Blar i NTNU Open på forfatter "Verma, Deepika"
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Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review
Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Journal article; Peer reviewed, 2021)The field of patient-centred healthcare has, during recent years, adopted machine learning and data science techniques to support clinical decision making and improve patient outcomes. We conduct a literature review with ... -
Clustering of Physical Behaviour Profiles using Knowledge-intensive Similarity Measures
Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Chapter, 2020)In this paper, we reuse the Case-Based Reasoning model presented in our last work (Verma et al., 2018) to create a new knowledge intensive similarity-based clustering method that clusters a case base such that the intra-cluster ... -
Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes
Verma, Deepika; Jansen, Duncan; Bach, Kerstin; Poel, Mannes; Mork, Paul Jarle; Oude Nijeweme d’Hollosy, Wendy (Peer reviewed; Journal article, 2022)Background Patient-reported outcome measurements (PROMs) are commonly used in clinical practice to support clinical decision making. However, few studies have investigated machine learning methods for predicting PROMs ... -
Modelling Similarity for Comparing Physical Activity Profiles - A Data-Driven Approach
Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Journal article; Peer reviewed, 2018)Objective measurements of physical behaviour are an interesting research field from the public health and computer science perspective. While for public health research, measurements with a high quality and feasible setup ... -
Similarity measure development for case-based reasoning?a data-driven approach
Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Peer reviewed; Journal article, 2019)In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using ... -
Similarity Measure Development for Case-Based Reasoning–A Data-Driven Approach
Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Chapter, 2019)In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using ... -
Using Automated Feature Selection for Building Case-Based Reasoning Systems: An Example from Patient-Reported Outcome Measurements
Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Chapter, 2021)Feature selection for case representation is an essential phase of Case-Based Reasoning (CBR) system development. To (semi-)automate the feature selection process can ease the knowledge engineering process. This paper ... -
Using Case- based Reasoning for Creating Intelligent Systems in Healthcare
Verma, Deepika (Doctoral theses at NTNU;2022:390, Doctoral thesis, 2022)Healthcare research is an emerging field of application of machine learning techniques to investigate complex health datasets. Patients are at the center of any healthcare system. There is a growing realisation of the ...