You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["HealthCareActivity", "InjuryOrPoisoning", "BodySubstance", "IntellectualProduct", "AnatomicalStructure", "SpatialConcept", "Chemical", "Bacterium", "MedicalDevice", "Organization", "BiomedicalOccupationOrDiscipline", "Finding", "BiologicFunction", "Virus", "ResearchActivity", "ClinicalAttribute", "PopulationGroup", "Eukaryote", "BodySystem", "Food", "ProfessionalOrOccupationalGroup"].

Please return the output as a JSON object of the format: {"Virus": ["HIV", ...], "Bacterium": ["MRSA", ...], "AnatomicalStructure": ["Lung", ...], "BodySystem": ["CNS", ...], "BodySubstance": ["Serum", ...], "Finding": ["Headache", ...], "InjuryOrPoisoning": ["Fracture", ...], "BiologicFunction": ["Death", ...], "HealthCareActivity": ["Biopsy", ...], "ResearchActivity": ["Clinical trial", ...], "MedicalDevice": ["Lenses", ...], "SpatialConcept": ["Camps", ...], "BiomedicalOccupationOrDiscipline": ["Forensic medicine", ...], "Organization": ["WHO", ...], "ProfessionalOrOccupationalGroup": ["Provider", ...], "PopulationGroup": ["Swimmers", ...], "Chemical": ["Gold", ...], "Food": ["Rice", ...], "IntellectualProduct": ["RPAM", ...], "ClinicalAttribute": ["Biomarker", ...], "Eukaryote": ["Dogs", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Heterogeneous depression trajectories in multiple sclerosis patients Trajectories of depression over time may be heterogeneous in Multiple Sclerosis (MS) patients. Describing these trajectories will help clinicians understand better the progression of depression in MS patients to aid in patient care decisions. Latent class growth analysis (LCGA) was applied to 3507 MS patients using an electronic health records (EHR) data base to identify subgroups of MS patients based on self-reported depression screening (PHQ-9). Latent trajectory classes were used for group comparisons based on baseline clinical characteristics. Three subgroups were found characterized by high (10.0% [of participants]), wavering above and below moderate (26.2%) and low and variable (63.8%) depression level trajectories. The subpopulation trajectories, respectively, were also characterized by high, moderate and low MS disability at baseline. In contrast, the overall average trajectory was slightly declining and below the moderate depression threshold. The LCGA approach described in this paper and applied to MS patients provides a template for improved use of an EHR data base for understanding heterogeneous depression screening trajectories. Clinicians may use such information to more closely monitor patients that are expected to maintain high or unstable depression levels.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["MS", "Multiple Sclerosis", "depression", "multiple sclerosis"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": [], "ClinicalAttribute": [], "Eukaryote": [], "Finding": ["unstable", "depression levels", "disability", "depression level"], "Food": [], "HealthCareActivity": ["monitor patients", "depression screening"], "InjuryOrPoisoning": [], "IntellectualProduct": ["electronic health records (EHR) data base", "EHR data base"], "MedicalDevice": [], "Organization": [], "PopulationGroup": ["participants", "subpopulation", "group", "subgroups"], "ProfessionalOrOccupationalGroup": ["Clinicians", "clinicians"], "ResearchActivity": [], "SpatialConcept": [], "Virus": []}