TY  - JOUR
A1  - Davidson, Padraig
A1  - Düking, Peter
A1  - Zinner, Christoph
A1  - Sperlich, Billy
A1  - Hotho, Andreas
T1  - Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study
JF  - Sensors
N2  - The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.
KW  - artificial intelligence
KW  - endurance
KW  - exercise intensity
KW  - precision training
KW  - prediction
KW  - wearable
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205686
SN  - 1424-8220
VL  - 20
IS  - 9
ER  - 
TY  - JOUR
A1  - Loda, Sophia
A1  - Krebs, Jonathan
A1  - Danhof, Sophia
A1  - Schreder, Martin
A1  - Solimando, Antonio G.
A1  - Strifler, Susanne
A1  - Rasche, Leo
A1  - Kortüm, Martin
A1  - Kerscher, Alexander
A1  - Knop, Stefan
A1  - Puppe, Frank
A1  - Einsele, Hermann
A1  - Bittrich, Max
T1  - Exploration of artificial intelligence use with ARIES in multiple myeloma research
JF  - Journal of Clinical Medicine
N2  - Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.
KW  - natural language processing
KW  - ontology
KW  - artificial intelligence
KW  - multiple myeloma
KW  - real world evidence
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197231
SN  - 2077-0383
VL  - 8
IS  - 7
ER  -