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MediCoSpace: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs
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2023 | ACM Transactions on Management Information Systems | Citations: 0
Authors: van der Linden, Sanne; Sevastjanova, Rita; Funk, Mathias; El-Assady, Mennatallah

Abstract: Healthcare systems are under pressure from an aging population, rising costs, an ... Expand

Abstract: Healthcare systems are under pressure from an aging population, rising costs, and increasingly complex conditions and treatments. Although data are determined to play a bigger role in how doctors diagnose and prescribe treatments, they struggle due to a lack of time and an abundance of structured and unstructured information. To address this challenge, we introduce MediCoSpace, a visual decision-support tool for more efficient doctor-patient consultations. The tool links patient reports to past and present diagnoses, diseases, drugs, and treatments, both for the current patient and other patients in comparable situations. MediCoSpace uses textual medical data, deep-learning supported text analysis and concept spaces to facilitate a visual discovery process. The tool is evaluated by five medical doctors. The results show that MediCoSpace facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients. Collapse

Semantic filters: spaCy data transformation

Topics: decision support knowledge base visual analytics knowledge representation healthcare data

Methods: qualitative interview qualitative content analysis exploratory data analysis ontological modelling usability testing
Social Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs
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2022 | HICSS | Citations: 0
Authors: Koss, Jonathan; Bohnet-Joschko, Sabine

Abstract: Pharmaceutical companies increasingly must consider patients’ needs in drug deve ... Expand

Abstract: Pharmaceutical companies increasingly must consider patients’ needs in drug development. Since patients’ needs are often difficult to measure, especially in rare diseases, information in drug development decision-making is limited. In the proposed study, we employ the opportunity algorithm to identify and prioritize unmet medical needs of multiple sclerosis patients shared in social media posts. Using topic modeling and sentiment analysis features of the opportunity algorithm are generated. The result implies that sensory problems, pain, mental health problems, fatigue and sleep disturbances represent the highest unmet medical needs of the samples population. The present study suggests a promising potential of this method to provide relevant insights into rare disease populations to promote patient-centered drug development. Collapse

Semantic filters: spaCy data transformation

Topics: social media cloud computing innovation management

Methods: computational algorithm sentiment analysis topic model survey NLTK

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MediCoSpace: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs

Social Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs