PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease.Comment: AAAI Conference on Artificial Intelligence 201

Heat transfer via dropwise condensation on hydrophobic microstructured surfaces

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 22).Dropwise condensation has the potential to greatly increase heat transfer rates. Heat transfer coefficients by dropwise condensation and film condensation on microstructured silicon chips were compared. Heat transfer coefficients are found to be seventy percent higher in the hydrophobic, dropwise condensation case relative to the hydrophilic, film condensation case. With this increased heat transfer coefficient, dropwise condensation using microstructures could improve many heat exchange applications, particularly electronics cooling.by Karlen E. Ruleman.S.B

A controlled study of hostile-helpless states of mind among borderline and dysthymic women

The aim of this study was to determine whether women with borderline personality disorder (BPD) are more likely than those with dysthymia to manifest contradictory Hostile-Helpless (HH) states of mind. A reliable rater blind to diagnosis evaluated features of such mental representations in transcripts of Adult Attachment Interviews from 12 women with BPD and 11 women with dysthymia of similar socioeconomic status (SES), all awaiting psychotherapy. In keeping with three hierarchical (non-independent) a priori predictions regarding the mental representations of women with BPD, the results were that (a) all those with BPD, compared with half the group with dysthymia, displayed HH states of mind; (b) those with BPD manifested a significantly higher frequency of globally devaluing representations; and (c) they exhibited a strong trend toward identifying with the devalued hostile caregiver (58% BPD vs. 18% dysthymic). In addition, significantly more BPD than dysthymic patients made reference to controlling behavior towards attachment figures in childhood. These findings offer fresh insights into the nature of BPD and extend previous evidence concerning affected individuals' patterns of thinking and feeling about childhood attachment figures