automated ways of therapy. However, the task is very challenging. Future developments likely depend on observations in the (recent) past, but how can we capture this history in features to generate accurate predictive models? And what length of history should we consider? We propose a framework that is able to generate patient tailored features from observations of the recent history that maximize predictive performance. For a case study in the domain of depression we find that using this method new data representations can be generated that increase the predictive performance significantly.
|
|
