首页|Comillas Pontifical University Reports Findings in Chronic Disease (Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study)
Comillas Pontifical University Reports Findings in Chronic Disease (Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Disease Attributes - C hronic Disease is the subject of a report.According to news reporting originati ng in Madrid, Spain, by NewsRx journalists, research stated, “Functionalimpairm ent is one of the most decisive prognostic factors in patients with complex chro nic diseases.A more significant functional impairment indicates that the diseas e is progressing, which requires implementingdiagnostic and therapeutic actions that stop the exacerbation of the disease.”The news reporters obtained a quote from the research from Comillas Pontifical U niversity, “This studyaimed to predict alterations in the clinical condition of patients with complex chronic diseases by predictingthe Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence mo deland data collected through an internet of things mobility device. A 2-phase pilot prospective single-centerobservational study was designed. During both ph ases, patients were recruited, and a wearable activitytracker was allocated to gather physical activity data. Patients were categorized into class A (BI 20;to tal dependence), class B (20 <BI 60; severe dependence), an d class C (BI >60; moderate or mild dependence, or indep endent). Data preprocessing and machine learning techniques were used to analyzemobility data. A decision tree was used to achieve a robust and interpretable m odel. To assess the qualityof the predictions, several metrics including the me an absolute error, median absolute error, and rootmean squared error were consi dered. Statistical analysis was performed using SPSS and Python for themachine learning modeling. Overall, 90 patients with complex chronic diseases were inclu ded: 50 duringphase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 andclass C: n=20). Most patients (n=85, 94% ) had a caregiver. The mean value of the BI was 58.31 (SD24.5). Concerning mobi lity aids, 60% (n=52) of patients required no aids, whereas the ot hers requiredwalkers (n=18, 20%), wheelchairs (n=15, 17% ), canes (n=4, 7%), and crutches (n=1, 1%). Regardingclinical complexity, 85% (n=76) met patient with polypathology cri teria with a mean of 2.7 (SD 1.25)categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chr onicdiseases criteria. The most characteristic symptoms were dyspnea (n=73, 82% ), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) ofpatients. The most important variables for predicting the BI were iden tified as the maximum step countduring evening and morning periods and the abse nce of a mobility device. The model exhibited consistencyin the median predicti on error with a median absolute error close to 5 in the training, validation, an dproduction-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in thetraining, vali dation, and test sets, respectively.”
MadridSpainEuropeChronic DiseaseCyborgsDisease AttributesEmerging TechnologiesHealth and MedicineMachine Learning
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